May 2022 Volume 4

AUTOMATION

address the actual plastic deformation of the workpiece as well as the elastic deflection of the press during the forging operation. For this, two Digital Twins were established – one to address the actual deformation of the workpiece and another to study the press as it applies force to the workpiece. The ANSYS modeling/simulation software platform was used for the press modeling and the DEFORM platform used for the workpiece modeling. Once the development of the digital twin architecture is completed, the physical and the cyber systems will be able to communicate during the forging operations. The idea of all this is to collect process and product data, through the Digital Twin, on the workpiece as it is being formed, while also defining how the press responds to the stresses of workpiece deformation. Since the digital data will be streamed through internet-connected devices, engineers and plant managers will be able to remotely monitor the forging operations. According to Prof. Ngaile: “The ultimate goal in our research is to determine through DT technology if a manufacturing parameter needs adjusting and making that adjustment in real time automatically without need of human intervention, downtime, or product flow disruptions. Keeping the machinery running smoothly improves the life expectancy of the equipment, ensures consistent product quality, and keeps manufacturing costs down. Whether the cause of a problem is mechanical, thermal or tribological, DT technology can potentially improve equipment and process designs.” And there is another good reason that Ngaile is so sanguine about the prospects of the Digital Twin technology. He says that “if we continue to apply the concepts of DT technology to our metal forming processes, we will attract more young engineers to our metalworking industries.”

In early 2021, Stefan Lagerkvist, CEO at Viking Analytics, reported that “the project has so far gone well, and the collaboration works well with digital meetings and live feeds to follow the production.” The project is expected to reach completion in Q3 0f 2022. The Lure of Automation Investment There are a number of ways to increase the automation component of a traditional forge shop’s operations. Investments in robotics is one of the most popular, and such was the case at Cleveland-based Presrite Corporation, a commercial steel forger with three production plants and a technical center in Ohio. In its fifty-first year of production, Presrite is a “high-mix, low volume” forger of low-carbon steel alloys, forging gears and other products used in many different applications and industries. They also provide additional value-added services such as shotblasting, heat treatment, and machining services. According to Martin Diemer, Presrite’s Corporate Director of Operations, “We always talked about automation, but for a variety of reasons we never did it. Several years ago, I attended a FIA-sponsored workshop on forging automation, which toured us through an automated forge, a stamping plant and a fabricating shop. After that we put a team together to study our entry into robotic automation.” It was decided that theywould studymaking a robotic investment for one of the forging lines in the Jefferson, Ohio plant. The team formed included seven people including management, people from operations and maintenance, an electrician who new PLC controllers and how to program them, and a robotics champion to “take the project to the floor and make it work.” Eventually, the eighth member of the team became the robotics integrator. The team looked at a couple of different robotic applications and, in what Diemer called “human assisted automation,” decided on a robot that would pick up heated billets from the induction furnace and place it on the first die station. At that point the tooling would flatten the cylindrical billet before the

Finite element simulation of a forging screw press at North Carolina State University, highlighting field data used in the development of a Digital Twin architecture for forging presses. Courtesy of North Carolina State University. Another Digital Twin In 2020 Viking Analytics, a Swedish provider of advanced analytic solutions partnered with Bharat Forge Kilsta (BFK), a Swedish forge, to assess the data collected by oven sensors in the oven that heated steel for crankshaft and front-axle beam manufacturing. In BFK’s Karlskoga plant, the steel is heated in an induction oven, whose temperature varies according to different steel grades and products. If a disruption occurs, the oven must be manually adjusted to keep the metal at a constant temperature, which sometimes caused human-related deviations in the proper temperature-level records. Based on large amounts of sensor data from the oven, it was expected that artificial intelligence (AI) should be able to control the system so that temperature adjustments can be made automatically, eliminating human error. To achieve that, data scientists at Viking Analytics developed a Digital Twin that simulates the production stage and tests if adding more sensors or changing parameters can influence the quality of the data to be used in machine learning.

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