The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Design for quality manufacturability analysis of missing/misplaced parts and part interference
Author:	Tamboo, Altaf Yusuf
View Online:	njit-etd1995-113 (xi, 63 pages ~ 2.6 MB pdf)
Department:	Manufacturing Engineering Division
Degree:	Master of Science
Program:	Manufacturing Systems Engineering
Document Type:	Thesis
Advisory Committee:	Das, Sanchoy K. (Committee chair) Abdel-Malek, Layek (Committee member) Sodhi, R. S. (Committee member)
Date:	1995-01
Keywords:	Production engineering--Design. Manufacturing processes--Design. Manufacturing processes--Quality control. Quality control--Methodology. Design, Industrial.
Availability:	Unrestricted
Abstract:	Design for Quality Manufacturability (DFQM) is an approach that addresses the issue of quality manufacturability (QM) - the likelihood that defects will occur during manufacture of a product in a standard plant. The DFQM methodology is based on the premise that defects found in assembled products are often influenced by some features of the design and/or assembly process (influencing factors). These influencing factors cause defects in the presence of certain error catalysts. One of the influencing factors is geometrical features such as shape and symmetry. A classification scheme for part shape and symmetry is developed. This scheme is summarized in a chart, in which each block bears a unique alpha-numeric code representing a class of parts. The chart is used to identify a given part with respect to its class. In DFQM analysis, the alpha-numeric code suggests potential problems which the part is likely to experience during its assembly. Missing/Misplaced parts and Part Interference are two defect classes that are analyzed for QM. Error catalysts that promote the occurrence of these defects are identified and related to affecting factor variables using catalysis graphs. Each catalysis graph leads to a value between "0" and "1", based on the factor variables for the given design, implying the likelihood of occurrence of that specific defect. These values are normalized to obtain a QM score for the design. Higher the score, better the design from QM perspective.