l This Is Smart Steelworks

As addressed in the POSCO the Lighthouse Factory #1, a smart steelworks collects and standardizes all factory data via PosFrame; then, the PosFrame self-learns the data to provide optimal process conditions and controls the various operations at steelworks.

The smart process minimizes the occasions for heuristic decisions preventing human errors. Objective and accurate data can lead to a eureka moment where innovative solutions are discovered. Wait, does this mean human labors are now dispensable?

Such hasty assumption is a surefire way to creating unnecessary anxiety. What the smart technology does is to let the workers delegate simple repetitive tasks to the machine, instead channeling their sophisticated skills and mental energy into perfecting the steelworks operation. The workers feed the result back into the PosFrame fostering the ultimate synergy between technology and humans.

How did smart technology transform the steelmaking operations at steelworks? Let’s find out.

[Order process] Small Lot Orders Go Smart

POSCO’s Order Management Group handles the planning for the entire steel production. One of their crucial tasks includes processing small lot orders — the order quantity for these small lot orders is too small that they typically do not meet a steelworks’ minimum production volume requirements.

Before smarticization, workers manually analyzed and processed the small lot orders — from checking the small lot order standards to figuring out whether and how to bulk-process chunks of small lot orders. The workers manually set the standards and modified them every half-year, the worst-case scenario being small orders sometimes ended up buried and left out. Some urgent orders were constantly classified as ‘small lot’ orders never receiving greenlight for production. In such cases, the order setting had to be reset so it can be incorporated with other orders. This used to take almost six hours or more. The average time for processing the entire small lot order is about 12 hours.

With ‘Smart Task,’ AI automatically organizes small lot orders — based on the analysis of all previous data, the system derives 12 factors impacting small lot orders. The AI also self-learns, so it can self-evaluate the orders. The processing accuracy is currently at 97%. To minimize production costs, the system also predicts the planning volumes so that small lot orders can be integrated with other orders — the production accuracy for which is at 99.99%. Compared to the previous 12 hours, the new procedure takes only an hour.

[Ironmaking] POSCO’s Smart Ironmaking Becomes the National Core Technology

The Smart Task comprehensively controls multiple factors such as furnace ventilation, combustibility, molten iron temperature, etc.; whereas, in the past, all of these were left to manual labor.
For the smooth operation of the Smart Task, the ironmaking department at Pohang Steelworks first defined variables that impact the furnace condition and subsequently integrated various information. In technical terms, this process is called ‘standardizing data.’

Under the Smart Task system, various indicators that were previously subject to the workers’ heuristic knowledge have been organized automating the manual processes. Now, the temperature of molten iron becomes data through IoT, and high-definition cameras conduct regular check-ups on raw materials and fuels.

POSCO started accelerating the establishment of an auto-control system in 2017. Through deep learning, AI self-learns, predicts and manages the data. Beyond simple automation of the current tasks, the system predicts and controls the potential variables and derives optimal results.

Even if the current furnace condition is satisfactory, the smart system goes above and beyond to make sure the furnace maintains its optimal condition. The Pohang No.2 blast furnace became a testing ground for the automated smart technology for the first time in Korea upping the daily molten iron production by 240 tons. The smart technology applied to the Pohang No.2 blast furnace received the government designation as the National Core Technology in July this year.

[Steelmaking] POSCO Goes PTX: Posco sTeelmaking eXpress!

The extremely high temperature required for steelmaking operation makes it difficult to measure temperature in real-time, which was why the daily operation was entirely dependent on field workers’ heuristic knowledge. Variations and human errors were inevitable in this system. Abnormalities at stages required not only a full-stop of the procedure directly involved — but also the procedures before and after.

The steelmaking Plant 2 at Pohang Steelworks has developed an integrated control system — it controls the timing, temperature, and the ingredients from the converter to the continuous casting process. There are potentially 125,000 probable procedures involved in the production for any given steel product.

POSCO digitized and standardized these procedures completing the PTX system by July 2018. With PTX, continuous processing became a reality — from converter through continuous casting without temporary halts or delays. Live-assessment of estimated arrival time, temperature, and product components also became available.

Before PTX, the temperature accuracy was at 80%, but now the system predicts the temperature with 90% accuracy. The use of raw materials also became more efficient.

[Continuous Casting] Prediction Made Simpler and Faster, Saves 600 million

Quality control is absolutely crucial, but it’s impossible to go through each and every material with a fine-tooth comb. So the typical inspection involved a through, 100% surface inspection of sample materials. If and when any abnormalities were detected in the particular sample, all other materials produced in the same batch had to be retrieved, subsequently undergoing thorough inspections.

The Pohang Steelworks No.2 and No.4 continuous casting plants annually inspected 340,000 tons of sample materials — only 30,000 tons found to be defective.

With a relatively small amount of defective materials, POSCO strove to minimize the time wasted, and to maximize the company’s resources. By applying artificial intelligence, POSCO collected a vast amount of data on steelmaking and continuous casting processes thereby establishing a model to predict defects. This was done by creating criteria of what constitutes as abnormalities in quality. Since the adoption of the new system in March 2018, the system now only screens defective materials, eliminating unnecessary testings. Furthermore, it pinpoints the reasons for the defect — the new system is expected to save over 600 million KRW every year.

[Rolling] AI Analyzes and Auto-controls Flatness Factors

Thick plate products first undergo the slab-heating process subsequently going through two rolling procedures. After that, according to the customer’s requirements, it undergoes an accelerated cooling process called Thermo-Mechanical Control Process (TMCP). TMCP involves spraying high-speed water on a steel plate rapidly cooling it to a designated temperature to improve the strength and the welding capacity of steel products. The process requires special equipment — and the water-spraying technology is highly advanced. Without accurate maneuver, the flatness of a steel product can be compromised.

In the past, the flatness of thick plates was manually analyzed, twice — once when the plate was hot (hot flatness) after TMCP treatment; and again, when the plate cooled down (cold flatness) during the shearing process. Measuring flatness in such a manner was extremely time-consuming, and it often caused a functional overload — it was challenging for the factory engineers to control the TMCP by manually inputting the results one at a time.

The plate plant at Gwangyang Steelworks created a learning model by analyzing the influential factors for hot and cold flatness — ultimately the company figured out the conditions that can achieve the most optimal flatness. Now, the engineers can simply monitor the optimal working condition values produced by AI supervising the smooth operations of each process. The correction needs for the products during the post-TMCP process are now reduced to 50% which saves about 1.3 billion KRW annually.

[Surface Treatment] Another National Core Technology, POSCO Surface Treatment Goes AI.

Continuous Galvanizing Line (CGL) involves processing cold-rolled coils with continuous heat treatment, putting them in zinc pot, ultimately producing galvanized iron plates. POSCO’s Giga Steel is also produced through CGL process.

After plates are taken out the zinc pot, an air knife controls the coating amount by cutting out zinc before it solidifies on the surface. The target coating weight varies for each product, and the coating weight could be measured only after the air knife procedure and solidification completed. This is why the live-control of coating weight remained challenging.

