利用深度神经网络提高飞机制造自动化过程的竞争力

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Leandro Ruiz, S. Díaz, Jose M. Gonzalez, Francisco Cavas-Martínez
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引用次数: 1

摘要

航空航天制造过程中的精度和可靠性要求是工业中最苛刻的。第一步是使用人工视觉模型进行检测和精确测量,以准确加工零件。然而,这些系统需要复杂的调整,并且在不受控制的情况下不能正常工作,而且需要人工监督,这降低了自动化机器的自主性。为了解决这些问题,本文提出了一种卷积神经网络,用于非受控工业制造环境中钻头和其他固定元件的检测和测量。此外,对从网络中得到的结果应用了一种微调算法,并定义了一个新的度量来评估检测质量。在实际生产环境中验证了该方法的有效性和鲁棒性,准确率为99.7%,召回率为97.6%,总质量因子为96.0%。作业者干预的减少率从13.3%降至0.6%。所提出的工作将使飞机部件制造过程的竞争力增加,工作环境将更加安全和高效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the competitiveness of aircraft manufacturing automated processes by a deep neural network
The accuracy and reliability requirements in aerospace manufacturing processes are some of the most demanding in industry. One of the first steps is detection and precise measurement using artificial vision models to accurately process the part. However, these systems require complex adjustments and do not work correctly in uncontrolled scenarios, but require manual supervision, which reduces the autonomy of automated machinery. To solve these problems, this paper proposes a convolutional neural network for the detection and measurement of drills and other fixation elements in an uncontrolled industrial manufacturing environment. In addition, a fine-tuning algorithm is applied to the results obtained from the network, and a new metric is defined to evaluate the quality of detection. The efficiency and robustness of the proposed method were verified in a real production environment, with 99.7% precision, 97.6% recall and an overall quality factor of 96.0%. The reduction in operator intervention went from 13.3% to 0.6%. The presented work will allow the competitiveness of aircraft component manufacturing processes to increase, and working environments will be safer and more efficient.
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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