Predictive Modeling for Machining Power Based on Multi-source Transfer Learning in Metal Cutting

Abstract

Energy efficiency has become crucial in the metal cutting industry. Machining power has therefore become an important metric because it directly affects the energy consumed during the operation of a machine tool. Attempts to predict machining power using machine learning have relied on the training datasets processed from actual machining data to derive the numerical relationship between process parameters and machining power. However, real fields hardly provide training datasets because of the difficulties in data collection; consequently, traditional learning approaches are ineffective in such data-scarce or -absent environment. This paper proposes a transfer learning approach for the predictive modeling of machining power. The proposed approach creates machining power prediction models by transferring the knowledge acquired from prior machining to the target machining context where machining power data are absent. The proposed approach performs domain adaptation by adding workpiece material properties to the original feature space for accommodating different machining power patterns dependent on the types of workpiece materials. A case study demonstrates that the training datasets obtained from the fabrication of steel and aluminum materials can be successfully used to create the power-predictive models that anticipate machining power for titanium material.