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M.-C. Chen, “Tolerance Synthesis by Neural Learning and Nonlinear Programming,” International Journal of Pro- duction Research, Vol. 70, No. 1, 2001, pp. 55-65.

has been cited by the following article:

• TITLE: Parametric Tolerance Analysis of Mechanical Assembly Using FEA and Cost Competent Tolerance Synthesis Using Neural Network

  AUTHORS: Govindarajalu Jayaprakash, Karuppan Sivakumar

  KEYWORDS: Compliant Assembly, Inertia Effects Quality, Loss Function, FEA

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.12, December 31, 2010

  ABSTRACT: Tolerance design plays an important role in the modern design process by introducing quality improvements and limiting manufacturing costs. Tolerance synthesis is a procedure that distributes assembly tolerances between components or distributes final part design tolerances between related tolerances. Traditional tolerance design assumes that all objects have rigid geometry, overlooking the role of inertia effects on flexible components of assembly. The variance is increasingly stacked up as components are assembled without considering deformation due to inertia effects. This study deals with the optimal tolerance design for an assembly simultaneously considering manufacturing cost, quality loss and deformation due to inertia effect. An application problem (motor assembly) is used to investigate the effectiveness and efficiency of the proposed methodology.