Manufacturing process energy consumption modeling: a methodology to identify the most appropriate model

Abstract This paper investigates the appropriateness of machine learning (ML) and other techniques for modeling manufacturing processes energy consumption by developing a comparison methodology. Three research questions are posed: Firstly, how do prediction errors compare using different techniques with varying complexity and ML use? Secondly, how does performance vary with different amounts of data? Thirdly, how do different techniques compare in terms of required expertise, effort to build and interpretability of results? To answer these questions, the authors develop a structured approach, which is also envisioned to be useable by practicing engineers and manufacturers. Four modeling categories are defined, ranging from simple non-ML methods, such as linear regression, to complex ML methods, such as deep neural networks. The approach is evaluated using data from a compound feed manufacturing process. The results confirm the notion that non-ML models are better suited to understand and model manufacturing processes when few parameters are present, due to their high interpretability, while ML models are recommended for analyzing processes with many potentially relevant and interrelated parameters. Interestingly the approach finds that the complex ML category model does not outperform the simple ML category model in terms of prediction accuracy, and only has the drawback of requiring more expertise to build and having lower interpretability. The study concludes that the decision to use complex ML for modeling manufacturing process energy consumption should be critically questioned and that a simpler approach may be better suited, suggesting that the developed methodology would be of value to practicing engineers.