GENERATIVE CAPP PDF

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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A typical CAPP frame-work is shown in figure While this improved productivity, dapp did not improve the quality of the planning of processes and it did not easily take into account the differences between parts in a family nor improvements in production processes. 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 Improved cost estimating procedures and fewer calculation errors More complete and detailed process plans Improved production scheduling tenerative capacity utilization Improved ability to introduce new manufacturing technology and rapidly update process plans to utilize the improved technology SUMMARY CAPP is a highly generatice technology for discrete manufacturers with a significant number of products and process steps.

CAD systems generate graphically oriented data and may go so far as graphically cappp metal, etc. While CAPP systems are moving more and more towards being generative, a pure generative system that can produce a complete process plan from part classification and other design data is a goal of the future. The geometry based coding scheme defines all geometric features for process related surfaces together with feature dimensions, locations, tolerances and the surface finish desired on the features.

Computer-Aided Process Planning

Prior to CAPP, manufacturers attempted to overcome the problems of manual process planning by basic classification of parts into families and developing somewhat standardized process plans for parts families Stage I. This is the function of CAPP. Process planning encompasses the activities and functions to prepare a detailed set of plans and instructions to produce a part.

Similarly, in generattive of machine breakdown on the shop floor, CAPP must generate the alternative actions so ggenerative 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 recognition techniques and artificial intelligence techniques such as expert system shells.

The system logic involved in establishing a variant process planning system is relatively straight forward — it is one of matching a fenerative with a pre-established process plan maintained in the system. It includes selection of processes, machine tools, jigs or fixtures, tools, inspection equipments and sequencing operations. Fabrication and assembly drawings to support manufacture as opposed to engineering drawings to define the part.

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For example, when one changes the design, it must be able to fall back on CAPP module to generate manufacturing process and cost estimates for generatvie design changes. Further,this graphically-oriented data gendrative then be provided to manufacturing in the form of hardcopy drawings or work instruction displays.

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. A further step in this stage is dynamic, generative CAPP which would consider plant and machine capacities, tooling availability, work center and equipment loads, and equipment status e.

Simple forms of generative planning systems may be driven by GT codes. The initial challenge is in developing the GT classification and coding structure for the part families and generatife manually developing a standard baseline process plan for each part cappp. Process plans which typically provide more detailed,step-by-step work instructions including dimensions related to individual operations, machining parameters, set-up instructions, and quality assurance checkpoints.

Since finite scheduling systems are still in their infancy, this additional dimension to production scheduling is still a long way off. At this stage, process planning decision rules are built into the system. The similiarities in design attributes and manufacturing methods are exploited for the purpose of formation of part families. Sometimes, the process plans are developed for parts representing a fmily of parts called ‘master parts’.

Ggenerative 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. A number of methods have been developed for part family formation using coding and classification genegative of group technology GTsimiliarity-coefficient based algorithms and mathematical programming models.

There are two major components of generative CAPP; a geometry based coding scheme and process knowledge in form of decision logic data. CAPP integrates and optimizes system performance into the inter-organizational flow. Retrieval and modification of standard process plan A number of variant process planning schemes have been developed and are in use. The planner will add the remaining ten percent of the cappp modifying or fine-tuning the process plan.

Process knowledge czpp form of in the form of decision logic and data matches the part geometry requirements with the manufacturing capabilities using knowledge base. Development of a standard process plan 4.

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These attributes allow the system to select a baseline process plan for the part family and accomplish about ninety percent of the planning work.

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 egnerative planners Increased productivity of process planners Higher level application progams such as cost and manufacturing lead time estimation and work standards can be interfaced.

Genrrative, variant CAPP is based on a Group Technology GT coding and classification approach to identify a larger number of part attributes or parameters. This type of system uses work instruction displays at factory workstations to display process plans graphically and guide employees through assembly step by step. 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.

The first step is the implementation of GT or FT classification and coding. Other capabilities of this stage are table-driven cost and standard estimating systems.

Module G:Computer Aided Process Planning

Manufacturers have been pursuing an evolutionary path to improve and computerize process planning in the following geneative stages:. Process planning translates design information into the process steps and instructions to efficiently and generatlve manufacture products. The first key to implementing a generative system is the development of decision rules appropriate for the items to be processed.

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.

Computer-Aided Process Planning

Rapid strides are being made to develop generative planning capabilities and incorporate CAPP into a computer-integrated manufacturing architecture.

CAPP is a caapp effective technology for discrete manufacturers with a significant number of products and process steps. The results of the planning are: 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 generativr raw material to finished state.