22-Mec-B4 Integrated Manufacturing Systems · May 2017

Question 5 of 6: Generative Planning, Machinability Data and the Benefits of CAPP

Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)

Notes on this paper

Paper format. National Exams, May 2017 — 16-Mec-B4 Integrated Manufacturing Systems. Three hours, open book, any non-communicating calculator permitted. Six questions are printed and any five constitute a complete paper; all questions are of equal value, so each is treated below as a 20-mark question. Every question is solved here, because the set is a study resource rather than a sitting.

Reference texts. M. P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, 5th ed. (process planning, CAPP, group technology, discrete control and programmable logic controllers); R. B. Chase and F. R. Jacobs, Operations and Supply Chain Management, 16th ed. (forecasting); S. Nahmias and T. L. Olsen, Production and Operations Analysis, 7th ed. (inventory models and lot sizing); C. E. Ebeling, An Introduction to Reliability and Maintainability Engineering, 3rd ed. (series and parallel reliability); S. Kalpakjian and S. R. Schmid, Manufacturing Engineering and Technology, 8th ed. (machinability data and cutting conditions).

Question 5: Generative Planning, Machinability Data and the Benefits of CAPP (20 marks)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

(a) A generative process planning system. A generative process planning system is one that creates a process plan for a new part automatically, from a description of the part and a body of formalised manufacturing knowledge, without retrieving or editing a plan stored for a similar part. Its inputs are a complete description of the part — geometry in the form of recognised machining features, material, dimensions, tolerances and surface finishes, together with the production quantity — and its outputs are the operation sequence, the machine assigned to each operation, the tools, fixtures and setups, the cutting conditions, the standard times and the resulting cost, issued as a routing sheet and operation instructions. Between the two sits the system proper, which has three parts: a part description scheme that the logic can reason over, usually a feature-based model or a group-technology code; a manufacturing database holding the shop's machine capabilities, tooling, materials and cutting data; and the decision logic that maps features to processes, typically expressed as decision trees, decision tables, or production rules in an expert system, together with algorithms that compute speeds, feeds, times and costs. The defining test is that if the knowledge base is complete for the domain, the system will produce a valid plan for a part it has never seen, which is precisely what a variant system cannot do.

(b) The objectives of a machinability data system. A machinability data system is the database and the calculation logic that supply recommended cutting conditions for every combination of work material, tool material, operation and machine in the plant. Its first objective is to give the planner or the NC programmer a defensible cutting speed, feed and depth of cut for each operation, so that the choice is made from recorded data rather than from an individual's habit. Its second objective is optimisation rather than mere adequacy: the data are used with a tool-life model, classically Taylor's equation $$V T^{\,n} = C ,$$ to select conditions that minimise cost per piece or maximise production rate, the two optima being different and both being computable once the tool-life constants, the tool cost and the labour and overhead rates are known. Its third objective is standardisation and retention of knowledge: cutting data held in a central file survive the retirement of the planner who developed them, and they make every routing for the same feature consistent. Its fourth is integration — the system exists to be queried automatically by the CAPP module and the NC post-processor, so that conditions flow into the process plan and the part program without transcription. Its fifth is time standards and cost estimating, because a machining time cannot be computed until the speed and feed are fixed, and scheduling and quoting both depend on those times. Finally, a good system closes the loop: actual tool lives and failures observed on the floor are fed back to refine the stored constants, so the data improve with use rather than decaying.

(c) The benefits of computer-aided process planning. The benefits reported for CAPP fall into four groups. The first is consistency and rationalisation: routings for similar parts are produced the same way regardless of who requests them, which removes the well-documented variation between planners and, because the standard routes are chosen deliberately, tends to move work onto the most suitable machines and reduce manufacturing cost. The second is productivity in the planning function itself — planning time and cost fall substantially, the dependence on scarce experienced planners is reduced, and plans can be revised quickly when a machine is added, retired or overloaded. The third is integration: CAPP is the bridge between design and manufacture, so its output feeds material requirements planning with routings and lead times, feeds cost estimating and quoting, feeds NC programming with tools and cutting conditions, and supports concurrent engineering by giving the designer early manufacturability feedback while changes are still cheap. The fourth is documentation and control: the routing sheets, operation instructions, tool lists and time standards are generated and reissued automatically, always current, and the plan is stored in a form that can be audited — which matters in a regulated or ISO 9001 environment where the process plan is a controlled document. To these should be added the ability to run "what-if" comparisons, evaluating alternative routes or batch sizes at almost no cost, which is impractical when every plan is written by hand.

Topic. Generative CAPP architecture, machinability databases, and the business case for computer-aided process planning.

Key relations. Generative CAPP = part description scheme + manufacturing database + decision logic. Taylor tool life VTⁿ = C, extended as VTⁿf^ad^b = C for feed and depth. Machining time per pass t_m = L/(fN) with N = 1000V/(πD) for turning; the cost-optimal cutting speed follows from differentiating cost per piece with respect to V once the tool-change time and tool cost are known.

Why this works. Process planning is the point at which a design becomes a cost, and most of a part's manufacturing cost is committed there rather than on the shop floor. Automating it therefore pays twice: once in the planner's time and once, much more substantially, in the quality of the decisions themselves. The machinability data system is what makes the automation credible, because a generated plan is only as good as the cutting conditions it assigns; without a maintained data file, a CAPP system generates plausible routings with arbitrary speeds and feeds and loses the cost benefit it was bought for.

Common pitfall. Answering part (b) with a list of cutting conditions rather than with objectives — the question asks what the system is for, so the answer must speak about standardisation, optimisation, integration, time standards and feedback. In part (c), a common weakness is to claim only that CAPP is faster; the integration and consistency benefits are worth more marks and are the ones that justify the investment.

Source. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, 5th ed., ch. 24 (Process Planning and Concurrent Engineering); Kalpakjian and Schmid, Manufacturing Engineering and Technology, 8th ed., ch. 22 and 40 on machinability, tool life and computer-integrated manufacturing.

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