When planning for a typical product it is common to consider the product in terms of components and reused features. (For a review of planning techniques in manufacturing refer to [Tsatsoulis and Kashyap, 1988]) Then each of these is separately considered for production methods, and finally details are planned. All of this can be illustrated as a hierarchy of tasks.

Figure 1.2 Hierarchical Decomposition of Planning Tasks

Dividing the tasks this way provides for an orderly division of macro tasks in the planning system. Within each of these tasks there are further subdivisions. The non-linear nature of planning may mean that some planning decisions must be undone. This is in itself an extensive subject [ElMaraghy, 1992], but some attempts have been made to address this in this thesis. For example, if operation details cannot be calculated in Operation Planning, then the failure of the rule can be reported to Process Planning for Parts. If there are alternate operations not already considered, then Process Planning can continue planning, or undo operations until a suitable alternative is found. If none can be found, then feedback of problems to the previous stage is expected.

The following sections will explain details of each of the planning modules.

1.4.1 Preparation for Planning

The basic form of the design may give clues about the structure of a product. One of these examples is the use of the Assemble operator. When this is used, it is obvious that the parts in the list are sub-assemblies, or basic parts. Another feature of interest is the presence of redundant features. These features are recognized by the system when redundant part names are used in the design. There are a number of cases not recognized by the system at this point. For example, if two parts are exactly identical, but have different names, they are not recognized as similar. A similar case exists if two parts are identical, but have different CSG representations. Eventually this can be solved by the addition of some geometric reasoning software, and a Same Object Detection algorithm.

The basic procedure for finding the hierarchy of subassemblies and features can be described using a sequence of operations.

1. Beginning with the main product, start examining the equation, and the other sets (and their equations). As the entire product definition tree is defined a list of terms, and subterms can be defined. A sample list is seen below, based upon the Big Clothes Pin in the examples chapter (Chapter 7). The derivation of this list is considered obvious, so in the interest of space the method is not included.


Figure 1.3 A Product Hierarchy


2. The list is reduced to terms that have assemblies as parents and/or have a quantity that is greater than 1. The result is a list of subassemblies, and features. A sample of this reduced list is shown below.


Figure 1.4 A Feature/Sub-Assembly Decomposition


3. The normal planning techniques now begin from the bottom of the list, and plan to the top.

As is described in the list above, a set of parts is identified with an algorithm. This step reduces the planning complexity, and identifies reused portions of a design.

1.4.2 Determination of Plans for an Individual Part

A simple planning method has been developed for finding process plans for individual parts. The rules were discussed earlier, but there are many approaches to applying them to the design. As described earlier, the method used here is a backtracking approach, using a hierarchical decomposition. The equation templates are matched, and then they call up production rules. Although this seems to be straight forward, there are a number of variations possible. The basic flow chart for the method for rule application is shown below (a higher level flow chart will be given later).


Figure 1.5 A Flow Chart Showing Low Level Rule Matching

This flow chart depicts the basic steps used for matching rules. In this case the routine starts with the equation for the part stored in equation form the equation refers to sets stored in a common area. Each of the templates are applied in turn until one matches. At this point the rules listed with the template are passed to the next stage. The list of rules are examined one at a time from top to bottom to check for condition matches. If a rule is fired it is applied, and the results are stored in other sets. After this the equation at the start of the routine is restored, and another rule is applied. If this fails a new template match is found. Eventually the system will have found a minimum number of alternatives, or run out of choices, When this minimum number of choices is found the routine quits so that selection of an optimal cost operation can be selected at a higher level.

The results from the planning method described are only for one plan step. It is of use for the reader to notice that there is one feedback in the case that the equation form does not yield any success. In this case a higher level routine can perform operations such as equation manipulation. The flowchart below describes a higher level planning routine. This routine is intended to plan for a complete part.


Figure 1.6 Macro Planning Level for Part

This routine will do some backtracking when a problem occurs, thus incorporating the non-linear planning techniques. The use of strategies to rearrange the equation also incorporates the concept of macro planning. The method of operation is that the routine will begin with the plan state to start planning for (This also allows replanning when outside failures occur). The first step involves planning for a single step. If enough alternatives are found, then the best alternative is found, and applied. Planning continues until the part is a void, indicating plan completion. In the event that too few alternatives are found for a single plan step the routine will attempt to work around it. First, the system will try to rearrange the equation. If this does not work, then the system will accept a limited number of alternatives. If no alternatives are found the system will backtrack to a previous operation, and rule out that alternative. This way, if a plan cannot continue, the planner will then look for viable alternatives. It is also possible that the planner cannot find a process plan. In this case there is simply an error report. This error report could be used in a system like that of Kimura [1989] that has design and CAPP tightly coupled for fast feedback of design features poorly designed for manufacture.