Setup Planning for Machining

In this chapter a brief introduction to process planning and setup planning in machining is described. Computer aided process planning/setup planning, its various approaches and main constraints are discussed. Some important concepts in relation to process planning and setup planning, viz. features and their tool approach directions, idea of datum, part dimensions and tolerances are presented. A brief discussion about flexibility in setup planning and data exchange formats is also included. Differences in setup planning for prismatic as well as rotational parts and setup planning for green machining are presented briefly.

In this chapter different phases of setup planning task are discussed in detail. Setup planning mainly comprises of feature grouping, setup formation, datum selection, machining operation sequencing, and setup sequencing. The main criteria for feature grouping and setup formation are tool approach direction and tolerance relation among the features. Datum selection primarily depends on area of a feature, its orientation, surface quality and its tolerance relations with other features. Machining operation sequencing and setup sequencing is done based on feature precedence relations.

Automatic setup planning has been an active area of research for a long time. Traditional approaches of setup planning have been using decision tree, decision table, group technology, algorithms and graphs. A number of experts systems also have been developed for setup planning. Expert systems follow forward chaining or backward chaining. The forward chaining supports data-driven reasoning; whilst the backward chaining supports goal-driven strategy. A number of soft computing tools have also been used in setup planning. Prominent among them are fuzzy set, neural networks and evolutionary optimization. Fuzzy set theory takes care of uncertainty and imprecision in the information. Artificial neural network can learn from the experience. Evolutionary optimization techniques help to provide optimum or near optimum solution. The goal may minimize the number of setups, manufacturing cost or resource consumption. The popular evolutionary optimization methods are genetic algorithms, particle swarm optimization and ant colony optimization. Nowadays cloud computing is also finding its application in setup planning.

In this chapter application of fuzzy set theory to setup planning is described. Fuzzy set theory can deal with the uncertainty and imprecision. At actual shop floor, one faces the problem of uncertainty, imprecision and subjectiveness. Fuzzy set theory can circumvent this problem to some extent. Two examples have been illustrated to show the efficacy of fuzzy set theory, one concerning with feature precedence relation and other with datum selection. A practical strategy for adaptively controlling the setup plan is also suggested.

Assignment of membership grades is an important task in the application of fuzzy set theory to setup planning area. There are various methods of assigning membership grades. Some of them are direct rating, polling, reverse rating, interval estimation, membership function exemplification and pair-wise comparison. There are various popular forms of membership functions like triangular, trapezoidal, Gaussian, Sigmoid, S-shaped and Π functions. The membership grades assigned by the expert can be fine tuned by the experts. In this chapter a method for fine tuning the membership grades assigned by experts is described. The method gives importance to the opinion of experts as well as relies on practical data. This method is applied to the estimation of burr size in the drilling process.

It is better to obtain a conceptual design of jigs and fixtures at setup planning stage itself. A brief review of different types of fixtures and relevant research is presented. Fixture design requires information about machining force. These can be estimated in an approximate manner using physics based or soft computing based approaches. Design of locators and clamps is based on the principles of contact mechanics. Considering uncertainty, fuzzy arithmetic can be used for the estimation of forces and design dimensions. The location of clamps and locators needs to be optimized for gaining better stiffness of the system. An illustrative example on end milling demonstrates how fixture related information can be obtained along with setup planning.