鑽削參數自主優化與智慧調變系統

A Parameter Optimization and Intelligent Control System for Drilling Process

蔡淳宇 ； 馬勝銘 ； 林豈臣 ； 戴仲裕 ； 彭達仁

《機械工業雜誌》 444期 (2020/03) Pp. 62-69

摘要

近年來於國內外各大工具機展中，皆可看到各大工具機廠發展顫振抑制與參數優化系統。不過當前的製程優化技術多是注重於銑削製程，較少應用於鑽削製程。然而在金屬零件加工製程中，進行深孔加工時經常會遇到鑽削顫振問題，本文將提出一套針對鑽削製程的參數優化與智慧調變系統，透過即時加工振動監控及加工異常振動警示功能，讓加工者能快速了解製程狀態，並在異常振動發生時保護機台。再透過鑽削製程參數優化快速找出加工甜蜜點，維持製程穩定。最後透過參數智慧調變系統，依照加工座標變化自主調變參數，降低因工件動剛性變異造成的振動問題，讓加工者不須因加工座標改變便須停機重新找參數，提升製程效率。

關鍵字

深孔加工 ； 參數優化 ； 機器學習

並列摘要

In recent years, major machine tool manufacturers have developed chatter suppression and parameter optimization systems, which have been disclosed were displayed in domestic and foreign machine tool exhibitions. However, the current process optimization technology is mostly focused on milling process, and is rarely used in drilling process. However, in the cutting process, drilling chatter is often encountered during deep hole processing. This article presents a set of parameter optimization and smart modulation systems for drilling process. Functions of processing vibration monitoring and abnormal vibration warning will allow users aware of the process status and protect the machine when abnormal vibration occurs. Through optimization of drilling process parameters, one can quickly find the ＂sweet spot＂ to machining process to maintain stability of the process. Finally, through intelligent parameter adjustment system, the parameters can be automatically adjusted according to the changing of processing coordinate conditions. The vibration problem that caused by dynamic stiffness of the workpiece will be reduced, and the user needs not to reset and re-find the parameters due to processing coordinate changes, and hence improves the efficiency of machining process.

並列關鍵字

Deep hole drilling ； Parameter optimization ； Machine learning

參考文獻

Altintas, Y.(2012).Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, And CNC Design.Cambridge University Press.

蔡淳宇,馬勝銘,吳偉任,戴仲裕,彭達仁(2019).切削參數自主優化系統及其加工應用.機械工業雜誌.440,38-45.

Zuperl, U.,Cus, F.,Mursec, B.,Ploj, T.(2004).A hybrid analytical-neural network approach to the determination of optimal cutting conditions.Journal of Materials Processing Technology.157-158,82-90.

Munoa, J.,Beudaert, X.,Dombovari, Z.,Altintas, Y.,Budak, E.,Brecher, C.,Stepan, G.(2016).Chatter suppression techniques in metal cutting.CIRP Annals.(CIRP Annals).

Uekita, Masahiro,Takaya, Yasuhiro(2017).Tool condition monitoring technique for deep-hole drilling of largecomponents based on chatter identification in time–frequency domain.Measurement.103,199-207.