To address the challenge, POSCO developed AI-based control model for coating weight — it uses deep learning for precise management of such factors as steel grade, plate thickness, width, operating conditions, and target coating weight. Previously, the accuracy rate for predicting coating weight was projected at 89% — now, with 99% accuracy, the technology has been applied to all surface treatment plants at both Pohang and Gwangyang Steelworks. Just as POSCO’s smart ironmaking technology, POSCO’s smart surface treatment technology also received government designation as National Core Technology.

l Smart Steelworks Enhances Safety

A truly smart steelworks operation prioritizes workers’ safety as well as the environment. POSCO is no exception. What makes POSCO exceptional in this domain, especially when it comes to its application of smart technology, is the way the company utilizes smart CCTVs. POSCO developed a smart CCTV system — unlike general CCTVs which simply record video footage, POSCO’s smart CCTV auto-detects patterns and movements at production facilities. When the system recognizes any abnormalities, it promptly alerts the administrator in charge.

POSCO developed an optimized smart CCTV system that meets various demands of steelworks operations by integrating previously accumulated footages. From last year, POSCO began installing the smart CCTVs at Gwangyang No. 3 surface treatment plant, No.2 steelmaking plant, No.1 coke plant, and Pohang No. 2 plate plant.

Smart CCTVs automatically recognizes and analyzes various information such as the product quality and safety information, reducing the amount of time workers merely ‘stand by’ just to check all the information manually. At a steelmaking plant, for example, smart cameras can measure the temperature of molten iron. At a surface treatment plant, the smart camera detects the subtle color difference of coil-ends allowing prompt response for potential quality defects.

Furthermore, multiple imaging devices, such as thermal imaging cameras, can proactively detect and prevent fire hazards. At all corners of POSCO steelworks, smart CCTVs gather information and identify potential hazards before anybody does.

POSCO’s smart steelworks is an ongoing project. With the establishment of smart technology, POSCO will continue to address the challenges remain unsolved — POSCO aspires to complete the smartization process to ensure production efficiency, top quality products and improved safety.

The two core concepts of the Forth Industrial Revolution (4IR), a term first introduced at the World Economic Forum 2016, are hyper-connectivity and super-intelligence. The two keywords that best describe hyper-connectivity are the Internet of Things (IoT) and Big Data. For super-intelligence, it would be artificial intelligence – in consideration of which the global companies’ race for AI dominance is understandable, as they brace for the Fourth Industrial Revolution where the concepts of hyper-connectivity and super-intelligence are expected to play pivotal roles.

POSCO is not exempt from this global competition. What they are specifically focusing on is fostering AI experts from within, so they’re organizing related programs to educate prospective AI specialists. This September, they even organized ‘POSCO Industrial-AI Solution Challenge,’ a contest dedicated to various topics on artificial intelligence. The Challenge was open to the general public.

It seems there is no apparent linkage between a steel company like POSCO and AI technologies, but the three topics POSCO assigned for the AI Solution Challenge suggests otherwise. From predicting the cost fluctuations of steel raw materials, figuring out the timing of big swells in the ocean, to calculating how many lunches to prepare for POSCO employees, the scope of AI technology can be far-reaching.

POSCO Newsroom sat with the winners of each challenge category – Team Serio, Zero Coke, and Fast Learner – to hear what they have to say about the contest. All three winners acknowledged the immense potential of AI technology and its applicability to virtually all sectors: AI has no borders.

┃“Braving it out with the AI technology” – Team Serio

Jae-Ho Lee and Dong-Woo Kim, the two members of Team Serio, are from POSTECH (Pohang University of Science and Technology), working in the same research department. The moment when they saw the contest information on the school bulletin board, they knew. That this would be a great opportunity to implement their classroom knowledge to help solve the pressing issues in the field. So they applied with no hesitation.

When asked why Serio had specifically chosen to predict the cost fluctuation of steel raw materials, Lee replied: “This challenge topic has the most economic impact, and we also thought it was the most versatile model as it can apply to other time series forecast of similar nature.”

To come up with a solution, Team Serio utilized the model DA_RNN (Dual-stage Attention based RNN), based on the human cognitive process. When recognizing a particular object, a person first notices an area of interest around her or him. Then, he or she identifies an object within the area of interest, finally recognizing the specific features of the object. That is the typical process of human cognition.

For Lee personally, the most significant achievement from competing in the POSCO AI Challenge was becoming more assured about the prospects of AI research. “Because of the satisfactory result of our model, I think we can brave it out going forward, applying AI technologies more proactively and widely,” he added.

The POSCO AI Challenge has ended, with Team Serio as the final category winner. Even after the contest, however, both Lee and Kim are still testing various approaches that they couldn’t utilize because of the time constraint. For Team Serio, the clock is still ticking.

┃“A Solution As Refreshing As… Coke Zero” – Team Coke Zero

Yong-Woo Cho and Kyu-Soo Han of Team Coke Zero share many interests: they go to the same university. They both major in computer science. They also share the love of… Coke Zero. So it didn’t take long for them to choose their team name. Devising a solution for their challenge topic wasn’t as simple. How did they come up with a solution just as refreshing as their team name, Coke Zero?

For companies like POSCO, ocean cargo shipping is crucial, which is incredibly dependent on wave action. Hence the challenge topic of its AI contest, predicting the big swells. The members of Team Coke Zero who have been studying AI and deep learning since earlier this year found this topic especially intriguing.

To predict the result, they worked with meteorological data from a total of 33 observatories along the coast of the Eastern Sea, from Donghae, Ulsan, Ulleungdo and Dokdo island. The main predictors were water temperature, significant wave height, wave period, wave direction and wind direction. All data were observed at one-hour intervals.

For computer science students like Cho and Han, figuring out the exact causes of big swells was especially challenging. So they built an RSCNN model (Random Shuffle Convolutional Neural Network) to establish the relationships between a total of 248 spatial and temporal predictors. Each model they designed learned a different predictor bundle and the team generated dozens of such models. Through the application of ensemble technique, the team completed a solution predicting either the presence or the absence of big swells in the ocean.

Through the contest, Cho ‘felt more convinced’ about his chosen university major. Coming up with an AI model itself was challenging, but it was also rewarding. He has never had this much fun coming up with solutions for industry challenges. After the contest, Cho is feeling even more dedicated to AI research.

┃“I Taught Myself Artificial Intelligence” – Team Fast Learner

Unlike the other winners, Young-Bin Noh of Team Fast Learner doesn’t come from a science background – he is a Business Management student. He was also a solo contestant with no other teammates. What’s even more surprising was the fact that he studied artificial intelligence on his own. On reasons why he participated in the POSCO AI Challenge, he said that he wanted to receive professional feedback from field experts.

The challenge category Noh had chosen was to calculate how many lunches to prepare for POSCO employees. He chose this topic mainly for two reasons. Firstly, he wanted to practice dealing with text data. Text data mining, aka Natural Language Processing, is widely used and popular, so he always wanted to try his hand at it. Secondly, the challenge category didn’t require much background knowledge. So it was also a practical choice for a business student like Noh.

