Digital twins: taking smart machining processes to the next level

What is it about?

Studies reveal that manufacturing stores more data than any other sector, and nearly every machine and sensor send out enormous amounts of data across the Industrial Internet of Things platform. However, it is widely accepted that manufacturing has been far from meeting its true potential in the digital age. One challenge is that much valuable domain expert knowledge and insight from the product lifecycle are not in the context of the shop floor data. Without the context information and domain knowledge, it is very difficult for data scientists to organize and analyze the data. Consequently, manufacturing cannot easily use the data to control and optimize processes to save energy, reduce waste and improve product quality. Also, a considerable amount of operational data, particularly those generated from design to process planning and manufacturing, are often underutilized, if used at all. These data are rarely “systemized” into a source that can make them available as part of the overall pool of operational data unless a formal procedure exists as part of operational processes. To address this technical gap, a paper published in 2020 proposes a smart machining process. What does this process do? It simulates industrial production, digitally. In a way, it creates a “digital twin” of the industrial production process. This twin is then studied to catch any small undesired abnormal conditions that are evident in the process. It gives a real-time look into the process. It also reduces the need for “dry runs.” These are done to test the equipment prior before it starts actually machining.

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Photo by Stephen Dawson on Unsplash

Photo by Stephen Dawson on Unsplash

Why is it important?

Large-scale production depends on efficient machining. Machine errors or lags in industries lead to reduced output, late deliveries, and dissatisfied customers. Now, the digital process that this paper proposes can pick up on subtle changes in the operation of machines. These changes are often a warning sign of machine breakdown due to excessive wear and tear. By calling out these changes, the process helps predict any lag in production caused due to machine breakdown. This helps industries foresee issues, reduces operating costs, and increases equipment life and production capacity. Moreover, the digital twin provides operational data. It helps gain insight into the machining of the product, so that it can be customized. The quality of the product, too, can be controlled. No additional parts are needed because the process builds on what is already present. Hence, it is easy and cheap to take on. And an added bonus? It can be used anywhere! KEY TAKEAWAY: Simulating industrial production digitally helps in many ways. It can make industrial production economical, quicker, and streamlined! Preventive maintenance will be a direct consequence of the use of the digital twin concept. With real-time data collection, trends are predictable and allow proactive maintenance actions, to reduce production downtime or to optimize the life of system components.