Simultaneous identification of group technology machine cells and part families using a multiple solution framework

1996 ◽  
Vol 9 (5) ◽  
pp. 402-416 ◽  
Author(s):  
Charles T. Mosier
1990 ◽  
Vol 28 (1) ◽  
pp. 145-152 ◽  
Author(s):  
G. SRINlVASAN ◽  
T. T. NARENDRAN ◽  
B. MAHADEVAN

2019 ◽  
Vol 18 (5) ◽  
pp. 991-1014
Author(s):  
Vennan Sibanda ◽  
Khumbulani Mpofu ◽  
John Trimble ◽  
Mufaro Kanganga

Purpose Reconfigurable machines tools (RMTs) are gaining momentum as the new solutions to customised products in the manufacturing world. The driving force, among others, behind these machines is the part envelope and the part family of products that they can produce. The purpose of this paper is to propose a new class of RMT known as a reconfigurable guillotine shear and bending press machine (RGS&BPM). A part family of products that this machine can produce is developed using hierarchical clustering methodologies. The development of these part families is guided by the relationship of the parts in the family in terms of complexity and geometry. Design/methodology/approach Part families cannot be developed in isolation, but that process has to incorporate the machine modules used in the reconfiguration process for producing the parts. Literature was reviewed, and group technology principles explored, to develop a concept that can be used to develop the part families. Matrices were manipulated to generate part families, and this resulted in the development of a dendrogram of six possible part families. A software with a graphic user interface for manipulation was also developed to help generate part families and machine modules. The developed concept will assist in the development of a machine by first developing the part family of products and machine modules required in the variable production process. Findings The developed concepts assist in the development of a machine by first developing the part family of products and machine modules required in the variable production process. The development of part families for the RGS&BPM is key to developing the machine work envelope and modules to carry out the work. This work has been presented to demonstrate the importance of machine development in conjunction with a part family of products that the machine will produce. The paper develops an approach to manufacturing where part families of products are developed prior to developing the machine. The families of products are then used to develop modules that enable the manufacture of the parts and subsequently the size of the machine. Research limitations/implications The research was limited to the development of part families for a new RGS&BPM, which is still under development. Practical implications The study reflects the development of reconfigurable machines as a solution to manufacturing challenges in terms of group technology approaches adopted in the design phase. It also highlights the significance of the concepts in the reconfigurable machine tool design. The part families define the machine work envelop and its reconfiguration capability. Social implications The success of the research will usher an alternative to smaller players in sheet metal work. It will contribute to the easy development of the machine that will bridge the high cost of machine tools. Originality/value The study contributes to the new approach in sheet metal manufacturing where dedicated machines may be substituted by a highly flexible reconfigurable machine that has a dual operation, making the investment for small to medium enterprises affordable. It also contributes to the body of knowledge in reconfigurable machine development and the framework for such activities, especially in developing countries.


CIRP Annals ◽  
1994 ◽  
Vol 43 (1) ◽  
pp. 425-428 ◽  
Author(s):  
K.K.B. Hon ◽  
H. Chi

2013 ◽  
Vol 717 ◽  
pp. 533-537
Author(s):  
Ki Seok Choi

In this paper, we propose a neural network-based algorithm for grouping machine and parts in cellular manufacturing. For grouping machines, we develop similarity coefficients which take into account both similarity and dissimilarity between machines. The machine cells are formed by an algorithm which is based on the maximum neural network. Another algorithm is used to find the part families associated with each machine cell. When compared with an existing algorithm, our algorithm shows better performance in terms of grouping efficiency and grouping efficacy.


1994 ◽  
Vol 5 (4) ◽  
pp. 225-234 ◽  
Author(s):  
M. Kamel ◽  
H. Ghenniwa ◽  
T. Liu

1973 ◽  
Vol 52 (3) ◽  
pp. 81 ◽  
Author(s):  
W.T. Whitfield
Keyword(s):  

1980 ◽  
Vol 59 (2) ◽  
pp. 51 ◽  
Author(s):  
Barry Dale ◽  
Philip Willey
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document