Noh utilized XGBoost algorithm, which creates hundreds of ‘decision-making trees’ to minimize prediction errors. Decision-making tree is one of the most basic algorithms in machine learning. Like the game of twenty questions, it narrows down the possible answers, ending up with one exact answer at the final stage. To further boost the predictability, Noh employed an ‘ensemble technique’ by creating one more XGBoost.

Noh admits he had to study harder after submitting his proposal, to prepare for the final presentation. The POSCO AI Challenge was an opportunity for Noh to connect with others who were studying data science more creatively. “I enjoyed teaching myself, but I also felt it would be great to learn with others, bouncing ideas off of each other,” he added.

Noh hopes one day the program he developed for the contest can be implemented to help reduce food waste in POSCO cafeteria.

The POSCO Industrial-AI Solution Challenge is now over, but the winners aren’t slowing down. Throughout the contest, they have been running toward the finish line. Now that they have extra time to improve their models, they’re trying to perfect them as best as they can.

The members of Team Serio, Coke Zero, and Fast Learner all said the POSCO AI Challenge was a great learning opportunity, strongly encouraging other AI researchers to participate in similar contests. Simply participating in an actual contest can teach something new, but the contestants can also receive valuable feedback from field experts.

Throughout the interviews, the enthusiastic energy of the young researchers was not only palpable, but also contagious, all of them treating each question with thoughtful consideration. With their dedication, Korea’s AI future seems promising.

POSCO is accepting applicants for the fourth round, offering free education courses on artificial intelligence, big data, and IoT for job seekers as well as the public.

Last year, POSCO along with Pohang University of Science and Technology (POSTEC) created education programs on AI, big data, and IoT in the form of basic online and advanced offline courses. The program comes as part of the company’s social contribution efforts offered free of charge to encourage these skills and broaden job opportunities.

Application for basic online courses are available for everyone until September 2 through POSTEC website. Upon completion of basic online courses, advanced offline courses are open to selected individuals for 10 weeks from September 3 through October 24 at POSTEC. To promote the offline program that provides intensive training in a short period of time, POSCO plans to actively promote these efforts in domestic universities and online communities for job seekers.

The four-month program is designed and carried out by POSTEC professors while equipment expenses are provided by POSCO. Basic online courses started out with six in the first round expanded to 13 this year. The first round of courses have ended on September 2. Over 6,000 students have completed basic knowledge on AI, machine learning, computational intelligence and more. POSTEC plans to take previous feedback into account to further enhance the quality of the courses.

The online programs on AI, big data and IoT were completed by 26,494 students thus far. The advanced offline courses completed by 49 students have successfully entered the domestic workforce  such as interns at POSTEC, regular employees at LG CNS, Kakao, and SK Innovation. POSCO plans to further advance the courses such as blockchain, cryptocurrency, and computer vision for students interested in even more specialized fields.

Meanwhile, Jeong-Woo Choi, who was elected as the 9th CEO of POSCO on July 27, presented ‘With POSCO(Building a better future together as corporate citizens)’ as a new vision at the inauguration ceremony, expressing his aspirations to create a better society through contribution. This program represents POSCO’s major social contribution activity, aimed at increasing job opportunities while enriching the country’s competency in the fourth industrial revolution.

Besides utility managed solar farms, solar developers are targeting other open spaces, such as auto parking lots, for large-scale energy savings (Source: Phys.org)

The surge in these installations is prompting a parallel upswing in solar infrastructure, which is seeing its own technology advances as a result of high demand and the need for greater cost efficiencies. While sturdy racking is important to protect against the ravages of nature, one of the main requirements for large solar farms — and any larger installation — is solar tracking equipment.

Solar tracking offers grid operators the opportunity to squeeze efficiency from sunlight at all hours of the day. While fixed-panel arrays are highly efficient for general-purpose energy harvesting, single-axis solar tracking can increase a solar farm’s annual output by roughly 30 percent. Dual axis trackers can add 10-20 percent beyond that. For example, air mass tables for solar panels show that a panel mounted on a tracker can harness as much as 60% of solar intensity in the late afternoon at an angle of 15 degrees, 50% at 10 degrees, and capture 25% at a mere five degrees. In large installations these gains can mean the ability to respond with enough power to satisfy peak demand.

Solar tracking systems explained

A solar tracking system is used for a variety of applications. Depending on the applications, the mounting systems will differ somewhat. Typically solar trackers hold single or multiple standard flat panels, arranged in rows and funneling power to an energy storage site or directly to a substation.

Parabolic mirrors can focus sunlight onto solar collectors. (Source: EU Solaris)

However, solar trackers are critical for large-scale solar installations using concentrated solar power (CSP). These trackers hold mounted mirrors that reflect sunlight to a central collector tower containing hot liquid or gas. They are also a key element in concentrator photovoltaic (CPV) installations that use parabolic mirrors or lenses to focus sunlight (and even ambient light from the sky) into solar collectors that are part of each unit. These panels and mirrors require precise angles to enable optimized concentration of the light onto a fixed-location tower, into Fresnel lenses, or into a solar thermal collector. The tracking system may be centralized, where a single motor moves many rows of panels at once, or a distributed system, where an individual motor moves one row or table of panels.

Horizontal single axis trackers (pictured above) are better suited for lower latitudes. (Source: Energia Solar)

Solar tracking devices can be classified by the number of axes around which they rotate and by their orientation. Horizontal single axis trackers (HSATs) are horizontal to the ground and mounted on rotating tubes supported by pylons or frames. Horizontal single axis trackers with tilted modules (HTSATs) are similar but installed at an angle, for higher latitudes, but rotate the same way. Vertical single axis tracker (VSAT) panels, also for higher latitudes, rotate around a vertical axis, but the layout must be designed to prevent the rows of panels from casting shade on each other. A hybrid horizontal/vertical tracking system is referred to as a tilted single axis tracker (TSAT).

A tilted single axis tracker is a hybrid of horizontal and vertical tracking systems. (Source: The University of Queensland)

In addition to the panels or mirrors, tracker hardware may include steel posts or rails, cables, junction boxes, slew drives to rotate the panels, and hydraulic dampers to provide flexible resistance against the wind. The structures must be robust, well-anchored, easily transported and installed, and must be able to withstand the elements, including hot or cold locations, strong storms, heavy rain or snowfall, and even seismic activity.

Perceptions of solar tracking

For a time, solar tracking for large-scale installations had a reputation as a more expensive and maintenance-intensive technology despite the higher energy capture rates. Some of these reliability perceptions still linger. In actuality, according to Greentech Media, solar tracking systems boast a 99.9 percent uptime rate and advances continue in mechanical functionality and software-based optimization. In fact, the U.S. Energy Information Administration reports that of all utility solar projects constructed in the US in 2016, 79% used solar tracking rather than a fixed panel.

