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In this work, the process parameters optimization problems of abrasive waterjet machining process are solved using a recently proposed metaheuristic optimization algorithm named as Jaya algorithm and its posteriori version named as multi-objective Jaya (MO-Jaya) algorithm. The results of Jaya and MO-Jaya algorithms are compared with the results obtained by other well-known optimization algorithms such as simulated annealing, particle swam optimization, firefly algorithm, cuckoo search algorithm, blackhole algorithm and bio-geography based optimization. A hypervolume performance metric is used to compare the results of MO-Jaya algorithm with the results of non-dominated sorting genetic algorithm and non-dominated sorting teaching–learning-based optimization algorithm. The results of Jaya and MO-Jaya algorithms are found to be better as compared to the other optimization algorithms. In addition, a multi-objective decision making method named PROMETHEE method is applied in this work in order to select a particular solution out-of the multiple Pareto-optimal solutions provided by MO-Jaya algorithm which best suits the requirements of the process planer.

CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account.

The paper deals with determining metal material properties by the use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebel's expression was genetically developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically.

The expert system STATEXS is presented for dimensioning, optimization and manufacture of gears and gearings. The optimum dimensions of the gearing were determined using genetic algorithms, well suited to such problems especially because of their robustness and their ability to detect global extremes. After completion of the calculations and optimization of the gears or gear pairs, there follows one of the most difficult operations, the manufacture of the product with theoretically determined and optimized properties. To this end we have also started to use the genetic algorithm approach for the manufacture of various products with demanding shapes.