Application of Fuzzy Classification Methods for Diagnosis of Reject Root Causes in Manufacturing Environment Ford Motor Company

Format:

Conference/Event

Author/Creator:

Chen, Yubao, author.

Conference Name:

International Automotive Manufacturing Conference & Exposition (1998-05-12 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 1998

Summary:

This paper presents an approach of using neural network and fuzzy logic methods for the diagnosis of fault root causes in a manufacturing environment. As the first step in this approach, data from all the valid test points were collected and studied based on their statistical characteristics. An information-gain-based procedure was then followed to quantitatively evaluate the relevance of each test point to the diagnosis process. Accordingly, an objective rank of all relevant test points was generated for a particular reject. The root cause of rejects was then identified by a procedure based on an information-gain-weighted radial basis function neural network and a fuzzy multiple voting classification algorithm. This method has been tested with the top five rejects of the transmission main control component at Ford and promising results have been obtained

Notes:

Vendor supplied data

Publisher Number:

981334

Access Restriction:

Restricted for use by site license