Advances in solar tracking

Despite being perceived as a less exciting infrastructural component of the solar industry, solar tracking has seen a number of technological and mechanical breakthroughs that promise increased growth due to lower costs and increased weather resistance.

One of the challenges is the economics of land use and site preparation. Until recently, utilities and third-party solar farm developers had to plan carefully to ensure that the needed amount of power could be generated from a particular site size given solar tracker configurations (e.g. HSAT vs VSAT). Additionally, they had to take into consideration the site contours and how much grading and preparation would be required to adapt the site to optimized solar tracker deployment. As a result, there has been stiff competition for flat sites.

Utilizing slopes and rolling terrain

In California, a company called Nevados Engineering is on the verge of launching single axis trackers designed to work on south-facing and sun-facing slopes and varied terrain, which offer better solar exposure. The company’s All Terrain Tracker (ATT) technology uses unique bearings that allow articulation with the driveline so it can fit the terrain using the same kind of posts as on flat land. Every post in the row is used to withstand the slope load, which is the toppling force exerted by the structure being pulled down the hill by gravity. The tracking units can handle slopes of over 20 degrees and can also handle side-to-side, and vertical, post misalignment of up to two feet. The advance vastly reduces the need for grading and landscaping, enabling solar developers to purchase significantly cheaper sloped or contoured terrain to maximize solar energy capture.  The company’s software enables developers to predict solar production for a site to calculate the economics.

Nevados Engineering’s All Terrain Tracker technology and help install solar trackers in all kinds of terrains. (Source: LA Cleantech Incubator)

“We need to look beyond what we expect solar trackers to do,” said Yezin Taha, CEO of Nevados Engineering. “Installing trackers on a slope can capture 7% more solar energy than trackers on flat ground, and 32% more energy than fixed tilt systems.”

Leveraging non-standard sites

Another recent development, floating ground mounts, has also made more large-scale solar sites available. While most solar trackers are mounted on posts that are driven directly into the ground or less frequently, on posts that use concrete foundations or ballast, certain kinds of terrain won’t support driving posts or invasive concrete structures. These sites, such as capped landfills, can be highly suitable for solar installations due to their height, restricted access, and unsuitability for other uses. The development of floating ground mounts, where the tracker is placed on a gravel pan that can be filled with a variety of materials, has made possible the use of these kinds of sites where posts can’t be driven.

Resisting the elements

Advances in resistive materials also show promise for the solar tracking industry. Posts and other tracking components are typically made of galvanized steel, which means they are coated with a high zinc content layer. Galvanization has been the most weatherproof and anti-corrosive solution to-date, but it still allows rust to form over time. Moisture can be an electrolyte that enables galvanic corrosion to occur, so that as humidity increases, the rate of corrosion increases. Also, atmospheric contaminants can catalyze the chemical reaction. Chlorides in marine environments, as well as and sulfur dioxide and nitrous oxides in industrial locations, may present a higher risk of corrosion.

Recently steel companies such as POSCO have begun to add magnesium to the coatings for further protection, a process that had been prohibitively complex until now.  POSCO’s PosMAC is a combination of zinc, aluminum and magnesium that “heals” any exposed parts of the steel by forming a layer of simonkolleite, a stable compound, over the vulnerable area. The development promises greater ruggedness and durability for tracking components.

Streamlining operations and maintenance

Finally, the advent of sensors, drone-based inspections, and the Internet of Things (IoT) will have a growing impact on solar farm operational costs by enabling remote monitoring, hardware predictability, just-in-time replacement and streamlined functioning. An operator in Germany could easily determine when to clean the solar panels in a Dubai installation after a dust storm or know when a tracking mechanism becomes jammed.

Solar tracking mechanisms can be monitored easily with the help of artificial intelligence. (Source: Micro Power Grids)

These advances, coupled with the cost efficiencies of solar and the increasing availability of grid-level energy storage solutions, will continue to boost the utility-scale solar energy market and take market share from fossil fuel power plants. The expansion of solar power offers a significant opportunity for multiple industries, from materials providers to solar panel manufacturers, tracking system component manufacturers, sensor distributors, battery makers, software developers and even drone suppliers. By leveraging the confluence of these technologies each industry can ensure that they participate in the solar market’s return on investment.

Tom Breunig is the publisher of Cleantech Concepts, an online magazine and market research firm tracking cleantech R&D and innovative business models.

What’s more, manufacturing is no longer a rigid industry that produces uniform, one-sided goods. There is constant communication between customers and businesses for hyper-customization. Not only that, machines and products communicate within a smart factory, and factories exchange data with other factories. Manufacturing is no longer limited to production plants. Moreover, the data generated in the manufacturing process is combined with customer information, and a new service can be created. This combination of manufacturing and services is resulting in creation of added value. In the midst of such drastic change, how can companies stay competitive?

Staying Competitive Through Connection and Convergence

Global manufacturing companies are at the forefront of the 4th Industrial Revolution, as evident in their smart factories. A smart factory collects data generated from the production process using ICT technology, and the system controls all processes from material input to the final product. This has led to flexible production systems with the ability to make various products in one factory or to expand the range of products on the basis of connectivity. Typically, when a company builds a smart factory, it can improve productivity by 20 to 30 percent. A 20 percent improvement in efficiency in the manufacturing sector is a significant, outright increase in global competitiveness.

SEE ALSO: How Smart Factories are Changing the Manufacturing Industry

Siemens Electronics Manufacturing Plant incorporates robotics, AI and IoT to its production processes. (Source: Zawya)

If the productivity of a plant can be improved through smartization, it is important to also think about connectivity with the ecosystem that exists outside of the plant. Once a smart factory is built, all the data from customer orders to production and delivery are collected in a system via sensors. The customer, product and production data create meaningful connections with each other and provide extensive insight. Examples of added value creation through meaningful connections include hyper-customized goods, data-driven after-sales services to customers and collaboration among companies connected within the smart factory’s external ecosystem. Such advancements will lead greater product quality, production stability as well as shortened delivery times between value chain suppliers.

The Adidas Speed Factory and GE Brilliant Factory are examples of successful smart factories. Adidas customers choose the materials, colors and design of their sneakers, and have them manufactured and shipped within 24 hours in an automated factory.

GE has built a system that can produce all of their widely-diverse products in one factory. When the factory receives customer orders, it operates in a flexible production system which starts with the necessary raw materials, inputted by the automated scheduling system that controls the entire production process, including the final distribution system.

The Future of Manufacturing

Overall, the adoption of innovative changes in the manufacturing sector is likely to progress from lighter industries to the heavy industries, from B2C to B2B sectors. Small plants, quick manufacturing and B2C companies can more readily adapt to rapid technological and market changes. On the other hand, heavy industries like steel and B2B companies with continuous and heavy manufacturing, large production volume and numerous linked companies are likely to be slower to adapt to changes.

It is much more difficult for traditional, heavy industries to adapt to changes. (Source: Live Mint)

For example, adapting to the changes of the 4th Industrial Revolution in the steel industry may be slow, but it is inevitable. What’s more, the long-term adaptation process is more likely to be systematic and deliberate.

