隨著市場需求不斷增長，製造業面臨著提升產能的壓力。由於人力資源有限，傳統手工易受到操作員技術差異的影響，導致加工誤差與品質不穩定，自動化機台成為解決人力不足和提升加工精度的方案。本研究設計一台自動化設備結合監控與數據採集系統(SCADA)，通過優化加工參數，以滿足大規模生產需求並改善加工品質。
本研究的自動化機台運用了該系統來實現數據收集與監控，並結合田口實驗法尋找目標數值，通過回歸分析進行精細調整。模擬實驗部分，利用影像辨識技術與霍夫圓變換進行凸輪形狀的識別與圓心定位，判斷加工件的外型是否合格。進一步應用了實驗數據來創建加工作業模擬，這裡提出一個程式自適應方案，計算每個範圍適合的參數，當中不需要人工參予。
研究結果的機台能夠通過參數調整，讓產量達標並將表面粗糙度控制在2.4 μm以下，實現95 %以上的合格率，提高了加工精度與生產效率。與傳統手工操作相比，操作時間減少了38 %。此外，模擬實驗提供具體參數數值，結果顯示，自適應轉速模式的波浪紋去除結果，皆在設定規範內，優於定轉速模式，將來可投入產線使用。

With the growing market demand, the manufacturing industry faces challenges in enhancing production capacity and precision. To address labor shortages and the quality instability of traditional manual processing, this study developed an automated system integrated with a SCADA framework. By leveraging data monitoring, the Taguchi method, and regression analysis, machining parameters were optimized. Simulated image recognition technology was utilized for profile determination, while simulated machining techniques were employed to achieve intelligent manufacturing processes. The study up to the present indicates that the current system achieves a qualification rate exceeding 95%, maintains surface roughness below 2.4 μm, meets daily production targets, and reduces operation time by 38%, effectively replacing manual labor.

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