Energy Efficiency Modeling for Configuration-Dependent Machining via Machine Learning: A Comparative Study

TLDR: This article combines the machining parameters and the configuration parameters into energy efficiency models, for which machine-learning (ML) algorithms are used considering the lack of theoretical formulas, and provides a comprehensive survey on ML-based modeling in terms of data sizes, temporal granularities, feature selection, and algorithm performance.

Abstract: Energy efficiency modeling is of great importance to energy management and conservation for machinery enterprises. To improve the generalization ability, this article combines the machining parameters and the configuration parameters into energy efficiency models, for which machine-learning (ML) algorithms are used considering the lack of theoretical formulas. Based on the three-year data collected in a shop floor, a comparative study for two different cases is conducted with a particular focus on prediction accuracy, stability, and computational efficiency. In Case 1, only cross-sectional data are used to predict energy efficiency, ignoring the deterioration of spindle motors and cutting tools. Three traditional ML algorithms, i.e., artificial neural networks, support vector regression, and Gaussian process regression, are evaluated with the help of five error metrics. In Case 2, we construct the models in a more realistic situation that considers the dynamic aspects of spindle motor aging and tool wear. A convolutional neural network, a stacked autoencoder, a deep belief network and the aforementioned traditional ML algorithms are investigated. The comparison shows that all the models in Case 1 suffer from performance degradation, while deep learning achieves the long-term improvement in accuracy. Note to Practitioners —Energy efficiency models deliver many advantages, ranging from energy-aware machine design to process optimization. Although a large amount of works in the past focused on physics-based and experimental modeling for specific machining configurations, it can be more effective to improve the applicability of the modeling methods by involving the configuration variables into the models. Due to the uncertainties in both the machine and the operation environment, machine learning is adopted to fit the high-dimensional and high-nonlinear energy system. To the best of our knowledge, this is the first article that provides a comprehensive survey on ML-based modeling in terms of data sizes, temporal granularities, feature selection, and algorithm performance. Such a survey helps engineers quickly justify the appropriate ML methods to meet the actual requirements.