雷射微孔軸加工的研究與應用

Abstract

隨著電子產品的薄型化發展趨勢，微孔板廣泛地應用在手機板以及高階的IC封裝載板。在微成孔的製程裡，雷射鑽孔由於可提供彈性、高速且精確的微孔加工，近年來已逐漸成為主流技術。 雷射鑽孔最常發生的缺陷就是縮孔現象，其形成的原因有很多，諸如雷射功率、脈波寬度、脈波頻率、脈波數目、輔助氣體壓力以及聚焦深度…等等。 由於機台的調校參數過於複雜而且彼此間又互相影響，不容易以一般的物理定律進行推導，本研究使用類神經網路將機台的複雜行為予以模式化並分別規劃雷射鑽孔、雷射切孔與雷射切軸三個實驗來探討類神經網路對於不同加工製程的應用問題，最後建立一套能夠計算製程參數最適值的數學系統，希望能夠藉此幫助製程相關人員縮短調機時間以及提升加工品質。

Modern electrical products display itself with the feature of not only versatile but also miniature. The request of the product creation force to develop more complicated integrated circuits and multiple layers Printed Circuit Board, PCB. Laser micro drilling technology can provide flexible, high speed, precise manufacturing process has become the most popular drilling technology, especially in small hole (< 1 mm diameter) drilling of electrical industry. But laser micro drilling technology also has some manufacturing problems such as hole tapering, barreling, surface debris and nonuniform hole diameter. These defects are the results of unsuitable drilling parameters. How to search for the correct operating parameters and then improving the qualities is the aim of the research. Generally, laser micro drilling process has about seven parameters to be properly adjusted, and the correlations between these parameters are nonlinear and complicated. It is difficult for a fresh engineer to be familiar with the operation in a short time. This paper reports an investigation of the quality control technique of laser micro drilling process. First, using the One-Factor-at-a-Time experiments, we can decide the levels of parameters. Second, adopting the Full Factorial experiment, we can get the training sets of neural network. Third, using training sets and neural network to find the mathematical model between parameters and machining qualities. Finally, by the calculation of neural network, we can find the optimal parameters of the laser micro drilling process. The result of the study can assist to generate the feasible parameters of laser micro drilling machine and improve the quality of the product.