隨著全球對可持續發展的重視日益提升，製造業作為能源消耗的主要產業之一，正面臨減碳與提升能源效率的雙重挑戰。降低製造過程中的能源消耗，不僅有助於企業提升競爭力，也成為因應環境壓力的重要對策。本研究不同於過往僅針對單道次加工或固定參數分析之研究，著重於多道次車削過程中工件負載隨尺寸變化而產生之能耗差異，並強調動態調整加工參數對節能的潛在貢獻。以CNC機台車削中碳鋼，探討不同加工參數對功率與能量消耗之影響，目標為降低能源使用。實驗採用L9直交表進行，將切削速度、進給率、切削深度與刀具磨耗做為控制因子，並將刀具磨耗更作為初始參數進行考量。根據實驗數據建立功率模型，擬合功率與各切削參數間之關係，利用粒子群最佳化演算法（Particle Swarm Optimization, PSO）對所得能耗進行最佳化，尋找最小能量消耗之參數組合。有別於過去研究於多道次加工中採固定參數組合之作法，本文依據每道次加工實際負載條件，設計不同參數組合，反映工件直徑逐步縮小對切削負載與能源需求的變化。在恆定切削速度下，隨工件直徑減少，主軸轉速上升，導致功率呈遞增趨勢。因此，應重視轉速變化對機台負載與能耗之影響，此外由L9直交表進行實驗設計所得之最佳參數組合分析結果可得，進給率與切削深度採用較高水準，因高進給與大切深可有效縮短加工時間，進而減少總能量消耗。儘管瞬時功率有所提升，但加工時間顯著縮短使得總能耗下降。根據本研究的方法，能源預估的百分比誤差皆在 1% 以內，顯示所建立之功率模型具備高度準確性。外徑車削中，Case3與Case1所得之最佳參數組合相比，能耗降低 2.53 %加工時間縮短 2.6 分鐘，與 Case2相比，能耗降低 1.23 %縮短加工時間 1.3 分鐘。輪廓車削中，Case3與Case1所得之最佳參數組合相比，能耗降低 4.33%加工時間縮短 4.8 分鐘，與 Case2相比，能耗降低 3.27 %縮短加工時間 0.9 分鐘。

The purpose of this study is to optimize energy consumption in CNC multi-pass turning by investigating the influence of workpiece diameter variation and applying dynamic cutting parameter adjustment. Unlike conventional studies that use fixed parameters, parameter combinations in this work were tailored for each pass based on actual load conditions, reflecting changes in spindle speed, cutting load, and energy demand as the diameter decreased. Experiments were carried out on medium carbon steel using an L9 orthogonal array, with cutting speed, feed rate, depth of cut, and tool wear as control factors. Spindle voltage and current signals were collected to establish a power model, which was then integrated with Particle Swarm Optimization (PSO) to determine the parameter set with minimum energy consumption.
Results indicate that using higher feed rates and depths of cut can significantly shorten machining time, thereby lowering total energy consumption despite an increase in instantaneous power. The energy estimation error of the developed power model was within 1%, confirming its high accuracy. In external turning, the dynamic adjustment strategy (Case 3) reduced energy usage by 1.23% and machining time by 1.3 minutes compared to fixed speed (Case 2), and by 2.53% and 2.6 minutes compared to L9 optimal parameters (Case 1). In profile turning, Case 3 reduced energy by 3.27% and time by 0.9 minutes compared to Case 2, and by 4.33% and 4.8 minutes compared to Case 1. These findings demonstrate that dynamic parameter control accounting for geometric variation can effectively enhance both energy efficiency and machining productivity.

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