三軸工具機之加工能耗預測與優化

Abstract

面對節能減碳的挑戰，工具機的低碳加工逐漸受到重視。由於機床的運行效率通常低於30％，因此在加工過程中能耗相當可觀，但同時也具有很大的節能和提高效率的潛力。因此，降低機床製造過程的能耗成為一個關注的焦點。在降低機床製造過程的能耗且提高加工效率的同時，必須兼顧加工品質、工具機穩定性和產能，以避免在實際加工中出現斷刀、主軸負載過大和機台顫動等問題，以實現淨零碳排放和綠色製造的目標。本研究提出了一種用於三軸工具機的加工能耗預測模型與優化方法，旨在實際加工前預測製造一批工件所需的能源消耗並優化以達到低碳加工。該方法利用CNC加工所使用的NC代碼作為輸入，透過本研究建立的參數提取方法，從這些NC代碼中逐行提取出所需的功率模型參數，預測工具機使用的功率並計算出切削過程時間，藉此預測加工的總能耗值。其中欲建立的功率模型包括待機功率模型、主軸空轉功率模型、進給軸功率模型、銑削材料移除功率模型和鑽孔材料移除功率模型。實際使用加工工件比較實際量測與預測的結果，本研究提出能耗模型預測準確度約為92.3%。接著，為找出最佳加工參數，首先透過功率模型建立能耗目標函數，並加入機台與刀具參數限制條件，限制函數搜尋的範圍。本研究使用全域搜索找出能耗目標函數在限制條件下的切削參數組合的最佳解，其切削參數包括主軸轉速、進給速率、切削寬度和切削深度。並進行優化比較未優化及優化結果，結果顯示透過目標函數優化的切削參數組合可以有效降低能耗約20%。

Facing the challenge of energy conservation and carbon reduction, low-carbon machining of machine tools is receiving increasing attention. As the operating efficiency of machine tools is usually below 30%, energy consumption during the machining process is considerable, but it also presents great potential for energy saving and efficiency improvement. Therefore, reducing energy consumption in the manufacturing process of machine tools has become a focus of attention. While improving machining efficiency, it is necessary to consider factors such as machining quality, machine tool stability, and production capacity to avoid issues like tool breakage, excessive spindle load, and machine tool vibration during actual machining, in order to achieve the goal of net-zero carbon emissions and green manufacturing. This study proposes a method for predicting and optimizing machining energy consumption, aiming to predict the energy consumption required for manufacturing a batch of workpieces before actual machining. The method utilizes the NC code used in CNC machining as input, which contains relevant information about the workpiece being machined. Through the developed parameter extraction module, the required power model parameters are extracted from these NC codes. These power models are established based on experimental data and include standby power, spindle idle power, feed axis power, milling material removal power, and drilling material removal power. With this method, it is possible to predict energy consumption during the machining process and perform energy consumption optimization. The energy consumption prediction successfully utilizes this method by taking the NC code as input and calculating the total energy consumption of the machining process based on the predicted power model and the extracted parameters, achieving a prediction accuracy of approximately 92.3%. Subsequently, an energy consumption objective function is constructed using the machine tool power model, incorporating constraint conditions to limit the range of function solutions. This study employs a global search to find the optimal combination of cutting parameters, including spindle speed, feed rate, cutting width, and cutting depth for the energy consumption objective function under the given constraints. A comparison is made between the results of the non-optimized and optimized parameters, demonstrating that the cutting parameter combination optimized through the objective function can effectively reduce energy consumption by approximately 20%.