A Prototype of Knowledge-Based System for Fault Diagnosis in Automatic Wire Bonding Machine

Chee-fai TAN

Article Details

A Prototype of Knowledge-Based System for Fault Diagnosis in Automatic Wire Bonding Machine

Authors:

Chee-fai TAN

Publisher:

TUBITAK

Journal:

Turkish Journal of Engineering and Environmental Sciences 2008 Vol.32 Issue 4, pp.235-244

Document Type:

Article

Publication Date:

3/2008

Document Language:

Turkish

Document ID (Org No.):

2000744837

Frequency:

Tri-annual

eISSN:

1303-6157

pISSN:

1300-0160

Abstract:

 

In the modern world, computing is essential in all aspects  of manufacturing activity.  Computers have brought to life terms like  artificial intelligence, and have played a critical role in  reinvention of manufacturing industry.  In continuing quest to  decrease the interval time between conceptualization of a product,  information technology has been fused with manufacturing practice.  This paper describes the use of an expert system shell to develop a  knowledge-based system (KBS) for an automatic wire bonding machine in  the hi-tech semiconductor industry.  The main aim of the KBS is to  diagnose and solve the problem of an automatic wire bond machine.  The  diagnosis method was based on maintenance requirements such as  measurement, machine, and material.  In the semiconductor industry,  production equipment and machine have depended heavily on the use of  human expertise for maintenance and it is costly.  Without a  knowledge-based system, their experience will be lost when they are  unavailable or resign.  With the developed KBS, the diagnosis process  for the automatic wire bond machine is standardized and accuracy will  be increased compared to the conventional way.  Therefore, the quality  of products that are produced will improve.  The constrain values for  KBS are based on the design data and experience of the engineer.  The  KBS is to improve bonding quality by reducing the production yield  loss.

 

In the modern world, computing is essential in all aspects  of manufacturing activity.  Computers have brought to life terms like  artificial intelligence, and have played a critical role in  reinvention of manufacturing industry.  In continuing quest to  decrease the interval time between conceptualization of a product,  information technology has been fused with manufacturing practice.  This paper describes the use of an expert system shell to develop a  knowledge-based system (KBS) for an automatic wire bonding machine in  the hi-tech semiconductor industry.  The main aim of the KBS is to  diagnose and solve the problem of an automatic wire bond machine.  The  diagnosis method was based on maintenance requirements such as  measurement, machine, and material.  In the semiconductor industry,  production equipment and machine have depended heavily on the use of  human expertise for maintenance and it is costly.  Without a  knowledge-based system, their experience will be lost when they are  unavailable or resign.  With the developed KBS, the diagnosis process  for the automatic wire bond machine is standardized and accuracy will  be increased compared to the conventional way.  Therefore, the quality  of products that are produced will improve.  The constrain values for  KBS are based on the design data and experience of the engineer.  The  KBS is to improve bonding quality by reducing the production yield  loss.

Article Details

A Prototype of Knowledge-Based System for Fault Diagnosis in Automatic Wire Bonding Machine

Authors: Chee-fai TAN

Publisher: TUBITAK

Journal: Turkish Journal of Engineering and Environmental Sciences 2008 Vol.32 Issue 4, pp.235-244

Document Type: Article

Publication Date: 3/2008

Document Language: Turkish

Document ID (Org No.): 2000744837

Frequency: Tri-annual

eISSN: 1303-6157

pISSN: 1300-0160

Issues

Authors:

Chee-fai TAN

Publisher:

TUBITAK

Journal:

Turkish Journal of Engineering and Environmental Sciences 2008 Vol.32 Issue 4, pp.235-244

Document Type:

Article

Publication Date:

3/2008

Document Language:

Turkish

Document ID (Org No.):

2000744837

Frequency:

Tri-annual

eISSN:

1303-6157

pISSN:

1300-0160