| Summary: | 碩士 === 中原大學 === 工業與系統工程研究所 === 101 === With the invention of the Computer Numerical Control (CNC) and development of the material technology, the engineering of advanced manufacturing is greatly improved. These advantages accelerate the development of manufacturing industry, which becomes a stable foundation of the automation. However, CNC nowadays are widely used in different kinds of industries, which mainly focus on how to minimize the cost and maximize the production and profit. These strategies play important roles in reaching entrepreneur’s goals and visions. At this point, a right decision making at a right time and the reduction of waste are the main benchmarks of many companies. To achieve the benchmarks, quality management is the key factor. However, the inspection of quality control always takes time. To shorten the process time, the idea of “In-process Quality Monitoring System” has been applied and developed.
In CNC operations, drilling is one of the most basic and common operations. However, there are few researches studying the quality measurement of this part. Presently, the manufacturing industry conducts off-line inspection to examine the surface roughness of drilling. The off-line method needed a lot of time with high cost. The surface roughness can be effectively controlled if the influencing factors can be precisely acquired. With a new in-process prediction system, the inspection cost is reduced and so the time is shortened as well. To fit the “In-process 100% inspection” system in the drilling operations, the purpose of this research is to combine the Sensing Technology and the CNC in-process prediction system of surface roughness.
This system inputs the machining parameters and the signal from force sensor as the factors. A neural network is applied to construct the prediction model of the system. Then we compare the accuracy of the system with the other prediction system without sensing technology. With repetitive training and testing, the system can reaches the idea of total quality measurement which can assist the entrepreneurs to reduce the cost and shorten the lead time.
The result indicates that the related influencing factors under Back Propagation Network (BPN) training prove that the cutting signal from the force sensor can be used to effectively predict the surface roughness in drilling operations. This study uses Taguchi method to find the optimal set of the network variables for BPN training, which allows the operator to immediately response via the signal from the sensor.
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