Configuration Model for Automating Work System Design

Muhamad Arfauz A. Rahman, John P. T. Mo

Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Malaysia.
School of Aerospace Mechanical and Manufacturing Engineering, Bundoora, Australia.

DOI: 10.4236/ajibm.2012.24016                

Abstract

The design of an automation work system involves important choices concerning the type of system process as well as the condition of the process. These are based on the requirements from the user. This work provides the development of configuration model for automating the design of work system. The model consists of the extraction and execution process of user requirements. It begins with the identification of the requirement statement. Once identified, the statement is extracted and sorted accordingly into process type, number of item count and condition of the process. Currently, the model considers simple sorting and basic assembly process. The model continues to the selection of system action and system component. At the end of the work, the user requirements are transformed into a set of system model and eventually provide the desired system specification. At this stage, the model is represented in a symbolic flow process.

Keywords

Automation; Configuration; System Actions; System Components; User Requirements; Work System

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. P. T. Mo, P. Dawson and M. A. A. Rahman, “Active Learning Approach in Developing Engineering Design Skill through Open Ended System Specification,” 20th Australasian Association for Engineering Education Conference, Adelaide, 6-9 December 2009, pp. 61-66.
[2]	H. ElMaraghy, et al., “Managing Variations in Products, Processes and Manufacturing Systems,” CIRP Annals—Manufacturing Technology, Vol. 58, No. 1, 2009, pp. 441-446.
[3]	U. Karmarkar and S. Kekre, “Manufacturing Configuration, Capacity and Mix Decisions Considering Operational Costs,” Journal of Manufacturing Systems, Vol. 6, No. 4, 1987, pp. 315-324. doi:10.1016/0278-6125(87)90007-0
[4]	A. Matta, et al., “An Integrated Approach for the Configuration of Automated Manufacturing Systems,” Robotics and Computer-Integrated Manufacturing, Vol. 17, No. 1-2, 2001, pp. 19-26. doi:10.1016/S0736-5845(00)00033-8
[5]	R. P. Monfared and R. H. Weston, “A Method to Develop Semi-Generic Information Models of Change-Capable Cell Control Systems,” Computers in Industry, Vol. 41, No. 3, 2000, pp. 279-294. doi:10.1016/S0166-3615(99)00050-0
[6]	C. Stoian and G. Frumusanu, “Reconfigurable Manufacturing Systems Design Principles,” The Annals Dunarea de Jos of Galati, Fascicle V, Technolologies in Mecanical Engineering, Vol. 2007, No. 1, pp. 62-65.
[7]	H. P. Wiendahl, et al., “Changeable Manufacturing— Classification, Design and Operation,” CIRP Annals—Manufacturing Technology, Vol. 56, No. 2, 2007, pp. 783-809.
[8]	E. Travaini, et al., “Methodological Approach and Reconfiguration Tool for Assembly Systems,” CIRP Annals—Manufacturing Technology, Vol. 51, No. 1, 2002, pp. 9-13.
[9]	H. A. ElMaraghy, O. Kuzgunkaya and R. J. Urbanic, “Manufacturing Systems Configuration Complexity,” CIRP Annals—Manufacturing Technology, Vol. 54. No. 1, 2005, pp. 445-450.
[10]	W. Covanich and D. McFarlane, “Assessing Ease of Reconfiguration of Conventional and Holonic Manufacturing Systems: Approach and Case Study,” Engineering Applications of Artificial Intelligence, Vol. 22, No. 7, 2009, pp. 1015-1024. doi:10.1016/j.engappai.2009.01.001
[11]	A. Ferscha, et al., “Building Flexible Manufacturing Systems Based on Peer-Its,” EURASIP Journal on Embedded Systems, Vol. 2008, 2008. doi:10.1155/2008/267560
