Enhanced piecewise least squares approach for diagnosis of ill-conditioned multistation assembly with compliant parts
Enhanced piecewise least squares approach for diagnosis of ill-conditioned multistation assembly with compliant parts
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DOI：
10.1177/0954405411423458
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
D. Ceglarek;D. Ceglarek;Prakash Prakash-Prakash;Prakash Prakash-Prakash;Prakash Prakash-Prakash
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文献类型：
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作者：
D. Ceglarek;D. Ceglarek;Prakash Prakash-Prakash;Prakash Prakash-Prakash;Prakash Prakash-Prakash
装配过程引起的尺寸变化对产品质量和功能有重大影响。现代产品的复杂性加上零件的合规性/灵活性经常导致装配系统状况不佳，进一步增加了过程控制和故障诊断的挑战。本文提出了一种基于增强型分段最小二乘法（EPLS）的方法，用于病态多工位装配系统的尺寸故障故障诊断。在这种方法中，从装配结构的逆刚度矩阵导出的预定故障模式和从产品测量数据获得的故障模式用于检测和隔离由夹具误差引起的尺寸故障。基于 EPLS 的诊断方法搜索一组称为潜在向量的组件，搜索受装配响应函数、装配系统的变异流分析 (SOVA) 模型的约束，该模型根据下线测量执行响应分解。所提出的方法的验证是基于具有合规零件的多站装配过程的基于梁的模型进行的，并且包括单个和多个故障场景的诊断。
Assembly process-induced dimensional variation has a significant impact on product quality and functionality. The complexity of modern products coupled with part compliance/flexibility frequently results in ill-conditioned assembly systems further adding to the challenges of process control and fault failure diagnosis. This paper proposes an enhanced piecewise least squares (EPLS)-based approach for dimensional fault failure diagnosis of ill-conditioned multistation assembly systems. In this approach, predetermined fault patterns derived from an inverse stiffness matrix of assembly structures and fault patterns obtained from product measurement data are used to detect and isolate dimensional failures caused by fixturing error(s). The EPLS-based diagnostic methodology searches for a set of components called latent vectors with thesearch constrained by assembly response function, the stream of variation analysis (SOVA) model of an assembly system, which performs decomposition of response based on the end-of-line measurements. The verification of the proposed methodology is conducted based on a beam-based model of a multistation assembly process with compliant parts and includes diagnosis of both single and multiple fault scenarios.