Chapter Conclusions and Future Studies 7.1 Research Contributions This research uses integrative features to integrate process planning and fixture design. It considers fixture design in the integration of product design and manufacturing which has not been addressed in detail in previous research. Integrative features, which contain machining features, fixturing features, setup features and machine resource features, are used as the link of the integration. A hybrid fixturing-feature-based approach has been implemented in the product module to obtaining fixturing features together with machining features which are to be used in the process planning module to perform process planning. In the process planning module, an optimal set-up planning approach and a robust fixture layout approach have been addressed. Research contributions are described in detail in the following sections. 7.1.1 The Hybrid Fixturing-feature-based Approach The hybrid fixturing feature-based approach, inclusive of fixturing feature extraction, design-by-fixturing-feature and interactive fixturing feature definition, has been developed to extract and design fixturing features in the product module. The output 142 Chapter Conclusions and Future Works consists of machining features with rich fixturing feature information, which would be the ideal input for the downstream processes such as process planning and fixture design. The contributions of this research are: Automatic fixturing feature extraction considers the design specification, such as tolerance, datum together, thus it can capture the designer’s intent. This approach takes into account the dimensional tolerances and positioning accuracy of the workpiece in relation to datum faces and features. Works as the supplement of the feature extraction approach, the inclusion of the design-by-fixturing-feature approach and the interactive fixturing feature definition approach makes the modification of product design easy, thus ensuring each machining feature can be machined. Through this hybrid approach, the fixturability of a design can be ensured at the early product design stage. The ability to account for the effect of fixturing features upstream in the product and set-up planning stage can reduce the development cost and lead time to ensure significant improvements in process and product quality. This can also lead to significant reduction in the lead time for fixture design and fabrication and eliminate the trial-and-error testing procedures typically used by the tooling engineers. 7.1.2 Optimal Set-up Planning The optimal set-up planning approach developed in the process planning module considers both machine tools selection and tolerance analysis, and is able to achieve an optimal set-up planning result by incorporating a cost model. ACO is employed to 143 Chapter Conclusions and Future Works solve this NP-complete problem. Parts can be produced within designed tolerances and with the lowest cost in a particular production environment. The contributions of this research are: The design specifications and the machining environment are considered in an integrated manner, and a cost model is used to optimize the set-up planning process. Both dimension and position tolerance requirements are taken into account and a tolerance cost factor is introduced to consider the compression of operational tolerances. A distributed machining environment, i.e., machine tools located in different places, is considered. A variety of machine tools, e.g., 3- to 5-axis machine tools can be taken into account. Real-time machining environment, i.e., the unexpected events that may occur to machine resources, can be taken into account by updating the machine resources in real time. Except for higher tolerance cost consideration, the schedules of the machine tools and the transport cost are taken into account, making the cost model more realistic. 7.1.3 ACO is adopted to solve the NP-complete set-up planning problem. Robust Fixture Layout Genetic algorithm and ant colony optimization are adopted in this research work to solve the multi-objective fixture layout problem, i.e., to maximize the distance of the 144 Chapter Conclusions and Future Works location points, to minimize the localization errors and the deviation of the localization errors. The experimental results have shown that: The MOO-ACO and MOO-GA are effective approaches to solving the fixture layout problem, and the experimental results are satisfactory. While both approaches are effective, MOO-ACO is more attractive by comparing the Pareto set and the computational time. 7.2 Future Studies Suggestions for further studies are as follows: Include more types of fixturing features in the hybrid fixturing-feature-based approach. Consider more machining operations in process planning. Consider multiple components machined simultaneously in the manufacturing environment. For fixture layout, further studies will be focused on the areas related to the ACO algorithm. More fixture layout heuristics will be added to the MOO-ACO approach, improving the pheromone update process, studying the effects when multiple colonies and multiple pheromone matrixes are used. It is also desirable to compare the performance of this approach to other approaches, like particle swarm optimization. Include fixture configuration in the system. 145 . which are to be used in the process planning module to perform process planning. In the process planning module, an optimal set-up planning approach and a robust fixture layout approach have. Chapter 7 Conclusions and Future Studies 7. 1 Research Contributions This research uses integrative features to integrate process planning and fixture design. It considers fixture design. the product and set-up planning stage can reduce the development cost and lead time to ensure significant improvements in process and product quality. This can also lead to significant reduction