積層製造技術具高度客製化與實現複雜幾何的特性，應用快速發展如汽車、航太與生醫產業。選擇性雷射熔融金屬積層製造技術於磁性材料的製程試作，參數選擇流程相當繁複，一般須經由主觀地觀察及錯誤嘗試，導致效率不高且可重複性低，特別於磁性材料列印時，除了考慮材料結構強度外，還需要考慮成品的導磁特性，使得參數選用更加複雜。本研究導入機器學習於列印參數之選擇設定，採用資料導向(Data-driven)的方式，以減少錯誤嘗試的耗時，進而提高效率。本文主要在探討應用機器學習在磁性材料之積層製造時的參數選用，利用機器學習的演算法XGBoost，建立一套有效率、可重複性高的軟磁材料積層製造參數選用流程。

Selective laser melting (SLM) is one of the widely used metal additive manufacturing (AM) techniques. While SLM is able to produce high quality products, the parameter selection process can be very complicated, especially for magnetic materials. The mechanical properties need to be concerned as well as the magnetic characteristics, which makes the parameter selecting process more complicated. In this research, the parameter selection process of magnetic material for SLM will be explored. The process that integrates machine algorithm and evolutionary algorithm, comparing with the existing approach, is not only repeatable but timesaving.

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