As a leading company in the global steel industry, POSCO is pursuing a long-term, systematic “Grand Design” to reinvent its systems to align with the changes of the 4th Industrial Revolution.

First, POSCO built a pilot smart factory in their steel mill, Gwangyang Steelworks, in 2015 that is currently in operation. The company used IoT to collect big data on site, analyze it in real time and build a smart factory that enables optimal control through AI and self-learning. As a result, the Gwangyang Steelworks is reaping the benefits of a smart factory not only in cost reduction but also in improved steel quality, minimized malfunctions and a safe and stable production environment. This year, POSCO plans to expand and apply smart factories to all of its production processes.

POSCO built a smart factory in Gwangyang Steel Mill.

Smart factory application throughout the entire steel mill will improve overall efficiency through a flexible production system. In addition, the factory will be able to respond directly to various customers in real time based on platform construction with customers within the connected ecosystem. The customized characteristics and design of the steel grade for each customer can be applied to production in real time.

SEE ALSO: How Factories Produce Steel – the Smart Way

Ultimately, a smart ecosystem that links manufacturing, processing and distribution with customer input will lead to a new, innovative ecosystem within the steel industry. In Europe, some companies are experimenting with material libraries and steel distribution platforms. The material library displays a variety of materials for customers to see, touch and test the workability and performance of the materials, and get information about the characteristics, design and delivery times through the order platform. Customers can designate the shipment date on the spot. This will be one of the new promising business models that steel and other material companies will strive towards in the coming future.

POSCO’s Grand Design includes a step-by-step approach to smart factories to expand the use of IoT, AI and Big Data in its production systems. To this end, POSCO ICT has developed PosFrame, a standard software platform that collects basic data of production processes and collectively manages, controls and analyzes the information.

POSCO uses its software platform, PosFrame, for data collection and analysis.

When the software becomes standardized and reliable enough to extend to other sectors, it will be applied to other business areas such as energy and construction, as well as to POSCO’s affiliates.

Manufacturers will have to take into account the heavy production environment, the slow industrial change cycle and the complexity of related industries and affiliates to implement the most effective, long-term, systematic upgrades to its production systems. This will result in a brand-new production and business model for manufacturing companies that will align with the new environment of the 4th Industrial Revolution.

Cover photo courtesy of Sputnik International.

Here’s a look at how steel is produced in a steel mill or factory, and what the “smartization” of steel factories will look like.

So, how is steel made?

Iron Making

To make steel, manufacturers first need molten iron. Molten iron comes from two raw materials; iron ore and coal. Iron ore is converted into sinter ore in a sintering plant and coal is converted into raw coke using a coke oven. The processed materials are poured into a blast furnace where hot air reaching 1200℃ is blown in from the bottom through tuyeres, causing a chemical reaction. This process oxidizes the coke and reduces the sintered ore, creating molten iron.   

Steelmaking

In the steelmaking stage, the molten iron is transported to the steel making plant via a torpedo car, where the liquid is poured into a converter. Then, oxygen is blown into the converter to burn off all the impurities. All that is left is pure molten steel.

Continuous Casting

This is where steel finally becomes solidified into different shapes such as slab, bloom and billet. Liquid steel is poured into molds and cooled as it passes through a continuous casting machine until it solidifies into the desired shape.

Hot steel passes through rolling machines to be rolled into specific sizes and thicknesses. (Source: Global Sourcing Blog)

Rolling Process

During the rolling process, steel is heated once more to achieve various sizes and thicknesses. Steel slabs are heated to over 1100℃, then pass through rolling machines. This results in hot-rolled coils that can be shaped for different uses such as thick plates. They can also be processed into long, wire-shaped rods for billets. Often, the hot-rolled coils are rolled at room temperature for cold-rolled coils. Cold-rolled coils can be fabricated to produce galvanized and electrical steel products.

“Smartizating” these processes will involve converging IoT, Big Data, and AI to connect the different facilities, IT systems and workers in order to collect and analyze data for optimization.

POSCO is “smartizing” the steel-production process

POSCO is a steel company looking to lead the industry in adopting smart factories. CEO Kwon Ohjoon made “smartization” one of the 4 key priorities for POSCO starting back in 2014, and Kwon will continue to increase those investments.

POSCO smart factory incorporates artificial intelligence to enhance safety and efficiency.

SEE ALSO: How Smart Factories are Changing the Manufacturing Industry

In 2016, POSCO established its Smart Solution Council in order to research AI, big data and IoT applications. In the same year, POSCO ICT’s smart factory platform, PosFrame, was completed and installed in POSCO’s Gwangyang Steel Mill. PosFrame allows engineers to collect and monitor big data. So far, the company has saved over USD 14 million by incorporating the new technology in its production practices.

Here are some other features of POSCO’s Smart Factory.

POSCO’s Smart Blast Furnace

The Pohang Blast Furnace No.3 became a smart furnace in 2017, following a 102-day repair period. The furnace is now equipped with automated sensors that monitor and control its internal conditions using AI technology. Smart sensors monitor the blast furnace for factors like raw material quality and ventilator status, preventing breakdowns and ensuring a much longer lifecycle.

SEE ALSO: Will Artificial Intelligence Lead to Breakthroughs in the Steel Industry?

Worker Safety

Smart sensors can be used for more than just process monitoring. The company is working toward a full implementation of smart sensors for safety purposes, using IoT to create a better working environment.

Steel manufacturing involves high temperatures and high pressure levels, which is dangerous for workers who come in close contact with the equipment. With smart sensors monitoring all of the factory information, workers will instead be monitoring operation from a safe distance.

Workers in POSCO’s smart factory wear smart sensors for safety.

Wearable sensors, in conjunction with factory smart sensors, will be able to detect if and when workers are approaching potentially dangerous areas, and will alert them. These sensors will also detect and alert with regards to any impending accidents, or life-threatening situations like gas leaks, explosions, or fires.

Also, dust, sulfur, and nitrogen compounds will be removed via a high-plasma method, creating an eco-friendly steel plant and a healthier environment for workers.

POSCO will continue to add more smart features to its steel mills to increase efficiency, safety and sustainability. In efforts to learn and implement new technologies, POSCO CEO Kwon Ohjoon visited GE’s smart factories to learn about their technology earlier this year. POSCO also held the 2017 Smart POSCO Forum to share its insights with clients and affiliates, all as part of its expanding smartization efforts.

POSCO’s smart safety features

As a steelmaking company, POSCO has been working towards becoming a “Smart POSCO”, or incorporating the implementation of AI into their production practices. In 2016, POSCO established a Smart Solution Council to research AI, big data and IoT applications. To date, the company has achieved a cost reduction of USD 14 million with the new technology. Even as one of the first steel producers in the world to implement a smart factory for production, the road ahead is a long one. Members of the discussion panel at the 2017 Smart POSCO Forum offered their expert opinions on how companies should move forward with AI research and applications.

