Comparison of Variant and Generative Process planning methods and their Computer Aided Process Planning Presented By- Pratik Patel. Approaches to Computer Aided Process Planning (CAPP). Variant Process Planning, Advantages and Disadvantages. Generative Process. The next stage of evolution is toward generative CAPP (Stage IV). At this stage, process planning decision rules are built into the system. These decision rules.

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Definition of coding scheme 2.

For example, if a primary work center for an operation s was overloaded, the generative planning process would evaluate work to be released involving that work center,alternate processes and the related routings. The system logic involved in establishing a variant process planning system is relatively straight forward — it is one of matching a code with a pre-established process plan maintained in the system.

When comapred with manual experience-based process planning, CAPP offers following advantages; Systematic developemnt of accurate and consistent process plans Reduction of cost and lead time of process planning Reduced skill requirements of process planners Increased productivity of process planners Higher level application progams such as cost and gensrative lead time estimation and work standards can be interfaced. genegative

Computer-Aided Process Planning

The assembly is shown on the screen and as a employee steps through the assembly process with a footswitch, the components to be inserted or assembled are shown on the CRT graphically along with text instructions and warnings for each step.

Tight acpp with a manufacturing resource planning system is needed to track shop floor status and load data and assess alternate routings vis-a-vis the schedule. Significant benefits can result from the implementation of CAPP.

The similiarities in design attributes and manufacturing methods are exploited for the purpose of formation of part families. Similarly, in case of machine breakdown on the shop floor, CAPP must generate the alternative actions so that most economical solution can be adopted in the given situation. The tools that are widely used in development of this database are flow-charts, decision tables, decision trees, iterative algorithms, concept of unit machined surfaces, pattern venerative techniques and artificial intelligence techniques such as expert system shells.

The first key to implementing a generative system is the development of decision rules appropriate for the items to be processed. A further step in this stage is dynamic, generative CAPP which would consider plant and machine capacities, tooling acpp, work center and equipment loads, and equipment status e. Process planning is very time-consuming and the results vary based on the person doing the planning. It includes selection of processes, machine tools, jigs or fixtures, tools, inspection equipments and sequencing operations.


The process plan developed with a CAPP system at Stage V would vary over time depending on the resources and workload in the factory.

Computer-Aided Process Planning

Grouping parts into part families 3. For example, when one changes the design, it must be able to fall back generatjve CAPP module to generate manufacturing process and cost estimates for these design changes.

The baseline process plans stored in the computer are manually entered using a super planner concept,that is, developing standardized plans based on the accumulated experience and knowledge of multiple planners and manufacturing engineers Stage III. The planner will add the remaining ten percent of the effort modifying or fine-tuning the process plan.

The decision rules would result in process plans that would reduce the overloading on the primary work center by using an alternate routing that would have the least cost impact. A typical CAPP frame-work is shown in figure Computer Aided Process Planning. CAPP integrates and optimizes system performance into the inter-organizational flow.

This suggests generxtive system in which design information is processed by the process planning system to generate manufacturing process details. Computer-aided process planning initially evolved as a means to electronically store a process plan once it was created, retrieve it, modify it for a new part and print the plan Stage II. This approach would involve a user responding to a series of questions about a part that in essence capture the same information as in a GT or FT code. A number of methods have been developed for part family formation using coding and classification schemes of group technology GTsimiliarity-coefficient based algorithms and mathematical programming models.

The nature of the parts will affect the complexity of the decision rules for generative planning and ultimately the degree of success in implementing the generative CAPP system. In the generative CAPP, process plans are generated by means of decision logic, formulas, technology algorithms and geometry based data to perform uniquely many processing decisions for converting part from raw material to finished state.

This type of purely generative system in Stage V will involve the use of artificial intelligence type capabilities to produce process plans as well as be fully integrated in a CIM environment. The majority of generative CAPP systems implemented to date have focused on process planning for fabrication of sheet tenerative parts and less complex machined parts.

Simple forms of generative planning systems may be driven by GT codes. In order to produce such things as NC instructions for CAM equipment, basic decisions regarding equipment to be used,tooling and cpap sequence need to be made.


There are two major components of generative CAPP; a geometry based coding scheme and process knowledge in form of decision logic data. In addition, there has been significant recent effort with generative process planning for assembly operations, including PCB assembly. Process knowledge in form of in the form of decision logic and data matches the part geometry requirements with generativf manufacturing capabilities using knowledge base.

Module G:Computer Aided Process Planning

Dynamic, generative CAPP also implies the need for online display of the process plan on a workorder oriented basis to insure that the appropriate process plan was provided to the floor. Since finite scheduling systems are still in their infancy, this additional dimension to production scheduling is still a long way off.

This routing becomes a major input to the manufacturing resource planning system to define operations for production activity control purposes and define required resources for capacity requirements planning purposes.

This system can be used to generate process plan for rotational, prismatic and sheet-metal parts. Manufacturers have been pursuing an evolutionary path to improve and computerize process planning in the following five stages:. The variant process planning approach can be realized as a four step process; 1. As the design process is supported by many computer-aided tools, computer-aided process planning CAPP has evolved to simplify and improve process planning and achieve more effective use of manufacturing resources.

The initial challenge is in developing the GT classification and coding structure for the part families and in manually developing a standard baseline process plan for each part family.

Other capabilities of this stage are table-driven cost and standard estimating systems. Fabrication and assembly drawings to support manufacture as opposed to engineering drawings to define the part.

Reduced process planning and production leadtime; faster response to engineering changes Greater process plan consistency; access to up-to-date information in a central database Caop cost estimating procedures and fewer calculation errors More complete and detailed process plans Improved production scheduling and capacity utilization Improved ability to introduce new manufacturing technology and rapidly update process plans generaitve utilize the improved technology SUMMARY CAPP is a highly effective technology for discrete manufacturers with a significant number of products and process steps.