Research on Low-Carbon Intelligent Machining Path Planning Method for Lightweight Composite Materials of Aerospace Components toward Green Manufacturing
軽量複合材料航空宇宙部品のグリーン製造に向けた低炭素知的加工経路計画手法の研究 (AI 翻訳)
Siyi Wang
🤖 gxceed AI 要約
日本語
本論文は、航空宇宙用炭素繊維複合材料の加工における低炭素化を目的とし、遺伝的アルゴリズムを用いた知的経路計画手法を提案する。実際の加工データを用いた実験では、経路長、加工時間、エネルギー消費、CO2排出量をそれぞれ約5～15%削減し、表面粗さは変わらないことを確認した。
English
This paper proposes a low-carbon intelligent path planning method using genetic algorithms for machining carbon fiber reinforced polymer aerospace components. Experimental results show a reduction in path length, machining time, energy consumption, and CO2 emissions by approximately 5-15% while maintaining surface roughness.
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
日本の航空機産業や製造業では、GHG排出削減が急務であり、本手法は加工工程のScope 1/2排出削減に直接貢献する。SSBJやSBT対応の具体策として、中小企業でも導入しやすい遺伝的アルゴリズムを用いた点が実用的である。
In the global GX context
As global manufacturing faces pressure to decarbonize, this method offers a practical, data-driven approach to reducing operational emissions in machining processes. The use of genetic algorithms makes it scalable for various manufacturing contexts, aligning with ISSB and TCFD recommendations for operational efficiency.
👥 読者別の含意
🔬研究者:Demonstrates a successful integration of genetic algorithm optimization with carbon footprint quantification in machining, providing a replicable framework for further research.
🏢実務担当者:Provides a clear methodology and quantifiable results that can be directly applied to optimize machining paths for carbon reduction in composite part production.
📄 Abstract（原文）
The work offers a methodology for low-carbon intelligent path planning in machining operations of carbon fiber reinforced polymers used in aerospace parts. In this case, the object of research is a lightweight bracket made from a carbon composite material with an irregular profile, two different slots, and areas of variable thickness. A set of 30 samples of such brackets was developed using Siemens NX 2306 software. Machining data have been collected on a VMC 850 machine equipped with Yokogawa WT5000 energy meter with a period of data sampling of 0.1 seconds. Genetic algorithm path optimization was carried out in MATLAB R2024a software, whereas data processing and computation of carbon footprint values was done with Python 3.11. The comparison of optimized paths was done against reference trajectories provided by Siemens NX under equal conditions of tools, spindles, feeds, depth of cuts, and inspections. Optimization showed a path shorter by 286.4 ± 42.7 mm, travel distance by 214.8 ± 36.5 mm, machining duration by 18.6 ± 3.9 seconds, energy consumed by 0.184 ± 0.037 kWh, and carbon emissions by 0.105 ± 0.021 kg CO2, with surface roughness being unchanged at 1.42 ± 0.16 μm.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54254/2755-2721/2026.34321first seen 2026-06-11 05:24:26
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