類神經網路應用於線切割放電加工厚度變化工件之研究

李其政(Chi-Cheng Lee)

指導教授 ： 廖運炫

國立臺灣大學/工學院/機械工程學研究所/碩士(2004年)

https://doi.org/10.6342/NTU.2004.00029

摘要

本文的目的在已知加工參數的情況下，能夠在加工厚度變化的同時找出穩定的進給速率作控制，並對於容易斷線的加工狀況作預防。文中利用類神經網路建構出進給速率與放電參數之間之簡易數學模式，以此放電模型參數簡化控制模組，利用已知的加工參數來推論出當時的進給速率，並配合控制放電休止時間來抑制斷線的可能。
實驗結果顯示，本文所採用的方法確實能夠針對機器找出放電模型參數，所控制的進給速率增量並非單純以間隙電壓差乘上一個增益值，而會針對當時的情況，不同的加工參數推論出不同的進給增量。觀察不正常放電比來控制放電休止時間，讓加工在趨近惡化的危險時能拉長放電休止時間來避免斷線的可能，等到不正常放電比回到安全範圍再逐漸降低放電休止時間。結合兩種不同目的的控制方式使系統更趨於穩定。

關鍵字

不正常放電比 ； 間隙電壓 ； 放電休止時間 ； 類神經網路

並列摘要

An algorithm to estimate the stable feed so as to control Wire-EDM process and prevent wire from breaking under varying workpiece height conditions is proposed and implemented. A neural network model related feed rate with workpiece height and relevant machining parameters in Wire-EDM process is established. With this model appropriate feed rate can be computed in accordance with the identified workpiece height and known discharge parameters. Pulse off time is increased immediately to prevent wire from breaking when the process becomes unstable and the rate of abnormal sparks is higher than the predetermined limit. It is reduced gradually once the rate of abnormal sparks is reduced to a safe value. Experiments have shown that satisfactory results are achieved.

並列關鍵字

artificial neural network ； pluse off time ； rate of abnormal sparks

參考文獻

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