The integration of Artificial Intelligence (AI) and Machine Learning (ML) models in vehicle shop floors has revolutionised manufacturing processes.
30 Sep 2023 | 14972 Views | By Radhika Dave
Tata Technologies is using artificial intelligence and machine learning to make its manufacturing processes more robust for its clients. The company is consistently using new technologies to reduce downtime and curtail pollution as well. Yogesh Deo, Senior VP and Delivery Head, ER&D (Engineering Research and Development), Tata Technologies explains how ...
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Tata Technologies is using artificial intelligence and machine learning to make its manufacturing processes more robust for its clients. The company is consistently using new technologies to reduce downtime and curtail pollution as well. Yogesh Deo, Senior VP and Delivery Head, ER&D (Engineering Research and Development), Tata Technologies explains how this has been achieved.
"Essentially, we studied the entire related process data for a year, and with the use of AI/ML models, we were able to reduce power consumption by 18 percent, there was an improvement in the process quality and a reduction in fuel consumption by another 10 percent and chemical consumption reduction by another 8 percent,” Deo told Autocar Professional.
Further assessing the importance of using AI, Deo explained how they have been able to identify anomalies to reduce downtime on the shopfloor. Taking the case of the paint shop blower, he said that if one of them goes down, it results in the entire process getting halted, and the repairs take anywhere from 7-8 hours to get it running at an optimal state.
“You can call it in like an anomaly detection just before the failure or actual shutdown. So that means we are not even waiting for the actual shutdown or failure to happen, but the moment when we are able to identify the anomaly. Then we are able to understand it and then we can take the corrective action there and there itself,” he added.
He also explained how for tooling, artificial intelligence was used to predict tooling failure in the case of dye design. “Here what we have done in the case of dye design is that we have gone through the historical operational data and prepared a model, which you can compare with the current operational data and then you can predict whether the dye will perform to its optimal position or it may fail. So we were able to correlate the operational data and failure cases and thereby we are able to determine the remaining useful life of the dye.
The company is "putting a lot of effort and investment into building a Centre of Excellence for AI technology," he said without divulging more information at the moment.