Integrating Taguchi and Desirability Function Analysis to Practice Low Carbon Fabrication With Recycled Filament
タグチ法とデザイラビリティ関数分析を統合したリサイクルフィラメントによる低炭素製造の実践 (AI 翻訳)
Cheng‐Jung Yang, Sin Syuan Wu
🤖 gxceed AI 要約
日本語
本研究は、リサイクルポリ乳酸(rPLA)を用いた3Dプリンティングの多目的最適化を検討し、層高と造形方向が精度、粗さ、炭素価格の主要因であることを示した。単一目的の精度最適化はコストを412.5%増加させるが、多目的シナリオ3ではコスト増を6.25%に抑えつつ粗さ46.03%低減、幾何誤差1.50%低減を達成した。低炭素製造の意思決定モデルに定量的基盤を提供する。
English
This study optimizes recycled PLA for 3D printing using Taguchi and desirability function analysis. It finds layer height and build orientation drive accuracy, roughness, and carbon price. Multi-objective optimization achieves 46.03% roughness reduction and 1.50% geometric error reduction with only 6.25% cost increase, providing a quantitative foundation for low-carbon additive manufacturing.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
台湾からの研究だが、日本の製造業がリサイクルフィラメントや低炭素3Dプリンティングを採用する際のコスト便益分析に示唆を与える。SSBJや開示枠組みとの直接的な関連は薄いが、炭素価格をプロセス最適化に組み込む手法はグリーン投資の意思決定に活用可能。
In the global GX context
This paper contributes globally by integrating carbon pricing into additive manufacturing optimization, showing how to balance cost and sustainability. It offers a replicable framework for firms aiming to reduce Scope 3 emissions under CSRD or SEC climate rules, though it lacks direct disclosure linkage.
👥 読者別の含意
🔬研究者:Provides a quantitative optimization framework for sustainable additive manufacturing with recycled materials.
🏢実務担当者:Offers a method to evaluate trade-offs between cost and carbon in 3D printing production decisions.
📄 Abstract（原文）
ABSTRACT This study presents an in‐depth investigation of the multi‐objective optimization of recycled polylactic acid (rPLA) for 3D printing via fused deposition modeling (FDM) with the aim of balancing industrial accuracy, surface quality, and environmental sustainability. Diverging from traditional literature that focuses on limited process parameters, this research applies a design of experiments (DoE) approach combining Taguchi and desirability function analysis (DFA) to evaluate 10 high‐dimensional parameters while incorporating carbon price as a critical optimization metric. The results indicate that layer height and build orientation are the primary drivers of the accuracy, roughness, and carbon price. Although single‐objective accuracy optimization triggers a 412.5% cost increase for marginal accuracy gains, multi‐objective Scenario 3 offers a holistic equilibrium, incurring a minor 6.25% cost increase to achieve reductions of 46.03% in roughness and 1.50% in absolute geometric error. This study addresses existing research gaps regarding geometric tolerance control, surface topography, and low‐carbon manufacturing decision models for rPLA, providing a quantitative foundation and an innovative pathway for a green transition in additive manufacturing.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/pola.70267first seen 2026-07-19 05:00:42
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