Abstract
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.
Highlights
In order to maintain competition, manufacturing enterprises have to cope with growing demands for increasing product quality, greater product variability, and less cost
We considered that the main cause of this coaxiality error was from the machine tool itself, we considered that the main cause this coaxiality was fromdegradation the machine and the preliminary judgement was that the error of was caused by theerror performance of tool the itself, and the preliminary judgement was that the error was caused by the performance degradation rotary work table
A novel root cause identification scheme to identify machining error based on the combination of the statistical process control and fault diagnosis of machine tools was proposed
Summary
In order to maintain competition, manufacturing enterprises have to cope with growing demands for increasing product quality, greater product variability, and less cost. Investigations into root cause identification of machining errors were primarily conducted by varying the source transmission in a multistage manufacturing process. Through this method, the geometric errors caused by fixture faults can be identified, but other error sources induced by machine tool degradation are not considered. Once the machining error occurs, the condition monitoring and fault diagnosis of machine tools are carried out to identify the root cause. 2. The Root Cause Identification Method Based on Statistical Process Control and Fault Diagnosis.
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