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  • 學位論文

運用倒傳遞類神經網路於鑽孔表面粗糙度即時預測系統

The Development of an In-process BPN Surface Roughness Prediction System in Drilling Operations

指導教授 : 黃博滄
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摘要


隨著電腦數值控制(Computer Numerical Control, CNC)的誕生,與材料科技之進步,進而使全球各地的研發精密製造工程與技術不斷精進,且製造工業得以突飛猛進,現今已全面帶動產業邁向工業自動化;然而,CNC設備目前已廣泛運用於各項產業,而各產業多以降低成本、減少浪費以提高生產效率和利潤,來幫助企業達到其目標與願景並增進競爭力,此時在正確的時候做正確的事情以及減少浪費便成了各企業所追求的首要目標。為達成此一目標,品質管理為一不可或缺之方法,對於品質管理中之品質量測,其過程總是耗時費力,為了能減少品質量測之時間,近年來,即時品質量測系統的觀念及研究逐漸被應用與開發。 於CNC加工中,鑽孔加工為一個最基本且被普遍運用的加工方式,卻少有研究探討其品質量測,因目前業界上對鑽孔表面粗糙度的測量多數是採用離線量測及破壞式檢驗,此類檢驗會使成本及時間提高,如果在鑽孔作業時能精確掌握影響粗糙度之因子就能有效控制產品的鑽孔表面粗糙度,再加以發展出一套即時預測系統,可使檢驗之成本大幅下降同時也縮短檢驗之時間;為了能使即時全面品質量測(In-process 100% inspection)系統運用在鑽孔加工中,本研究的目的在於發展結合感測技術與預測系統的CNC鑽孔表面粗糙度即時預測系統。此系統加入機器之加工參數以及力量感測器的訊號作為輸入因子,並經由類神經網路的資料處理來訓練連結權重,並加以比較有無加入感測技術之預測精確度,最後再將上述的預測模型整合成一個精確度較高的鑽孔表面粗糙度即時預測系統。此系統經由不斷的學習與測試,即可達到即時全面品質量測之概念,進一步來協助企業減少時間與金錢之浪費,使其增進產業之競爭力。 研究結果顯示,相關影響因子經由倒傳遞類神經網路訓練後,證明從力量感測器所收集的鑽削力訊號能有效的預測鑽孔表面粗糙度,然後使用田口方法找尋網路參數最佳化的組合,以達到可從力量感測器所呈現的訊號做即時的回饋。

並列摘要


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.

參考文獻


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