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.
The software that was written for this thesis has been developed in a modular structure. All of the functions are isolated whenever possible. For example the Boolean algebra representation is stored in one C++ class, while the Boolean algebra manipulation subroutines refers to that class. The sets that the equations refer to are stored in yet another class. This hierarchy continues up, and beyond the planning level.
All software was written in a portable style, and it can be compiled and run under UNIX and MS-DOS at the present time (Excluding the MetCAPP component that is only available on the UNIX platform). To accommodate the wide variety of platforms, the software utilizes dynamic memory allocation. For the test cases the memory usage grew anywhere from less than 100 Kilobytes to over 200 Kilobytes for the examples in this thesis. The base memory usage is about 200 Kilobytes for the executable programs. Computation time also varied between 1 to 30 seconds on both the UNIX and MS-DOS platforms.
When this software is actually used in an industrial setting it will probably require about 50 rules for a small job shop, and hundreds of rules for large corporations. At present the relationship is hard to evaluate, and will remain so until an industrial implementation is tried.
To date the entire planning process is automatic. In the best of cases this is ideal, but for industrial use, it would be advisable to allow human direction, and verification for all plan steps. This would require the addition of an extensive user interface that does not exist at the present time.
The design files were all created by hand. Eventually the designs should be obtained as the output from a CAD system. Even more useful would be a CAD and CAPP system that would share a common database, and not need to encode the design in ASCII format for transfer between software. This tight coupling would allow realtime interaction between CAD and CAPP. The same can also be said about an interface to Scheduling.
The rule files are constructed by hand, and would have to be produced by a skilled Knowledge Engineer. At present this will lead to difficulties in locating personnel capable of developing the rule set. Eventually other methods and techniques could be developed to simplify the collection of new rules.
The final product of BCAPP is a design file that has been expanded to include process information. This format has benefits for replanning, and also referring back to the original design after process planning. At present I have provided a simple utility to convert this file into a set of Operation Sheets, and provide some operation planning using MetCAPP. Eventually this should be replaced by specific planning modules for the specific manufacturing technologies.
By using Boolean equations, and sets to represent designs, it has been shown to be possible to generate process plans. The process plans have a number of features of interest,
multiple manufacturing domains are considered,
sequencing is somewhat inherent, although not required,
even without interpreting the product geometry, the system is able to suggest process plans.
The input form used by the planner does not contain unreasonable knowledge that would not normally be available from a CAD system. This information is processed by a non-linear, hierarchical process planner. The hierarchical representation of knowledge is based upon high-level examination of design equations for recognized equation forms and then subsequent application of more traditional production rules. The process planner can handle non-linear cases by backtracking from failed states. The example cases showed a number of successful applications to process planning situations. In the next section some of the outstanding issues will be discussed in terms of future work.