Problem Mining

“One of the biggest difficulties in applying AI to manufacturing companies is problem mining. The only way to solve a problem is to find it and define it.” Kim Byung-In, a professor at the POSTECH Department of Industrial and Management Engineering, started off discussions and added, “Along with problem mining, people can learn from the past with reinforcement learning. Reinforcement learning means learning through similar tasks from the past to apply to new assignments. Defining problems and solutions more precisely and systematically managing related keywords can be useful for future tasks.”

Professor Zhang Byoung Tak, an AI professor at Seoul National University and a distinguished professor for POSCO added, “Traditionally, people solve problems, but in the generation of AI, AI will be able to solve problems for us… People now have to focus on defining problems, implementing systems and getting the right kind of data.”

Cooperative Research and Investment

Along with defining the exact problems that AI can provide solutions for, the implementation of AI will also call for a new working culture for companies and industries.

Tech company VUNO CTO Jung Gyuhwan stated, “Each company has its own production or operation method, and different data formats and protocols, but product inspection and screen UI (User Interface) are common areas. If we can find such common areas that can be commercialized and continue to research ways to expand AI applications to different sectors, we can achieve meaningful results together.”

Researchers developing programming and coding technologies

Jung went on to highlight that most AI personnel are unfamiliar with manufacturing. In particular, steel production terms are so difficult that it takes a considerable amount of time to share and understand information. There are also numerous obstacles and obligations for startups entering an unfamiliar development environment full of regulations and restrictions on things like the use of codes.

“In other words, task efficiency is a trade off with information security. I believe that if there is such a process in POSCO where the risk of technology leakage is not high, it will speed up results through collaboration with start-ups. POSCO ICT in Pangyo is a good place for startup employees to work. If we can take advantage of the POSCO ICT databases, collaboration with start-up companies will be easier. As of now, there are not many AI companies in Korea that specialize in manufacturing. Most people don’t even know that there is a lot of data available in the manufacturing industry. That’s why fostering start-up companies specializing in manufacturing now, is a good idea.”

Google’s Fei-Fei Li announces Google’s acquisition of Kaggle. (Source: Venture Beat)

According to CB Insights, M&A (mergers and acquisitions) of AI startups have increased 700 percent since 2011, because they are more efficient and often more insightful when it comes to AI research. Besides investing in existing startups, members of the panel agreed that collaborative competitions such as Kaggle, a contest platform where data scientists from around the world participate in solving problems in industry and society, are a great place to find talent and foster them as start-up companies.

Tackling New Challenges

The flurry of activity around AI research shows just how new the sector really is. People are still finding ways to apply AI across different sectors and industries. In such a volatile environment, how do companies and researchers navigate their way forward?

According to Choi Heeyoul, a professor of computer science at Handong Global University, companies need to “start where the people, problem definition and data are ready. Whether it is in production, sales, distribution, etc., if there is the will to solve problems, then results will follow. Especially when collecting data, you need the help of data analysts. For example, classification data for different types of steel can only be analyzed if the history of the classification criteria is recorded. Speech recognition is not any better for AI application than other fields, yet the most active and productive research has been completed in this sector. This is attributable to the will to apply AI, the abundance of accurate data and close cooperation with data analysts.”

Obtaining accurate data is vital for AI research

Professor Zhang Byung-Tak had another approach to offer. “I hope people will boldly take on challenges. The dramatic increase in computing power can now enable attempts that were previously considered reckless…No matter how much data we gather, it will never be enough. We need to approach problems as experiments and just start. One way to go about it is to work in reverse instead of waiting for data to be accumulated. You can start the task to define the data that is needed and determine the coherence of the data to the task.”

Lee Jong-Seok, a Professor at Sungkyunkwan University added, “When we are defining problems, we need to separate them into ones that can be solved in the short term and ones that require long term solutions. Some problems can be solved with minor improvements, while other problems will persist. For the latter, it is important to have patience and invest in a solution over a long term.”

That’s exactly what manufacturing companies are doing. They are collecting data every stage of production all the way to sales and administration. Big data is now available to study and apply to boost companies’ efficiency, sustainability and performance. Those who come up with the optimal software and modeling systems for data analytics will win the competitive edge early on.

UNIST professor Choi Jaesik echoed the panel’s sentiment, in closing, saying, “It’s very uncommon for a manufacturing company to hold AI courses for all their employees. I think POSCO can take pride that…POSCO is a great place to apply AI and achieve results. There is already plenty of data on site and employees are continuously working to obtain more accurate data. I look forward to seeing what POSCO will bring to the advancement of AI.”

POSCO CEO Kwon Ohjoon plans on doing exactly that, by increasing investment in research and implementing AI technology where applicable. Read more on POSCO’s smartization efforts here.

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POSCO has been ranked as the world’s most competitive steelmaker for seven years in a row. It has remained competitive in part by focusing on its World Premium Products, which offer advanced steel solutions for their customers. POSCO has also put extensive effort into maximizing its non-steel technologies and products to find new growth in non-steel sectors. In addition, POSCO has been a leader in innovating its own manufacturing processes, incorporating advanced AI and IoT technologies into its smart factories. POSCO has also remained committed to not only growing its non-steel secondary businesses, but advancing upon existing technologies to create new and eco-friendly production methods.  Below we take a closer look at how POSCO has continued its work to innovate and stay competitive.

POSCO Expands Lithium Manufacturing Business

Amid the rising demand for rechargeable lithium-ion batteries used in smartphones, laptops, and electric vehicles, POSCO has been working on proprietary technology for lithium extraction. Earlier this year, POSCO opened Korea’s first lithium plant, PosLX, and commercialized the production of lithium carbonate from rechargeable batteries after 7 years of development.

From left to right: Jae-chun Song, chairman of Gwangyang City Council, Hyun-bok Jeong, mayor of Gwangyang, Gi-jong Woo, deputy governor of South Jeolla Province, POSCO CEO Ohjoon Kwon, Ung-beom Lee, president of LG Chem and Nam-seong Cho, president of Samsung SDI who can be seen pressing symbolic buzzers to show that the plant is now online.

The PosLX Plant is expected to produce 2,500 tons of lithium carbonate per year using a new eco-friendly technology, which is enough to manufacture about 70 million laptop batteries. This output will supply POSCO’s battery making partners LG Chem and Samsung SDI, as well as POSCO ESM, a subsidiary that produces cathodes for secondary batteries.

POSCO incorporates AI into Smart Factories

POSCO is also breaking through technical barriers by designing smart factories connected through IoT technology and artificial intelligence (AI) that produce higher quality products with less waste. POSCO has also begun incorporating advanced AI technology into its smart factories in order to more efficiently manufacture automotive steel.

Smart factories operate autonomously as the machines talk to each other through sensors – reducing faulty products and waste.

