Mass producing custom products requires an innovative type of manufacturing environment. Manufacturing environments at present do not possess the flexibility to generate mass produced custom products. Manufacturers’ rapid response in producing these custom products in relation to demand, yields several beneficial results from both a customer and financial perspective. Current reconfigurable manufacturing environments are yet neither financially feasible nor viable to implement. To provide a solution to the production of mass customised products, research can facilitate the development of a distinctive hybrid manufacturing cell, composed of characteristics inherent in existing manufacturing paradigms. Distinctive hybrid manufacturing cell research and development forms an environment where Computer Integrated Manufacturing (CIM) cells operate in a Reconfigurable Manufacturing environment. The development of this Hybrid Reconfigurable Computer Integrated Manufacturing (HRCIM) cell resulted in functionalities that enabled the production of mass customised products. Manufacturing characteristics of the HRCIM cell were composed of key Reconfigurable Manufacturing System (RMS) features and CIM capabilities. This project required hardware to be used in developing an integrated HRCIM cell. The cell consisted of storage systems, material handling equipment and processing stations. Specific material handling equipment was enhanced in its functionality by incorporating RMS characteristics to its existing structure. The hardware behaviour was coordinated from software. This facilitated the autonomous HRCIM cell behaviour which was derived from the mechatronic approach. The software composed of HRCIM events that were defined by its unique programming language. Highlighted software functionalities included prioritisation scheduling that resulted from customer order input. Performance data, extracted from each type of equipment, were used to parameterise a simulated HRCIM cell. During operation, the cell was frequently introduced to an irregular flow of different product geometries, which required different processing requirements. This irregularity represented mass customisation. The simulated HRCIM cell provided detailed manufacturing results. Significant results consisted of storage times, queueing times and cycle times. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2011.

Thesis (PhD)--University of South Australia, 1997

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Accessibility; CAM; CAD; Integration; Fixture Design. Includes bibliographical references (leaves 108-109).gh

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 102-105). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 102-105). Also available on the Internet.

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, 2005. / Includes bibliographical references.

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 4, 2007) Includes bibliographical references.

A hierarchical methodology is developed for the overall design of manufacturing systems. The methodology consists of solutions to four levels of problems, namely, (1) manufacturing system selection, (2) shop loading, (3) machine loading and tool allocation, and (4) testing the feasibility of a schedule and determining strategies for the operational control of the system. Although these problem levels are developed in a hierarchical sense, they can be applied independently by assuming appropriate inputs to the problem level under consideration. The third and the fourth level problems are addressed in this research for the flexible manufacturing system. The first level of the hierarchical methodology addresses the problem of manufacturing system selection. The mathematical 4 model formulated for this problem captures the basic and integrated relationships among the systems and system components. This model provides a practical approach and a precise tool to determine an optimal mix of systems, to assign appropriate machines to each system, and to select the best material handling system for each system to best suit long-term production requirements at minimum costs. The second level of the hierarchical methodology addresses the shop loading problem. A mathematical model is developed for partitioning parts among the manufacturing systems selected at the first level to minimize total operating costs. For the third level problem, a mathematical model is formulated to obtain routings of parts through an FMS and to assign appropriate cutting tools to each machine in the system to minimize total machining cost. For the fourth level problem, a simulation model is developed for testing the feasibility of the solution obtained at the third level. It also helps to determine strategies for the operational control of the system. The computational experience with the mathematical models is presented using the MPSX-MIP/370 package. Sensitivity analysis is also performed to further understand system behavior under various operating conditions. Several new findings of the research are reported. Because of the special structure of the mathematical models, a computational refinement for their solution is also proposed based on Lagrangian relaxation. / Ph. D.

Creating flexible and automated production facilities is a complex process that requires high levels of cooperation involving all mechatronics disciplines, where software tools being utilised have to work as closely as their users. Some of these tools are well-integrated but others can hardly exchange any data. This research aims to integrate the software systems applied by the mechatronic engineering disciplines to enable an enhanced design process characterised by a more parallel and iterative work flow. This thesis approaches systems integration from a data modelling point of view because it sees information transfer between heterogeneous data models as a key element of systems integration. A new approach has been developed which is called middle-in data modelling strategy since it is a combination of currently applied top-down and bottom-up approaches. It includes the separation of data into core design data which is modelled top-down and detailed design data modules which are modelled bottom-up. The effectiveness of the integration approach has been demonstrated in a case study undertaken for the mechatronic engineering design process of body shop production lines in the automotive industry. However, the application of the middle-in data modelling strategy is not limited to this use case: it can be used to enhance a variety of system integration tasks. The middle-in data modelling strategy is tested and evaluated in comparison with present top-down and bottom-up data modelling strategies on the basis of three test cases. These test cases simulated how the systems integration solutions based on the different data modelling strategies react to certain disturbances in the data exchange process as they would likely occur during industrial engineering design work. The result is that the top-down data modelling strategy is best in maintaining data integrity and consistency while the bottom-up strategy is most flexibly adaptable to further developments of systems integration solutions. The middle-in strategy combines the advantages of top-down and bottom-up approaches while their weaknesses and disadvantages are kept at a minimum. Hence, it enables the maintenance of data modelling consistency while being responsive to multidisciplinary requirements and adaptive during its step-by-step introduction into an industrial engineering process.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy