Vision-driven adaptive welding solutions for the top three challenges in welding fabrication

IF 2.4 4区 材料科学 Q2 METALLURGY & METALLURGICAL ENGINEERING
Mahyar Asadi, Ahmad Ashoori, Mehrnoosh Afshar, Ali Sheikhshab, Todd Scheerer, Austin Kaspardlov, Soroush Bagheri, Sina Firouz, Soroush Karimzadeh
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引用次数: 0

Abstract

With experience in more than over 100 robotic deployments in pipe prefabrication and a decade-long dedication to welding automation, we have pinpointed the key challenges, notably fit-up variation, tack adaptation, and live seam tracking. We engineered an innovative adaptive welding solution that integrates the perceptual and cognitive abilities of welders into articulated robots. This system dynamically responds to real-time welding scenarios, effectively tackling associated challenges. Unlike existing methods reliant on pre-scanning or laser readings before welding, our vision-based adaptive welding technology operates instantaneously, replicating the expertise of proficient human welders. The outcome is a consistent delivery of high-quality welds. Given the widespread advancement of AI, the heart of the adaptive welding system must skillfully manage diverse welding conditions, covering different joint preparations, types, positions, thicknesses, materials, and beyond. Addressing the necessity of training the AI core requires navigating through diverse practical challenges in deployments. Leveraging our expertise in deploying various methodologies, we ultimately provide an efficient solution for training the welding AI, primed for widespread deployment across high-mix low-volume applications. This solution incorporates a data tracing and monitoring platform across deployments, enhancing ERP (Enterprise Resource Planning) functionality, and providing insights into welding operations, historical performance analytics, and problem tracking with proactive improvements.

视觉驱动的自适应焊接解决方案,解决焊接制造中的三大挑战
凭借在管道预制领域100多个机器人部署的经验,以及长达十年的焊接自动化研究,我们已经确定了关键挑战,特别是装配变化、粘接适应和实时焊缝跟踪。我们设计了一种创新的自适应焊接解决方案,将焊工的感知和认知能力集成到铰接机器人中。该系统动态响应实时焊接场景,有效解决相关挑战。与现有焊接前依赖于预扫描或激光读数的方法不同,我们基于视觉的自适应焊接技术可以即时操作,复制熟练的人类焊工的专业知识。其结果是始终如一地交付高质量的焊缝。鉴于人工智能的广泛进步,自适应焊接系统的核心必须熟练地管理各种焊接条件,涵盖不同的接头准备、类型、位置、厚度、材料等。解决培训人工智能核心的必要性需要在部署中应对各种实际挑战。利用我们在部署各种方法方面的专业知识,我们最终为培训焊接人工智能提供了有效的解决方案,为高混合小批量应用的广泛部署做好了准备。该解决方案集成了跨部署的数据跟踪和监控平台,增强了ERP(企业资源规划)功能,并通过主动改进提供了对焊接操作、历史性能分析和问题跟踪的见解。
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来源期刊
Welding in the World
Welding in the World METALLURGY & METALLURGICAL ENGINEERING-
CiteScore
4.20
自引率
14.30%
发文量
181
审稿时长
6-12 weeks
期刊介绍: The journal Welding in the World publishes authoritative papers on every aspect of materials joining, including welding, brazing, soldering, cutting, thermal spraying and allied joining and fabrication techniques.
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