In particular, the ‘Smart Solution for Coating Weight Control Based on AI‘ technology uses an automated control technology that predicts the coating weight in real time and accurately meets the target coating weight. When coating weight is controlled manually, quality deviates depending on the skill level of the worker, which inevitably results in significant amounts of wasted zinc. However, when it is automatically controlled by AI, the quality of POSCO’s automotive coated steel can be enhanced while production costs are decreased. These new automated processes have also helped increase work efficiency and productivity with workers.

Also, CEO Ohjoon Kwon recently visited Siemens in Germany and GE in the U.S., both of which are known to have successfully operated advanced smart factories to observe how other companies have implemented highly sophisticated smart factory models. Through close cooperation with POSCO’s major affiliates such as POSCO E&C, POSCO Energy, and POSCO ICT, Kwon aims to embrace smart technology and reorganize POSCO’s entire business structure (Smart Factory, Smart Buildings & Cities, and Smart Energy).

POSCO’s Thailand CGL, Southeast Asia’s First Automotive Steel Sheet Plant

Faced with sluggish growth due to the oversupply from China and a decline in steel demand, POSCO has been looking to expand sales in new markets. In a bid to become the world’s largest steel provider for automobiles, POSCO completed its Thailand CGL (Continuous Galvanizing Line) in 2016. The plant has an annual production capacity of 450,000 tons and incorporates some of the most advanced AI technologies in its smart factory system.

In addition to being one of the strongest automotive production bases in Asia, Thailand is also the center of the consumer-electronics industry in Southeast Asia.

With the newly established automotive steel plate plant in Thailand, POSCO’s global volume will reach over 9 million tons in 2017 and more than 10 million tons per year starting in 2018. The automotive steel sheets manufactured by the facility will be supplied to global auto parts companies and carmakers operating in Thailand including Toyota, Nissan, and Ford. Despite the increase in competition from other steelmakers, POSCO aims to become a market leader through building strategic partnerships with global automakers operating in the country.

POSCO CEO Ohjoon Kwon and Deputy Prime Minister and Minister for Foreign Affairs Tanasak Patimaprakorn shake hands at hands at Thailand CGL.

As cities around the world get more crowded, urban dwellers are facing more and noisier neighbors. To help combat this problem, POSCO introduced PosCozy in 2016, a unique flooring system that combines manganese (Mn) Z-clips with continuous galvanized steel plates that greatly reduce impact noise, save space, and lower costs (product video).  

Unlike conventional flooring that uses non-steel materials such as styrofoam insulators and rubber buffer materials, POSCO’s floor plates are developed by combining high Mn steel Z clips – a material that is 4 times more vibration-resistant than general steel – giving it the ability to reduce noise between floors. PosCozy is able to reduce the sound of typical walking from 50dB to just 40dB, while impact sounds are lowered from 58dB to 38dB.

PosCozy is the world’s first high manganese steel floor plate with first-class floor noise reduction.

PosCozy is the world’s first high manganese steel floor plate with top quality floor noise reduction. It is also the first flooring system to receive the highest rating in sound insulation from Korea’s industrial rating agency.  Due to its unique and superior qualities, PosCozy won the prestigious Jang Young Sil Award in 2016.

POSCO’s High Manganese Steel Used in the World’s Largest LNG-Fueled Bulk Carrier

Due to the rising number of shipbuilding orders for large-sized liquefied natural gas (LNG) carriers, there has been a shortage of nickel,  requiring new and innovative solutions to construct LNG-fueled ships. In order to address these issues, POSCO developed a new type of high-performance manganese steel and announced in 2016 that it would be applied to the LNG-fueled bulk carrier built by Hyundai Mipo Dockyard.

Developed independently by POSCO, this high manganese steel contains an Mn content of 20%. Compared to traditional materials, it boasts improved performance through unique properties that include high strength, low-temperature toughness, wear resistance, non-magnetic, and damping properties.

It is the first time a bulk carrier will have been constructed to include an LNG-fueled system and a fuel tank made of high manganese steel.  Its higher strength, ability to withstand an extremely low temperature (-162℃), and cost competitiveness make it superior to the nickel and aluminum alloys that are typically used in tanks. The ship, which will be the world’s largest LNG-fueled bulk carrier, is expected to have a capacity of 50,000 tons, seven times more than previous ships.

POSCO’s high-performance manganese steel will be applied to the world’s largest LNG-fueled bulk carrier built by Hyundai Mipo Dockyard.

POSCO has been able to overcome immense barriers, from the nickel scarcity in the LNG market to the oversupply of steel from China. POSCO strives to be a pioneer across all industries with its innovative products and will continue to do so in the years to come.

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We are entering a new era of manufacturing, one in which computers and machines are coming together to perform automated actions. Even more impressive, they are able to do this with less and less human involvement, learning from their mistakes and teaching themselves to constantly perform better.

Smart factories operate autonomously as the machines talk to each other through sensors – reducing faulty products and waste.

This new era, what is being called Industry 4.0, is one in which manufacturing moves beyond some of its former limitations by becoming more efficient, less wasteful, and much more productive. Corporations like Siemens, BASF, GE, and POSCO are leading the way by designing smart factories connected through IoT technology and artificial intelligence that produce higher quality products with less waste.

Industry 4.0 and the Smart Factory

The First Industrial Revolution marked the movement from pure human labor to using machines. Steam engines also appeared helping move things along further and more quickly. Next came electricity, and with it, mass production. The Third Industrial Revolution saw the rise of computers, more automation, and the replacement of some human labor. Now, we are entering the Fourth Industrial Revolution, or Industry 4.0, in which these machines interact and learn from each other with little to no human interference.

(Infographic courtesy of Christoph Roser)

With Industry 4.0 we are seeing the smart factory. A smart factory is a physical manufacturing system connected by AI, IoT, and tons of data in the cloud that teach the systems to work by themselves and make improvements without human involvement.

Bernard Marr says that for a factory to be considered Industry 4.0, it must include:

• Interoperability — machines, devices, sensors and people that connect and communicate with one another.
• Information transparency — the systems create a virtual copy of the physical world through sensor data in order to contextualize information.
• Technical assistance — both the ability of the systems to support humans in making decisions and solving problems and the ability to assist humans with tasks that are too difficult or unsafe for humans.
• Decentralized decision-making — the ability of cyber-physical systems to make simple decisions on their own and become as autonomous as possible.

In the examples below, we can see how three corporations are bringing these pieces together to advance smart factory production. Siemens, GE, and BASF have all utilized this technology to build a more efficient production system, and POSCO is doing the same in its steel production line.

Siemens, GE, and BASF – Case Studies in Smart Manufacturing

Siemens

The Siemens AG plant in Amberg, Germany, is unique in that it not only produces automated machines to be used in other industrial factories, but that it does so using fully automated machines in its fully connected smart factory. With over 1,000 manufacturing units connected via the web across 100,000 square feet, each step of the manufacturing process is automated and programmed to make customized products immediately.

The plant, established in 1989, is currently operated with around 1,200 employees and about 75% of operations are automated. The plant manufactures more than 12 million programmable logic controls (PLCs) that are used to automate cruise ships, automobile production lines, and ski lifts to name a few. With production quality at an impressive 99.99885%, an inspector would be hard pressed to find any defective products.

