Variation Source Diagnosis in Compliant Sheet Metal Assemblies Using Neural Networks

سال انتشار: 1384
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,446

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شناسه ملی سند علمی:

ICME07_205

تاریخ نمایه سازی: 6 آذر 1388

چکیده مقاله:

When a structure is made up of deformable sheet metal panels joined together by means of spot welds, rivets, adhesives or the like, it is commonly referred to as a compliant sheet metal assembly (CSMA). The dimensional quality of a CSMA is primarily determined by the variations of its constituent parts and fixture(s). While conventional variation analysis methods such as Worst Case, Root Sum Square and Monte Carlo may be able to simulate the contribution of rigid part variations to the dimensional variation of the final product, they definitely do not produce realistic results for the case of compliant sheet metal assemblies. In recent years, a number of CSMA-specific models have been proposed that take into account the deformability of the constituent parts. These models can fairly accurately predict the variations of the final assembly, given the variations of the individual parts and of the fixture(s). However, they are more of a prediction tool rather than a diagnosis tool which the designers really need. In other words, the designer often wishes to identify the source(s) of a particular variation in the final product (assembly) as a function of the variations of the components and the fixtures; whereas these models only predict the effect of a component-level variation in the assembly.

نویسندگان

M Shariat-Panahi

Assistant Professor Department ofMechanical Engineering,University of Tehran, Tehran, Iran

S.H Sadat

Graduate Students, Dept. of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

S Bagheri

Graduate Students, Dept. of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

M.H Mokhtari

Senior Design Engineer, Iran Khodro Company, Tehran, Iran

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