The essence of the machining process is the interaction that occurs between machine tools and a workpiece under certain conditions of cutting parameters. Root cause identification (RCI) is critical to the quality control and productivity improvement of machining processes. The geometric error caused by fixture faults can be identified in most RCI methods; however, the influence of machine tool degradation on workpiece quality is usually neglected. In this paper, a novel root cause identification scheme of machining error based on statistical process control and fault diagnosis of machine tools is proposed. With the pattern recognition of control charts, quality fluctuations can be detected in a timely manner. Once the machining error occurs, the fault diagnosis of machine tools are carried out. The relationship between machine tool condition and workpiece quality is established and the root cause identification of the machining error can be achieved. A case study of the machining of a complex welded box-type workpiece is presented to illustrate the feasibility of the proposed scheme. It is found that the coaxiality error of the two holes in two sides of the box’s wall is caused by the wear of the worm gear in the rotary work table of the machine tool.
(2017). Root Cause Identification of Machining Error Based on Statistical Process Control and Fault Diagnosis of Machine Tools. Machines, 5(3), 20. https://doi.org/10.3390/machines5030020