線切割放電加工聚晶鑽石之精修加工參數決策

Abstract

線切割放電加工聚晶鑽石（Polycrystalline Diamond，PCD）時，加工過程循環的熱效應會造成聚晶鑽石表面的熱損傷，在製作 PCD 刀具時會影響其切削刃的品質，進而影響刀具壽命。業界往往會預留加工裕量，在線放電粗割加工後對其進行研磨。若能在線切割機台上通過精修減少熱損傷層的深度，即能減小更換機台的定位誤差，降低研磨製程的時間並減少砂輪損耗所帶來的成本。故本研究關注 PCD 經過線放電加工後的熱損傷大小，以及通過多道次精修，能將熱損傷層減小至最少，並通過分析各項精修加工參數對 PCD 加工特性的影響提出一供加工者參考使用的決策方法。 研究運用田口直交表進行兩組實驗，分別是 L18 直交表的線放電粗割加工實驗和 L27 直交表的線放電精修加工實驗，探討了加工模式、伺服參考電壓等 8 個加工參數和Offset深度對於線放電加工 PCD 的熱損傷層和其它加工特性的影響。線放電粗割加工 PCD 的加工速度範圍落在 0.56 – 2.65 mm/ min ，平均熱損傷層深度大小範圍落在 20 - 60 µm 。精修加工實驗結果顯示，影響加工速度的主要因子為Offset深度、加工模式與開路電壓，三者貢獻度合計逾 83%；影響表面粗糙度的因子為Offset深度 、加工模式、開路電壓、放電時間，貢獻度達 75%；影響熱損傷層移除深度者，以Offset深度最顯著，其貢獻度達 90.97%。 基於上述分析，本研究建構一套自動化精修加工參數決策系統，使用者僅需輸入粗加工的參數設定與目標表面粗糙度，系統即能回傳最適道次組合與參數設定。系統內建三種策略模組（預設參數法、加工速度優先法與表面粗糙度優先法），支援單道至三道次的彈性組合，具備依目標導向調整的能力。在相同熱損傷層深度下，不同策略模組展現出明顯的加工結果差異與代價取捨：預設參數法運算快速、適用範圍廣，但缺乏針對性調整，常難以兼顧品質與效率；加工速度優先法可有效縮短總加工時間，惟表面粗糙度略為犧牲；表面粗糙度優先法則能顯著提升最終 Ra 表現，代價為加工時間略增。本系統可依使用者目標，於加工品質與效率之間提供清晰的策略選擇依據。

During the process of Wire Electrical Discharge Machining (WEDM) of Polycrystalline Diamond (PCD), the cyclical thermal effects can cause thermal damage to the surface of the PCD, which impacts the quality of the cutting edge when making PCD tools and consequently affects the tool life. The industry often leaves a machining allowance and performs grinding after rough cutting by WEDM. If the thickness of the thermally damaged layer can be reduced through fine-tuning on the wire cutting machine, the positioning error from changing machines can be minimized, grinding process time reduced, and the cost from wheel wear decreased. Therefore, this study focuses on the extent of thermal damage to PCD after WEDM and how multi-pass fine-tuning can minimize the thermally damaged layer. It also proposes a decision-making method for operators by analyzing the effects of various fine-tuning parameters on the characteristics of PCD machining. Two sets of Taguchi orthogonal experiments were conducted using L18 and L27 designs for roughing and finishing stages, respectively, to investigate the effects of eight machining parameters—such as machining mode, servo reference voltage, and finishing depth—on heat-affected depth and cutting speed. The observed roughing speeds ranged from 0.56 to 2.65 mm/min, with heat-affected layer thickness ranging from 20 to 60 µm. Experimental results for finishing revealed that the most influential factors on cutting speed were depth of cut (X), machining mode (IP), and open voltage (OV), collectively contributing over 83%. For surface roughness (Ra), X, IP, OV, and discharge time (ON) were dominant, with a combined contribution of 75%. As for the removal depth of the heat-affected layer (Re), depth of cut (X) had the most significant effect, with a contribution of 90.97%. Based on the above analysis, this study developed an automated decision-making system for fine machining parameters. By simply inputting the rough machining parameters and the target surface roughness, the system returns the optimal combination of passes and parameter settings. It incorporates three strategic modules—default parameter strategy, cutting speed–prioritized strategy, and surface roughness–prioritized strategy—with flexible support for one to three passes, and can be adjusted according to different optimization goals. Under the same HAZ depth, the three strategies exhibit clear differences in machining outcomes and trade-offs. The default strategy offers fast computation and wide applicability, but lacks parameter fine-tuning and often fails to balance quality and efficiency. The speed-prioritized strategy effectively shortens total machining time, at the cost of slightly poorer surface quality. In contrast, the surface roughness–prioritized strategy significantly improves the final Ra, though with a slight increase in machining time. The system provides users with a clear basis for selecting strategies according to their priorities between machining quality and efficiency.