| Summary: | 碩士 === 國立成功大學 === 製造資訊與系統研究所 === 104 === Automatic Virtual Metrology (AVM) system has been successfully applied to many high-tech industries such as the semiconductor industry. It can convert sampling inspection with metrology delay into real-time and online total inspection. Currently, the precision machinery factories’ demands for enhancing the workpiece prediction accuracy are continuously increasing. The major challenge of applying the AVM system to the machine tool industry in order to achieve online and real-time total inspection is to adjust the AVM prediction and decision-making schemes from steady and mass production as in the semiconductor industry to small volume and semi-steady production in the precision machine tool industry.
This study proposes the Target-Value Adjustment Scheme (TVA Scheme) and Automated Sampling Decision Scheme (ASD Scheme). TVA can adjust the target values automatically to cope with the issue of applying the same model creation to various types of workpiece machining conditions, including different machining dimensions and tolerance ranges. ASD can dynamically adjust the sampling rates that AVM requires to reduce the measurement cost while still maintaining good prediction accuracy.
The actual machining case studies show that after applying TVA to the wheel machining automation, TVA can reduce the sample count required for model-refreshing in response to different machining conditions. TVA can also reduce the need of refreshing the same sample count as for model creation to only 2-3 samples, which are sufficient for maintaining good prediction accuracy.
As for ASD, under the stable mass-production environment, such as standard workpieces machining, it can reduce the sampling rate from 100% to 7.57% at best. The techniques described above are also applied to the aerospace industry in the prediction accuracy of the aircraft engine casing machining. TVA and ASD not only help to realize the goal of online and real-time total inspection, but also are verified to effectively reduce the measurement cost.
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