臺灣博碩士論文加值系統

為能夠應付科技產品的短生命週期、表面品質要求高、數量大等特點，工廠紛紛使用通用加工機配合智慧化的管理、設計及監控系統。
隨著科技發展，近年來間接式感測器愈來愈成熟且電腦的運算能力愈來愈快，硬體方面的阻礙漸漸地被消除了，因此製造智慧化領域有不少學者使用類神經網路預測表面粗糙度，藉由使用實驗資料訓練類神經網路，獲得切削系統的輸入及輸出之間的關係，訓練完成後，類神經網路可以有效預測工件表面品質，達到加工品質提升的智慧化。然而，眾多學者在得到工件品質預測能力後卻沒有將其進一步的利用，實為可惜。
本研究利用類神經網路並融合力信號及聲射信號建構出有效的表面粗糙度預測模型，並且對此預測系統最佳化，在可以接受的表面品質下，找到最大材料移除速率（MRR）的建議參數組合，經過實驗驗證，本系統的表面粗糙度預測最大誤差為11%，平均絕對誤差約為5.2%，每次運算約110秒，表現相當優異。若將本系統適當改良，則可以將本系統移植入PC-based的CNC控制器作為最佳化系統，使得切削加工的參數調整作業得以智慧化。

Technical products have characteristics such as short life cycle, high surface quality request and large quantity. Therefore, manufacturing factories gradually tend to use general types machine tool equipped with intelligent management, intelligent design and monitoring systems.
Recently, with technological development, there are more and more indirect sensors which are normally used, and computer efficiency is higher than the past. The obstacles of hardware are gradually removed. Accordingly, in the field of manufacturing, many researchers use artificial neural networks (ANNs) to predict the surface roughness. They use milling experiment data for training ANNs, and get the relationship between inputs and outputs. In most of cases, well trained ANNs can predict workpiece surface roughness effectively. The prediction system can achieve the intelligentization of rising process quality. However, when their systems got the prediction of workpiece surface quality, the data did not be further used.
This research project will investigate the use of ANNs with sensor fusion to construct an effective surface quality prediction system by making use of force and acoustic emission signals. The prediction system was then optimized under the constraint that the workpiece surface roughness must be lower than the requested surface roughness. The optimization system determined the best parameter combination by maximizing the material removal rate (MRR), and then relaying this information to the machine tool controller.
The optimization system experiments show that the maximum error of is about 11%, and the Mean absolute percentage error is about 5.2%, and each optimization operation takes around 110 sec. The performance of system is excellent. The optimization system can be modify and setup on pc-based CNC controller. Let manufacturing industry field can achieve the intelligentization of rising process efficiency.

致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1前言 1
1.2文獻回顧 4
1.4研究目的 11
1.5研究流程 12
第二章 相關理論基礎 13
2.1切削 13
2.2切削條件 14
2.3刀具磨耗 16
2.4切削監測 17
2.5信號分析、處理、萃取 21
2.6類神經網路（Artificial Neural Networks） 24
2.7最佳化（Optimization） 25
第三章 實驗設備、架構與流程 28
3.1實驗設備 28
3.2實驗架構 34
3.3實驗流程 36
第四章 建立材料移除速率最佳化系統 38
4.1切削預實驗-篩選實驗要素及擷取信號 38
4.2 信號萃取 42
4.3 建立預測模型 54
4.4最佳化方法 59
第五章 測試暨驗證材料移除速率最佳化系統 67
5.1 切削實驗-取得訓練資料 67
5.2 切削實驗-驗證最佳化系統可行性 73
第六章 結論及未來發展 75
6.1實驗結果總論 75
6.2研究效益及價值 77
6.3未來發展方向 77
參考文獻 80

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