While Siemens has largely automated the production process, workers are still needed in the beginning to place the circuit board on the production line and supervise it through its production cycle. Producing more than 1,000 product variants for over 60,000 customers worldwide, Siemens has developed a model smart factory with automated systems creating nearly perfect products with less waste.

BASF

At BASF, the chemical giant incorporated a smart factory system so that it could manufacture fully customizable soaps and shampoos. When an order is placed, the automated factory line adjusts its protocols to make the unique product and packaging that was ordered by the customer. The set up is automatic and the quality is near perfect.

At its smart pilot plant in Kaiserslautern, Germany, once an order is placed for a personally customized soap or shampoo, radio-frequency ID tags that are attached to the soap containers send out wireless signals. These wireless signals tell the machines on the production line about the customized order.

The setup and manufacturing process is automated, exact, and with near perfect quality control.

GE

At GE’s Advanced Manufacturing Works in Greenville, South Carolina, engineers are working on new ways to streamline smart factories using AI and IoT technology. This $75 million plant sits next to where GE makes the world’s largest gas turbines – products manufactured on a huge scale with incredibly intricate pieces. They have introduced smart factory systems into this factory and are working to provide more to its other plants around the globe.

Staffed with 80 engineers, PhDs, and machinists with decades of experience – they will use the Advanced Manufacturing Works to try new manufacturing and design ideas in order to streamline the production processes of other factories.

POSCO Implements AI Into Its Smart Factories

POSCO CEO Ohjoon Kwon recently visited Siemens and GE to compare their work in smart factory systems with POSCO’s. All three of them are working on vastly different products; however, the ideas behind the technology are similar.

At POSCO’s Gwangyang Works, they have installed a data integration infrastructure that encompasses all of its operations and facilities. This technology uses an automated control technology that predicts the coating weight of zinc in automotive steel in real time to precisely control the Continuous Galvanizing Line (CGL), the primary technology used in automotive steel sheet production. In combination with the coating weight system, they have also created a data pre-analysis system that can preemptively detect abnormalities.

POSCO’s smart factory systems have improved quality and reduced waste

Coating weight control is a highly-sophisticated technology that keeps the thickness of the coating layer consistent – even when operating conditions change suddenly. When coating weight is controlled manually, quality deviates depending on the skill level of the worker, inevitably resulting in significant amounts of wasted zinc. However, the plating process is now automatically controlled by artificial intelligence, increasing the quality of POSCO’s automotive coated steel while decreasing production costs.

Also, in order to apply smart technologies more quickly, POSCO Group University is partnering with POSTECH (Pohang University of Science and Technology) to build an AI ecosystem that nurtures AI specialists and fosters advanced research. POSCO Group University will be in charge of launching related training programs POSCO and its subsidiaries, while POSTECH Information Research Laboratories (PIRL) will be in charge of developing content for basic and advanced courses to increase people’s understanding of AI, big data, programming, pattern recognition, machine learning, natural language processing, and computer vision. This partnership will serve as a significant milestone in expanding cooperation between companies and academic institutions, a fitting move in the era of the Fourth Industrial Revolution.

POSCO, Siemens, GE, and BASF are all utilizing the connected technologies of Industry 4.0 to move their industries forward. GE recently estimated there will be over “50 billion connected devices by 2020 and that the Industrial Internet could add $15 trillion to global GDP in productivity gains over the next 20 years.” Despite the industry barriers facing steel companies, POSCO is leading the industry in implementing connected technologies that will help them build the smart factories of the future and remain competitive for years to come.

Automotive coated steel, one of POSCO’s World Premium Products (WPP), is a high-grade product that requires a high level of sophisticated technology. Only 20 out of 800 steel companies in the world can produce it at this time. Last year, POSCO sold approximately 9 million tons of automotive steel sheets, accounting for 10% of the global automotive steel sheet market.

The ‘Smart Solution for Coating Weight Control Based on AI‘ is a technology that drastically reduces the deviation of coating weight by using artificial intelligence to precisely control the Continuous Galvanizing Line (CGL), the primary technology used in automotive steel sheet production.

In particular, this technology uses an automated control technology that predicts the coating weight in real time and accurately meets the target coating weight by combining the coating weight production model of the artificial intelligence technique with the control model of the optimization technique.

A developer and worker are seen in the operating room monitoring the optimal coating weight predicted through artificial intelligence. POSCO succeeded in developing the ‘Smart Solution for Coating Weight Control Based on AI‘ through a joint industry-university effort, and applied it to POSCO 3CGL last January. With artificial intelligence, it is now possible to collect hundreds of different types of data in real time, as well as accurately predict and control target coating weights.

Coating weight control is a highly-sophisticated technology that keeps the thickness of the coating layer consistent even when operating conditions change at the request of automakers. When coating weight is controlled manually, quality deviates depending on the skill level of the worker, inevitably resulting in significant amounts of wasted zinc. However, the plating process is now automatically controlled by artificial intelligence, increasing the quality of POSCO’s automotive coated steel while decreasing production costs. These new automated processes have also helped to increase work efficiency and productivity with workers.

Since the inauguration of CEO Ohjoon Kwon in 2014, POSCO has been preparing to take the lead in smart solutions by integrating AI into its smart factories. Last June, POSCO Technical Research Laboratories identified the need for an automated coating weight control system and collected the relevant data after discussing with several departments. POSCO worked with Prof. Jon-seok Lee of the Department of Systems Management Engineering at Sungkyunkwan University in Seoul, Korea, to develop the artificial intelligence coating weight prediction model algorithm. Prof. Lee is an expert in statistics, data mining, machine learning and optimization methodologies. He collaborated with POSCO researchers to develop the coating weight prediction program after analysis of the plating process.

Starting in July 2016, a beta program was introduced for two months. After successful completion of the beta test, POSCO Technical Research Laboratories added an additional program to ensure quality control even under changing operating conditions.

The core AI technology that was applied to the coating weight control automation is a self-learning software that utilizes big data deep learning techniques. This method operates in real time allowing the AI program to stay up-to-date by analyzing hundreds of different types of data generated during the plating process. It can accurately predict and control coating weight through real-time, even if equipment has been replaced or operating conditions have changed.

The completed coating weight control automation solution was applied to POSCO’s 3CGL line in Gwangyang on a trial basis for approximately 2 months, enhancing accuracy and stability. Normally, when operating manually, deviations in the coating weight can extend up to 7g per m², but with POSCO’s AI-based technology, this number is reduced to 0.5g per m². After a technical verification process, the technology was incorporated into the 3CGL line on January 5.

To maintain its position as a global leader in automotive coated steel technology, POSCO is planning to apply this coating weight control automation solution to other CGLs at home and abroad, like the recently opened Thailand CGL. POSCO is also taking steps to introduce artificial intelligence technology to the manufacturing processes of other steel products while also continuing to build smart factories that can help POSCO continue to be a leader in the steel industry.

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