1.1 THE OBJECTIVES
It is obvious from the previous chapters that the BCAPP approach was successful in generating process plans. But this alone is not the only measure of fitness in the method. The objectives initially stated in the first chapter are an important guide in evaluating the success of the method. These will be addressed one at a time.
“Simplify the problem of recognizing features from design” - By using the Boolean equations and sets, I have sidestepped the problems that arise when defining geometry by surfaces, vertices, and points, but have maintained the power of feature based systems. In fact the definition of the Spring in one of the test cases illustrates that the design representation can encompass features also.
“Be able to recognize alternative production technologies to produce product features. This requires the planner be able to consider multiple planning domains” - The examples clearly illustrate the ease with which the system combines multiple planning domains. For example one product is manufactured using metal forming, machining, embossing, stock retrieval and assembly.
“Be able to produce alternative operations for each feature” - This is shown through examination of the process plans with multiple operations listed.
“Allow some degree of innovation in the process plan” - Innovation will naturally occur in these process plans. This is mostly because the system is not intelligent enough to ignore some uncommon approaches.
“Permit a structure that allows feedback of production problems to the process planner” - As the thesis has illustrated, process planning feedback can be used by the system, and if enhanced with a complete manufacturing database, this information could be used by the process planner immediately.
“To allow the computer to reduce the knowledge barrier between process planning and production” - By using manufacturing rules, to capture some production knowledge, it reduces the requirements on the process planner to immediately know all detailed cases that may occur in production.
“Minimize human effort and intervention when process planning” - This item has two aspects. At best the system will work unguided, and produce good process plans. At worst, the system will get stuck, but still provide the planner with partial process plans. Also, since all plans are generated from rules at present, this system is easily classified as generative, but in the future the system could also make use of a variant approach (also known as semi-generative).
“Be capable of accepting new manufacturing technologies without fundamental changes in the process planner” - The use of Boolean expressions for mathematical combinations allows easy incorporation of new technologies through the addition of new rules. This statement can be supported by the argument that since Boolean algebra is a rigorous form of mathematics, and it can generate all possible mappings of space, it can represent any physical product. Since the BCAPP rules are based on these methods, they can be formed to accommodate any physical configuration.
“Be able to optimize process plans” - The ability to generate alternates, as BCAPP does, allows the system to iteratively try to reduce the costs of manufacturing. This does not mean the plan will have a minimum cost, but the system can reduce the cost, with an increase in computational time.
“Handle all products” - As can be seen by the variety of examples, the method is capable of handling many possible products. But, at present there are still outstanding research questions about modelling some products with CSG models. For example the spring in the Large Clothes Pin has an unusual shape that is hard to model with CSG.