Process Planning

Abstract

Process planning plays a major role in determining the cost of components and affects all factory activities; disappointingly, it is an art rather than a science. Process planning activities are predominantly labour intensive, depending on the experience, skill and intuition of the planner. Thus, it robs the manufacturing system of its natural flexibility.

This chapter advises two methods for managing this important task:

First, run seminars on how to improve process planning in which experience is transferred from one planner to the others. Include an understanding of the detail parameters of deciding upon a process plan. Use textbook data.

Second, redefine the process planner’s task. A process planner should build a roadmap of alternatives, and let each user generate the suitable routing at the time of need.

Appendix

1.1 Hyper—Rcapp Demo

The RCAPP concept divides the process planning task into three stages:

Stage 1.

Technology stage; generates TP—Theoretical Process . It is the “best” possible process from a technological standpoint. It does not violate any physical law. It is theoretical from a specific shop viewpoint. This stage is done by a process planner, or a computer program, as shown in Table 3.2

Table 3.2 Table of TP—Process Operation

Table 3.3 Universal Roadmap

Stage 2.

Transformation stage; constructing an Operation-Machine roadmap, as shown in Table 3.3. It lists all required operations, as generated by the time in Table 3.2 (TP). It considers the available resources and transforms machining time of each operation to consider the constraints of each specific machine, and builds the content of the roadmap (Ti, j—The time to perform operation I, on machine j). The transformation may be done following the operation transformation flow chart, as shown in Fig. 3.8

Fig. 3.8

Operation transformation flow chart

Assume that six resources are being considered. A short list of specifications of these resources is given in Table 3.4.

Table 3.4 Specifications of available resources

Using a simple computer program, or human process planner, to transfer in ­Table 3.5, the time roadmap can easily be converted to a cost roadmap. This is done by multiplying the time by the hourly rate. Thus, the value of Ti, j is converted to Ci, j, where Ci, j represents the cost of performing operation i on resource j. The converted time-to-cost values are shown in Table 3.6

Table 3.5 Resource—Operation time roadmap

Table 3.6 Resource—Operation cost roadmap

Stage 3.

Decision (mathematics) stage; Roadmap solution. Compute the path and sequence of operations that will result in the optimum process plan according to the criteria of optimization.

The roadmap format represents an almost infinite number of possible processes. For a roadmap of N  = 10 operations and M = 10 machines, the number of process combinations is

$$ \text{N}!{{\text{M}}^{\text{N}}}\,=\,\text{3}.\text{6288}*\text{1}{{0}^{\text{16}}}. $$

Finding the appropriate process becomes a mathematical problem and not a technological one. The definition of the mathematical problem is as follows:

Given: Operation (i)—Resource (j) roadmap listing all operations and the process value for each operation on each resource (Vi, j). A decision is required as to which resource to use, which operation(s) to perform on each resource, and what their sequence should be. The constraints are indicated by Priority —which ­indicate operations that must precede others and certain Relationships that must tie operations to be performed on the same resource. Extra expenses and time should be added to cover extra set up, chucking, and transfer of parts between resources, additional complication in capacity planning , job recording and inspection, etc. These extra expenses are called a “penalty” . Thus, the penalty for a batch is a function of the quantity to be produced. Naturally, in each case, the sequence of operations might be different.

The solution might be solved by the basic feature of dynamic programming which is that the optimum is reached stepwise, proceeding from one stage to the next. An optimum solution set is determined, given any conditions in the first stage. This optimum solution set from the first stage is then integrated with the second stage to obtain a new optimum solution, given any conditions. Then, in a sense ignoring the first and second stages as such, this new optimum solution is integrated into the third stage to obtain still further optimum solutions, and so on until the last stage. It is the optimum solution that is carried forward rather than the previous stage.

This dynamic programming procedure is shown in Fig. 3.9. At an intermittent point in the series of decisions, it was decided that job 3 is to be performed on resource 4 (see Fig. 3.9). At this stage, we can ignore how and why we reached this decision. The problem at hand is: to where should we precede from this point, that is, on which resource should job 2 be performed? Thus, this procedure is a finite problem, and can be solved easily and quickly. The number of combinations to be solved is

Fig. 3.9

Dynamic programming procedure

$$ \text{N}*\text{M}. $$

In the problem at hand, the stages are referred to as jobs (operations) and decisions are made by choosing the optimum path between any two jobs. However, since the sequence of operations listed in the roadmap is not fixed, this sequence can be changed. One of the problems to be solved is which sequence of operations will result in an optimum solution. Therefore, the general dynamic programming solution procedure has to be modified in order to handle the problem at hand.

The proposed solution is divided into two stages.

The first stage is from the bottom up, that is, from the last operation up to the first. It will proceed operation by operation, determining the optimum path (resource selection) for each operation independently of the previous operation. However, at each operation, a review of all previous optimum decisions is made in order to examine the effect of the sequence of operations. The sequence that results in a total path optimum is selected.

The second stage is from the top down, which is from the first operation down to the last. It reviews the optimum achieved by examining the effect of the sequence of operations from any operation up to the first operation. The sequence that results in a total path optimum will be used.