機器學習輔助設計雷射積層參數製造超硬合金

Machine Learning Assisted Design to Optimize Laser Additive Manufacturing Parameters for Cemented Carbide

李芳怡 ； 莊睿晟 ； 葉安洲

《機械工業雜誌》 467期 (2022/02) Pp. 56-65

摘要

此文使用擇區雷射熔融（Selective Laser Melting，SLM）製造WC-Co超硬合金，擇區雷射熔融是一種積層製造（Additive Manufacturing, AM）技術，能克服傳統液相燒結超硬合金過程中，因模具的使用所造成之幾何限制，得以進一步製作複雜度高的組件。然而快速冷卻的製程容易導致裂縫生成等缺陷，使其生產具有挑戰性。本文透過機器學習演算法協助設計SLM參數以減少缺陷，並在緻密度（Densification）、硬度（Hardness）、韌性（Toughness） 和表面粗糙度（Surface Roughness）達到更好的綜合表現。本文章主要描述研究過程利用機器學習演算法種類中的隨機森林模型，輔助設計雷射積層參數，所建立之模型可以提供準確的預測能力及通用性，實驗驗證列印超硬合金，可以達到98.59 %的緻密度、1700 HV1的硬度、8.66 MPa√m的破壞韌性和0.1 mm的粗糙度，揭示機器學習為可被利用於優化3D列印參數之實用的工具。

關鍵字

WC-Co硬質合金 ； 隨機森林演算法 ； 擇區雷射熔融

並列摘要

This article describes how to utilized selective laser melting (SLM) to fabricate WC-Co cemented carbide. SLM is an additive manufacturing (AM) technique, which can be used to avoid traditional manufacturing constraints and offer freedom in complexity design for products. However, the fabrication of WC-Co cemented carbide by SLM is very challenging because of the material is brittle and is prone to form defects such as crack due to rapid cooling process. This article describes how to apply one of the machine learning methods-Random Forest Algorithm in design to optimize SLM parameters to reduce defects and achieve a better combination of properties including densification, hardness, toughness and surface roughness. In summary, random forest model is an effective tool in design to optimize SLM parameters, according to the experimental results, a balanced properties with 98.59 % in densification, 1700 HV1 hardness, 8.66 MPa√m fracture toughness and 0.1 mm roughness have been achieved. This article shows that the machine learning can be a practical tool to optimize process parameters for 3D printing.

並列關鍵字

WC-Co cemented carbide ； Random forest algorithm ； Selective laser melting

參考文獻

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