Optimal robotic assembly sequence planning with tool integrated assembly interference matrix

IF 1.7 3区工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing Pub Date : 2023-01-18 DOI:10.1017/S0890060422000282

Chiranjibi Champatiray, M. R. Raju Bahubalendruni, Rabindra Mahapatra, Debasisha Mishra

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引用次数: 3

Abstract

Abstract Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.

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基于刀具集成装配干涉矩阵的机器人装配序列优化规划

制造业正在寻找高效的装配计划器，能够快速开发出实际可行的装配序列，同时保持成本和时间降到最低。大多数装配顺序规划依赖于虚拟环境中的零件关系。如今，工具和机器人抓取器完成了大部分的装配任务。忽视关键方面会使解决方案实际上不可行。此外，在使用机器人夹具和工具之前，测试定位和组装组件的可行性至关重要。本文提出了一种新的概念，即同时考虑零件和刀具的几何形状，提出了“刀具集成装配干涉矩阵”(TIAIMs)和“刀具集成轴向包围盒”(TIAABB)来生成实际可行的装配序列方案。此外，零件串联技术用于确定最佳装配顺序计划的实际机械部件。结果表明，该方法能够有效地处理实际工业问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing 工程技术-工程：制造

CiteScore

4.40

自引率

14.30%

发文量

审稿时长

>12 weeks

期刊介绍： The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.