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An integrated restoration methodology based on adaptive failure feature identification
Robotics and Computer-Integrated Manufacturing ( IF 9.1 ) Pub Date : 2022-12-16 , DOI: 10.1016/j.rcim.2022.102512
Chuanpeng Hao , Yan He , Yufeng Li , Yulin Wang , Yan Wang , Wen Ma

Remanufacturing is an emerging eco-friendly industry because it consumes less energy, cost, and material to manufacture like-new parts with a warranty to match. However, restoration processes are ad-hoc and complex because the "raw" materials for remanufacturing are returned used parts, which exhibit significant uncertainties in failure features involving failure location, failure mode, failure volume, and failure degree. Thus, customized remanufacturing process planning (RPP) and restoration tool paths should be generated to restore the defects for each part. An integrated restoration methodology based on adaptive failure feature identification for remanufacturing is proposed to enable efficient and cost-effective remanufacturing. In this study, an adaptive failure feature identification algorithm is developed to identify the failure features on defective parts quickly. In this stage, the point clouds of the nominal model and defective model are used to extract defective regions through Boolean operations and then calculate the failure volume and degree. Based on the identified failure features, a knowledge reuse algorithm is proposed to retrieve the optimal RPP rapidly through mixed case-based reasoning (CBR) and rule-based reasoning (RBR). Finally, a tool path generation algorithm of hybrid Subtractive Manufacturing (SM) and Additive Manufacturing (AM) for the restoration of identified defects. The proposed methodology is verified by remanufacturing a defective blade with multi-defects and is approved to be flexible and effective.



中文翻译:

一种基于自适应故障特征识别的综合修复方法

再制造是一个新兴的环保行业,因为它消耗更少的能源、成本和材料来制造与新零件相匹配的保修。然而,修复过程是临时的和复杂的,因为用于再制造的“原材料”是退回的旧零件,这在失效位置、失效模式、失效体积和失效程度等失效特征方面表现出很大的不确定性。因此,应生成定制的再制造工艺规划 (RPP) 和修复工具路径,以修复每个零件的缺陷。提出了一种基于自适应故障特征识别的再制造集成修复方法,以实现高效且具有成本效益的再制造。在这项研究中,开发了一种自适应故障特征识别算法,以快速识别缺陷部件的故障特征。该阶段利用标称模型和缺陷模型的点云,通过布尔运算提取缺陷区域,计算失效体积和程度。基于已识别的故障特征,提出了一种知识重用算法,通过混合基于案例的推理(CBR)和基于规则的推理(RBR)快速检索最佳 RPP。最后,一种用于修复已识别缺陷的混合减材制造 (SM) 和增材制造 (AM) 的刀具路径生成算法。所提出的方法通过再制造具有多个缺陷的有缺陷的叶片得到验证,并被证明是灵活和有效的。利用标称模型和缺陷模型的点云,通过布尔运算提取缺陷区域,计算失效体积和程度。基于已识别的故障特征,提出了一种知识重用算法,通过混合基于案例的推理(CBR)和基于规则的推理(RBR)快速检索最佳 RPP。最后,一种用于修复已识别缺陷的混合减材制造 (SM) 和增材制造 (AM) 的刀具路径生成算法。所提出的方法通过再制造具有多个缺陷的有缺陷的叶片得到验证,并被证明是灵活和有效的。利用标称模型和缺陷模型的点云,通过布尔运算提取缺陷区域,计算失效体积和程度。基于已识别的故障特征,提出了一种知识重用算法,通过混合基于案例的推理(CBR)和基于规则的推理(RBR)快速检索最佳 RPP。最后,一种用于修复已识别缺陷的混合减材制造 (SM) 和增材制造 (AM) 的刀具路径生成算法。所提出的方法通过再制造具有多个缺陷的有缺陷的叶片得到验证,并被证明是灵活和有效的。基于已识别的故障特征,提出了一种知识重用算法,通过混合基于案例的推理(CBR)和基于规则的推理(RBR)快速检索最佳 RPP。最后,一种用于修复已识别缺陷的混合减材制造 (SM) 和增材制造 (AM) 的刀具路径生成算法。所提出的方法通过再制造具有多个缺陷的有缺陷的叶片得到验证,并被证明是灵活和有效的。基于已识别的故障特征,提出了一种知识重用算法,通过混合基于案例的推理(CBR)和基于规则的推理(RBR)快速检索最佳 RPP。最后,一种用于修复已识别缺陷的混合减材制造 (SM) 和增材制造 (AM) 的刀具路径生成算法。所提出的方法通过再制造具有多个缺陷的有缺陷的叶片得到验证,并被证明是灵活和有效的。

更新日期:2022-12-18
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