Automatic identification of Sub Assemblies in an assembly.

1. Automatic
Identification of
Sub Assembly in
an Assembly
Ishan Kossambe

2. Content
• Introduction
• Literature review
• Methodology
• Implementation
• Conclusion

3. Introduction
• Assembly
• Need
• Objectives

4. Assembly
• Hierarchical process
• Information and complex relationships
• Consists of combination of sub assembly
• Better final assembly

5. Need
• Complex assembly = Number of subassembly
• Assembly planning
• Assembly cost
• Frequent changes in design
• Automatic Assembly planning

8. Objective
• Extracting information from computer database
• Building of liaisons
• Use of API of CAD Software
• Develop an algorithm

9. Literature Review
• Several papers have been published
• Most of the sub assembly detection techniques makes use of
mathematical models
• Automation of subassembly detection
• Reducing human interference

10. Zussman
• Object graph
• Kinematic Relationship
Dini and
Satochi
• Rules for generation of sequencing
Ong and
Wang
• Made use of connectivity and relationship
Zhang
• Mathematical models and computer program

11. Methodology
1. Generation of database
2. Development of Algorithm
3. Implementation

12. Generation of Database
• Mating relations in the assembly are required to decide about the sub-
assemblies
• Code is written which extracts the mating relations
• It is stored in the database

14. Algorithm
start
EXTRACT INFORMATION
STORE IN DATABASE
.
MATE
MATERI
AL TYPE
PART
EXTRACT THE MATE INFORMATION AND
NUMBER OF PARTS
A

15. A
DOES PART
i HAS A
MATE
WITH PART
j
SAVE THE PART
CONSIDER A
PART AND
COMPARE WITH
OTHER PARTS
DOES PART j
HAS A MATE
WITH THE i
UNSAVED
PART
COUNT
DON’T COUNT
B
B
COUNT=1
PART i AND SAVED j
PARTS IS NOT A SUB
ASSEMBLY
SUB ASSEMBLY
END
DON’T SAVE
REPEATTILLi≤NUMBEROFPARTS
REPEATTILLj≤NUMBEROFPARTSSAVED
NO
YES
YES
NO
YES
NO

16. Implementation
• Crane Hook Assembly
• Extraction of mating relations using AME Algorithm
• Execution of Sub-assembly detection algorithm

17. Crane Hook Assembly

18. Extraction Of Mating Relations

19. Conclusion
• Reduction in number of parts to be handled during assembly.
• Assembly cost and time.
• Automation.
• Feasible sub-assembly detection.

20. Future Scope
• Validating assemblies.
• Feasible sub-assembly detection.

21. References
• J. L. Nevins and D. E. Whitney, “Concurrent design of product and processes,” McGraw-Hill, New York, 1989.
• U. Rembold, C. Blume, and R. Dillmann, “Computer- integrated manufacturing technology and systems,” Mar-cel
Dekker, New York, 1985.
• S. S. F. Smith, “Using multiple genetic operators to re-duce premature convergence in genetic assembly plan-
ning,” Computers in Industry, Vol. 54, Iss. 1, pp. 35–49, May 2004.
• E. Zussman, E. Lenz and M. Shpitalni, “An approach to the automatic assembly planning problem”, Annals
CIRP, 39(1), pp. 33–36, 1990.
• Dini, G.; Santochi, M., “Automated sequencing and sub-assembly detection in assembly planning”, Annals of the
CIRP, Vol.41, 1992.
• Ong, N.S.; Wong, Y.C., “Automatic Sub-assembly detection from a product Model for disassembly sequence
generation”, International journal of Advanced Manufacturing technology, Vol.15, 1999, pp. 425-431.
• Y.Z. Zhang, J. Ni, Z.Q. Lin, X.M. Lai, “Automated sequencing and sub-assembly detection in automobile body
assembly planning”, Journal of Materials Processing Technology, 129 (2002) 490–494.
• Arun Tom Mathew; C. S. P. Rao, “A Novel Method of Using API to Generate Liaison Relationships from an
Assembly”, Journal of Software Engineering & Applications, 2010, 3: 167-175

22. Thank You