May 2024 Volume 6

MAINTENANCE

MATERIALS

Efficiency and Quality Efficiency and quality require considering the entire supply chain and opportunities where steelmakers, forgers, machinists, and end users all play a role. The interchange among these entities, guided by a comprehensive grasp of raw material requirements and customer preferences, constitutes the foundation on which logistical efficien cies are built. Automation and machine learning enable steel manufacturers like Metallus to achieve greater precision and reduce waste in their opera tions. Forging customers rely on precision in steel composition and dimensions for their own productivity and quality. Marked by an injection of capital in automation and technolog ical advancements, the specialty steel industry heralds a renaissance in manufacturing prowess. As steelmakers transform facilities into smart factories, investment in devices and applications approached $500 million in 2022. By 2031, this number is expected to surge to nearly $1 billion.1 In fact, the greatest amount of technology spending in the steel industry stems from data analytics, with $790 million spent in 2022 and $2.9 billion anticipated by 2031. Data analytics are a key foun dation for addressing environmental concerns because they allow steelmakers to optimize the production process and to track carbon emissions output. Data analytics also help steelmakers plan for waste reduction, promote a circular economy and reach higher-quality levels. This surge of innovation strengthens the industry's competitive advantage and aligns with the increasing need for specialized steel products customized to suit the requirements of demanding appli cations. At Metallus, the integration of sophisticated sensor technology throughout production lines is one example. These sensors are stra tegically deployed at critical junctures across various stages of the manufacturing process, from the initial melting of raw materials to the final rolling and finishing of the steel being calibrated to monitor an array of vital parameters with pinpoint accuracy. Temperature sensors are installed for control that is crucial for achieving optimal molecular structure during melting and casting. Pressure sensors are placed along pipelines and within reaction chambers to detect any deviations that could indicate leaks or block ages, potentially preventing system failures. Chemical composition analyzers also play a critical role in real-time monitoring by using techniques such as spectroscopy or chromatography to assess the elemental makeup of the steel, ensuring that the proportions of carbon, manganese, silicon, and other alloying elements comply with precise specifications for different grades. This sensor network feeds data continuously to a central processing unit equipped with advanced machine learning algorithms. The system analyzes the data against historical performance metrics and current production requirements, enabling real-time decision making. If a parameter deviates from its target range, the system instantly adjusts the settings of the relevant machinery, such as when furnace temperature drifts lower than desired.

The application of machine learning is also crucial for achieving unparalleled precision in steel properties, scrutinizing a complex array of variables to tailor the processes, including rolling to the product specifications. For example, sensors embedded within the rollers capture data including the steel’s internal temperature, the ambient temperature of the environment and the mechanical pressures being applied. The system also considers the specific properties of the steel alloy being processed—such as its carbon content, tensile strength, and elasticity. Learning models are then trained on historical production data including analysis of past successful runs where optimal quality was achieved, allowing the system to identify patterns and predict the best possible settings for current operations. By integrating this material-specific data, the algorithms can adjust rolling speeds and pressures accordingly. For instance, if the steel is detected to have a higher alloy content, which could potentially make it harder, the system may choose to lower the rolling speed or increase the pressure. Or on a particularly humid day, the algorithms might compensate by tweaking the rolling parameters to counteract any potential impacts on the steel’s microstructure. These automation investments ensure that the steel rolled in Metallus’ facilities not only meets the required specifications but does so with a precision that minimizes waste and maximizes performance. The end result is a product finely tuned for the application, underpinned by technology that drives industry standards forward. AI also plays a crucial role at Metallus in analyzing equipment data to foresee potential issues and ensure seamless operations. Simulta neously, this virtual intellect is crucial in codifying best practices and incorporating specialists' expertise into daily operations. By harnessing the knowledge of top experts, for instance, AI enables the standardization of this information, making it accessible to everyone. In terms of machine operations, this can help to ensure consistent quality across the board, regardless of the differing skill levels of individual operators. Labor Labor shortages emphasize the pressing need for adaptation. Across the industry spectrum, the struggle to appeal to a younger generation amplifies the challenge of acquiring skilled workers. This predica ment is not just a reflection of changing demographics but a call for adaptation. As one industry expert puts it, "finding reliable labor is a constraint," highlighting the role of automation in bridging the gap. The digital revolution, often misconceived as replacing humans with machines, presents yet another nuanced solution in the context of the steelmaking industry. Robotics, machine learning and AI serve as pivotal tools to augment the existing workforce by taking on repetitive, physically demanding or often dangerous tasks, thereby addressing the labor gap while also enhancing the performance and job satisfaction of plant workers.

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