Soft sensor for in-line quality control of turning processes based on non-destructive testing techniques and advanced data fusion

Abstract This study describes the systematic process of training, testing, and validating a soft sensor designed for quality control of a turning process on components made of AISI 4140 steel. The soft sensor allows product quality to be predicted and unfavorable surface conditions to be identified, in particular the appearance of a phenomenon known as “White Layer”, often characterized in the case of AISI 4140 steel by an ultra-fine-grained microstructure (UFG). Basis of the soft sensor is a data fusion supported by non-destructive testing techniques (NDT), particularly micromagnetic methods (3MA). A critical part of this work is to address challenges such as lift-off compensation and in-process detection using 3MA. The application of machine-learning techniques, including Principal Component Analysis (PCA) and regression analysis, is detailed. These techniques result in robust models capable of detecting the occurrence of the White Layer phenomenon while minimizing the influence of measurement setup variations and process disturbances. In addition, the study demonstrates the integration of NDT into the machining process which drives the soft sensor and allows suitable adjustments of the process parameters. The data-driven soft sensor approach demonstrates a possible In-Line control system and discusses different control theories and their respective advantages and disadvantages. This system can effectively set targeted surface conditions in real time during the turning process.