[12]	A. D. Toni and S. Tonchia, “Manufacturing Fexibility: A Literature Review,” International Journal of Production Research, Vol. 36, No. 6, 1998, pp. 1587-1617. doi:10.1080/002075498193183
[13]	M. A. A. Rahman and J. P. T. Mo, “Development of Theoretical Reconfiguration Structure for Manufacturing Automation Systems,” International Journal of Agile Systems and Management, Vol. 5, No. 2, 2012, pp. 132-150.
[14]	W. Lepuschitz, et al., “Toward Self-Reconfiguration of Manufacturing Systems Using Automation Agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 41, No. 1, 2011, pp. 52-69.
[15]	Y. Huang, et al., “Manufacturing Execution System Architecture Supporting Cross-Grain Reconfiguration,” Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, Vol. 17, No. 4, 2011, pp. 747-759.
[16]	N. Fuller, “The House of Quality,” Supply Management, Vol. 3, No. 3, 1998, p. 44.
[17]	J.-C., Jiang, M.-L., Shiu, M.-H. Tu, “Quality Function Deployment (QFD) Technology Designed for Contract Manufacturing,” TQM Journal, Vol. 19, No. 4, 2007, p. 291. doi:10.1108/09544780710756205
[18]	Z. Ocak, “Applying Quality Function Deployment in the Manufacturing Industry: A Review & Case Study in Production,” The Business Review, Vol. 9, No. 2, 2008, p. 4.
[19]	R. K. Singh, C. C. Elrod and E. A. Cudney, “Comparative Analysis of Quality Function Deployment Methodologies: A Case Study Analysis,” The Quality Management Journal, Vol. 19, No. 1, 2012, p. 7.
[20]	Y. Z. Mehrjerdi, “Applications and Extensions of Quality Function Deployment,” Assembly Automation, Vol. 30, No. 4, 2010, p. 388. doi:10.1108/01445151011075843
[21]	R. Mahanti, “The Application of Quality Function Deployment to User Interface Design,” The Quality Management Journal, Vol. 16, No. 1, 2009, p. 29.
[22]	C.-H. Liu, “A Group Decision-Making Method with Fuzzy Set Theory and Genetic Algorithms in Quality Function Deployment,” Quality and Quantity, Vol. 44, No. 6, 2010, p. 1175. doi:10.1007/s11135-009-9304-1
[23]	A. Olewnik and L. Kemper, “Limitations of the House of Quality to Provide Quantitative Design Information,” The International Journal of Quality & Reliability Management, Vol. 25, No. 2, 2008, p. 125. doi:10.1108/02656710810846916
[24]	J.-S. Shin, K.-J., Kim and M. J. Chandra, “Consistency Check of a House of Quality Chart,” The International Journal of Quality & Reliability Management, Vol. 19, No. 4, 2002, p. 471. doi:10.1108/02656710210421535
[25]	S. Ratchev, H. Hirani and M. Bonney, “Knowledge Based Formation of Re-Configurable Assembly Cells,” Journal of Intelligent Manufacturing, Vol. 18, No. 3, 2007, pp. 401-409. doi:10.1007/s10845-007-0031-y
[26]	H. F. Manesh, D. Schaefer and M. Hashemipour, “Information Requirements Analysis for Holonic Manufacturing Systems in a Virtual Environment,” The International Journal of Advanced Manufacturing Technology, Vol. 53, No. 1-4, 2011, pp. 385-398. doi:10.1007/s00170-010-2822-0
[27]	T. K. Yan, “Genetic Algorithms, Function Optimization, and Facility Layout Design,” European Journal of Operational Research, Vol. 63, No. 2, 1992, pp. 322-346. doi:10.1016/0377-2217(92)90034-7
[28]	B. A. Peters and M. Rajasekharan, “A Genetic Algorithm for Determining Facility Design and Configuration of Single-Stage Flexible Electronic Assembly Systems,” Journal of Manufacturing Systems, Vol. 15, No. 5, 1996, pp. 316-324. doi:10.1016/0278-6125(96)84194-X
[29]	S. L. Robin, “Automated Facilities Layout: Past, Present and Future,” Automation in Construction, Vol. 9, No. 2, 2000, pp. 197-215. doi:10.1016/S0926-5805(99)00005-9
[30]	S. L. Robin, “Automated Facilities Layout: Past, Present and Future,” Automation in Construction. Vol. 9, No. 2, 2000, pp. 197-215.