Loading...

Predicting fitting quality of mechanical assemblies through statistical-based process capability analysis

Movahhedy, M. R ; Sharif University of Technology | 2011

978 Viewed
  1. Type of Document: Article
  2. DOI: 10.4271/2011-01-0466
  3. Publisher: 2011
  4. Abstract:
  5. The process capability indices are widely used to measure the capability of the process to manufacture objects within the required tolerance. Fit quality is mainly dominated by the distribution of fit dimensions, i.e., a gap dimension. As the fit dimensions are very difficult to be measured in mass production, they are not to be considered as a direct inspection objective. The quality inspection and evaluation relative to fit quality are focused on whether the processes of assembly requirements are conformed with their specification limits respectively. Fit quality specification can be indicated by the process capability indices of mating parts. In this paper, the statistical-based process capability analysis method is presented to estimate ability of manufacturing process for considering of assembly requirements and fit quality in a mechanical assembly with asymmetric tolerances. According to this scheme, toleranced components are described as the statistical distributions of manufactured variables. In this paper a quantity factor to consider the contribution effects of variables that reduce the assembly process capability is introduced. The application of this method is demonstrated through an example and its results are discussed
  6. Keywords:
  7. Assembly process ; Asymmetric tolerance ; Fit quality ; Manufacturing process ; Mass production ; Mating parts ; Mechanical assembly ; Process capability analysis ; Process capability indices ; Quality inspection ; Specification limit ; Statistical distribution ; Fits and tolerances ; Industrial engineering ; Manufacture ; Process control ; Specifications ; Production engineering
  8. Source: SAE 2011 World Congress and Exhibition, 12 April 2011 through 12 April 2011 ; April , 2011
  9. URL: http://papers.sae.org/2011-01-0466