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<p>As additive manufacturing (AM) makes it possible to realize highly intricate and customized designs, it has become essential to understand how increasing design complexity affects production processes, where production scheduling under varying levels of complexity emerges as a critical challenge in advanced manufacturing. In particular, lattice design complexity in AM can introduce trade-offs in build time and post-processing, necessitating a comprehensive evaluation of its impact on overall production performance and efficiency. To address these challenges, this study proposes a methodology for developing a novel Volumetric Design Complexity (VDC)-based dispatching rule to improve manufacturing efficiency in AM. In the initial step to establish the proposed methodology, fifteen Metal-Polymer Hybrid Structure (MPHS) samples with lattice structures are fabricated using the VDC metric as a basis. The resulting data are then used to train a Multi-Output Gaussian Process Regression model for predicting manufacturing outcomes, including lead time, cost, and energy consumption, for thirty additional VDC values and their corresponding production data. Subsequently, discrete-event simulations assess five dispatching rules across the combined forty-five orders processed through powder bed fusion and hotplate bonding to identify the best dispatching rule. The results show that the VDC-Average Matching (VAM) method achieves the shortest average lead time, the lowest total cost, and the minimal energy consumption. This study demonstrates that the proposed complexity-driven dispatching rules can improve production efficiency, reduce costs, and lower energy consumption in manufacturing MPHS.</p>

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Design Complexity-Driven Optimization of Production Efficiency and Scheduling for Metal-Polymer Hybrid Structures in Additive Manufacturing

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Jongsuk Lee,
  • Seong Je Park,
  • Joon Phil Choi,
  • Sangjun Jeon,
  • Jungyeon Kim,
  • Seung Ki Moon

摘要

As additive manufacturing (AM) makes it possible to realize highly intricate and customized designs, it has become essential to understand how increasing design complexity affects production processes, where production scheduling under varying levels of complexity emerges as a critical challenge in advanced manufacturing. In particular, lattice design complexity in AM can introduce trade-offs in build time and post-processing, necessitating a comprehensive evaluation of its impact on overall production performance and efficiency. To address these challenges, this study proposes a methodology for developing a novel Volumetric Design Complexity (VDC)-based dispatching rule to improve manufacturing efficiency in AM. In the initial step to establish the proposed methodology, fifteen Metal-Polymer Hybrid Structure (MPHS) samples with lattice structures are fabricated using the VDC metric as a basis. The resulting data are then used to train a Multi-Output Gaussian Process Regression model for predicting manufacturing outcomes, including lead time, cost, and energy consumption, for thirty additional VDC values and their corresponding production data. Subsequently, discrete-event simulations assess five dispatching rules across the combined forty-five orders processed through powder bed fusion and hotplate bonding to identify the best dispatching rule. The results show that the VDC-Average Matching (VAM) method achieves the shortest average lead time, the lowest total cost, and the minimal energy consumption. This study demonstrates that the proposed complexity-driven dispatching rules can improve production efficiency, reduce costs, and lower energy consumption in manufacturing MPHS.