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<p>Metal Additive Manufacturing (AM) is a rapidly growing technology that enables the production of parts with complex geometries. However, accurate cost estimation remains a critical challenge, preventing the industry from fully capitalizing on AM’s capabilities. While conventional cost models provide a solid analysis of the build process, none consider the full process chain in sufficient detail. This paper presents, for the first time, a new approach based on Discrete Event Simulation to develop a rigorous dynamic model for analyzing manufacturing costs in laser powder bed fusion (L-PBF) of metal components. The analysis shows that post-processing activities in AM significantly impact overall costs. Part geometry and production rate also influence key cost drivers. The model provides a flexible and adaptable tool for identifying and optimizing major cost and time drivers across the entire AM process chain, enabling comprehensive analysis for decision-makers in the manufacturing industry.</p>

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Activity-based costing of laser powder-bed additive manufacturing incorporating discrete event simulation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Büşra Karaş,
  • Alborz Shokrani

摘要

Metal Additive Manufacturing (AM) is a rapidly growing technology that enables the production of parts with complex geometries. However, accurate cost estimation remains a critical challenge, preventing the industry from fully capitalizing on AM’s capabilities. While conventional cost models provide a solid analysis of the build process, none consider the full process chain in sufficient detail. This paper presents, for the first time, a new approach based on Discrete Event Simulation to develop a rigorous dynamic model for analyzing manufacturing costs in laser powder bed fusion (L-PBF) of metal components. The analysis shows that post-processing activities in AM significantly impact overall costs. Part geometry and production rate also influence key cost drivers. The model provides a flexible and adaptable tool for identifying and optimizing major cost and time drivers across the entire AM process chain, enabling comprehensive analysis for decision-makers in the manufacturing industry.