22-Mec-B4 Integrated Manufacturing Systems · May 2015
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
Paper format. National Exams, May 2015 — 07-Mec-B4 Integrated Manufacturing Systems. Three hours, open book, any non-communicating calculator permitted. Seven questions are printed; any five constitute a complete paper and only the first five appearing in the answer book are marked, each of equal value (20 marks). All seven are solved here, because the complete set is the study resource. Questions 5, 6 and 7 are explicitly essay questions, in which the examiners award marks for clarity and organisation as well as content.
Reference texts. E. S. Buffa and R. K. Sarin, Modern Production / Operations Management, 8th ed. (requirements schedules, economic lot size, economic order interval, part-period balancing, production planning); R. B. Chase and F. R. Jacobs, Operations and Supply Chain Management, 16th ed. (demand components, adaptive forecasting, aggregate planning, statistical quality control); B. W. Niebel and A. Freivalds, Methods, Standards, and Work Design, 13th ed. (time study, performance rating, allowances, wage incentive plans); D. C. Montgomery, Introduction to Statistical Quality Control, 8th ed. (Shewhart charts, process capability); M. P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, 5th ed. (process planning, CAPP, machinability data systems, maintenance); S. Nahmias and T. L. Olsen, Production and Operations Analysis, 7th ed. (forecasting, aggregate planning).
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 generative process planning system is a computer-aided process planning system that synthesises the process plan for a new part from first principles each time it is asked, using a body of encoded manufacturing knowledge together with a description of the part — rather than retrieving and editing a plan that already exists. It contains no library of standard plans. Its inputs are a geometric and technological description of the part (increasingly taken directly from the CAD model through feature recognition, otherwise entered through a special part description language), a description of the plant’s available processes, machines, tooling and their capabilities, and a set of decision rules encoding process capability, tolerance relationships, machining practice and precedence constraints. From these it selects the machining operations, sequences them, chooses machines, tools, fixtures and datums, computes cutting conditions and times, and issues the routing sheet and operation sheets.
The definition is best fixed by contrast with the alternative. A variant system codes the new part with a group-technology classification, retrieves the standard plan already stored for that part family, and presents it to the planner for editing; it is fast, cheap to implement and entirely dependent on the quality of the existing plans and on a sound classification scheme, and it cannot plan a part that belongs to no established family. A generative system reasons instead of retrieving, so it can plan a genuinely new part, gives consistent output regardless of who runs it, and can be re-run automatically when the design changes or when a machine is added or retired. The price is that the knowledge base is difficult and expensive to build and to keep current; fully generative systems remain most successful in restricted domains — rotational parts, sheet-metal components, printed circuit assembly — and many commercial systems are semi-generative hybrids that retrieve a skeleton plan and generate the detail.
A machinability data system is the database and the selection logic that answer one question: for this workpiece material, this tool material, this operation and this machine, what cutting speed, feed and depth of cut should be used? It exists because that recommendation was traditionally carried in handbooks and in the heads of experienced planners and machinists, and neither source can serve an automated planning system. Its objectives are as follows.
The first is to supply optimal, not merely feasible, cutting conditions, computed against an explicit criterion — minimum cost per piece, maximum production rate, or maximum profit rate — using a tool-life relationship such as Taylor’s $vT^{n}=C$ together with the machining cost model. Handbook values are conservative by design; a data system can recover the margin they leave. The second is consistency and repeatability: the same part on the same machine receives the same conditions whoever plans it, which is the precondition for reliable standard times and costs. The third is accurate time and cost estimation, since the operation times that feed process planning, scheduling, capacity planning and quotation all derive from speeds and feeds. The fourth is to serve as the computational engine of CAPP and NC programming — a generative planner cannot select a cutting condition unless something can supply it, and an NC post-processor needs the same numbers. The fifth is tool-life and tool-inventory management: predicted tool life drives tool-change scheduling, tool magazine loading on machining centres and the tool crib stocking policy. The sixth is to act as a living repository that is updated from shop feedback and from tests, so that experience with a new workpiece material or a new coated insert becomes corporate knowledge rather than personal knowledge. Modern systems achieve this in two layers: a stored database of recommendations, and a mathematical layer that computes optimum conditions from tool-life constants when the exact combination is not stored.
Process planning is the bridge between design and manufacture, and it is the one link in the CIM chain that was manual longest. Computerising it yields benefits in four groups.
Consistency and quality of the plans. Manual planning is a matter of judgement, and three planners will produce three different routings for the same part; a computer-aided system applies the same logic every time, so the plan reflects the firm’s best practice rather than the habits of whoever was available. Rationalised, standardised plans reduce the number of setups and machines a part visits, which shortens lead time and reduces work in process. Productivity of the planning function. Reported reductions in planning effort of the order of 50 to 80 per cent are typical, which matters most where an experienced planning workforce is retiring and its knowledge would otherwise leave with it; the system captures that knowledge in the decision logic and the standard plans. It also shortens the response time on a quotation or an engineering-change request from days to minutes. Direct manufacturing cost. Better process selection and optimised cutting conditions cut machining time and tooling consumption; standardised routings reduce fixture and tool variety; and consistent plans give tighter, more defensible standard times, so the cost estimates and the quotations built on them are sounder. Integration. This is the benefit that justifies the investment in a CIM environment. A computer-based plan is machine-readable, so it can feed the routing and standard times to MRP and capacity requirements planning, supply the operation list to shop-floor control, generate NC programs and work instructions, and be re-run automatically when the design model changes. It closes the loop back to design as well: a system that cannot generate an economical plan for a proposed feature is giving the designer manufacturability feedback while the design can still be changed.
Against these must be set the real costs — building and maintaining the knowledge base or the family plans, the group-technology coding effort a variant system requires, and the discipline of keeping machine and tooling data current. A CAPP system that is allowed to fall out of date is worse than no system, because its output carries an authority it no longer deserves.
