Computer Aided Process Planning – Ant Colony

Computer Aided Process Planning Ant Colony

Introduction

One of the key issues emerging in the manufacturing scenario is the development of a computer-aided variant process planning. The cycle time, the material usage rate, the operation costs, costs of tooling etc have to be optimized in order to make the system effective. This article deals with optimization of above- mentioned parameters by reducing set-up time and costs. Various alternative process plans have been generated which is supported by a graphical user interface. By using a probability-based algorithm, an optimized or the best sequence is arrived.

Description

Time and cost is rapidly becoming the most strategic topic of the companies. The manufacturers, particularly in the aerospace industries have to make more customized products of high quality in smaller series with shorter lead-time and without increasing cost. Having an optimized process plan before entering into the manufacturing sector can acquaint the outline of strategic business.

Various metal cutting operations such as drilling, milling, turning and grindings are used to convert blank materials into finished products by removing stock from blanks.

Each of these processes is distinct in their own ways. The parameters, which distinguish these processes, are cost, operation time, accuracy, availability and reliability. The actual process to be used should be an optimum one, taking care of various factors as above. This paper deals with optimization of multivariate problems involving cycle time and cost. a computer aided dynamic variant process planning is to be created to draw the process chart of any component whose CAD representation is taken as input.

The formulation of an optimum sequence of operation is done using Ant Colony Optimization Technique.

Ant Colony Algorithm:

The selection of the optimum process is done by comparing the various probabilities of occurrence of possible sequences. These probabilities are determined using a relationship given by ant colony algorithm. It relates time with cost. The process having the highest probability is selected as the optimum sequence.

Conclusion

The optimum sequence is made using Ant Colony Optimization Techniques and this is interlinked with the computer aided dynamic process planning software. The selection of machines according to tolerance requirements machine- process capability ratio and other constraints as stated in the paper were developed. This can be made available in database (which in now under development), and it can be interfaced with DNC system so that an alternative process plan can be developed.

To visualize this optimized process plan we are using user graphics interfacing for which code has been developed in Java. It will give the step-by-step procedure of process, how the work piece is machined or drilled or operation flow in step by step as per depth of cut given from the database.

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