Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

The energy consumption of production processes is increasingly becoming a concern for the industry, driven by high cost electricity, growing environment and greenhouse emissions. It necessary to develop improve efficiency systems, reduce ecological footprint costs. Thus, in this work, system developed capable extracting evaluating useful data regarding metrics outputs. With extracted data, machine learning-based models were created predict expected an automotive spot welding, proving clear insight into how input values can contribute each product or machine, but also correlate real ideal ones use information determine if some process not working as intended. method demonstrated real-world scenarios with robotic cells that meet Volkswagen Ford standards. results are promising, accurately from allow managers infer problems optimize schedule decisions based on consumption. Additionally, nature conceived architecture, there room expand build additional systems upon currently existing software.