Joze Balic

Optimization of cutting process by GA approach

Abstract The paper proposes a new optimization technique based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA. It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.

Intelligent programming of CNC turning operations using genetic algorithm

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.A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16%. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced.

A genetic-based approach to simulation of self-organizing assembly

Abstract The paper proposes a new and innovative biologically oriented idea in conceiving intelligent systems in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the production environment and the environment in a wider sense. Simulation of self-organizing assembly of mechanical parts (basic components) into the product is presented as an example of the intelligent system. The genetic programming method is used. The genetic-based assembly takes place on the basis of the genetic content in the basic components and the influence of the environment. The evolution of solutions happens in a distributed way, nondeterministically, bottom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program intelligent manufacturing systems, which is one of the biggest projects ever introduced.

A multi-objective algorithm for optimization of modern machining processes

Multi-objective optimization aspects of four modern machining processes namely wire-electro discharge machining process, laser cutting process, electrochemical machining process and focused ion beam micro-milling process are considered in this work. In WEDM process cutting velocity and surface quality are important objectives which are mutually conflicting in nature. Minimization of kerf taper is vital in the laser cutting process which increases with the increase in material removal rate. The ECM process is characterized by high material removal rate, but poor dimensional accuracy, high tool wear rate and high over cut. FIB micro-milling process is useful in applications where a nano-level surface finish is desired but this process is characterized by a very low material removal rate. All the above mentioned objectives are vital as they closely govern the performance of the machining processes considered in this work. Therefore, the aim of this work is to achieve these objectives through process parameter optimization. In order to handle multiple objectives simultaneously a new posteriori multi-objective optimization algorithm named as multi-objective Jaya (MO-Jaya) algorithm is proposed which can provide multiple optimal solutions in a single simulation run. The regression models for the above mentioned machining processes which were developed by previous researchers are used as fitness function for MO-Jaya algorithm.In the case of WEDM process the optimization problem is an unconstrained, linear and parameter bounded. In the case of laser cutting process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In the ECM process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. The second case study of ECM process the optimization problem is a non-linear, constrained, non-quadratic and parameter bounded. In the case of FIB micro-milling process, the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In addition, the performance of MO-Jaya algorithm is also tested on a non-linear, non-quadratic unconstrained multi-objective benchmark function of CEC2009. In order to handle the constraints effectively a heuristic approach for handling constraints known as the constrained-dominance concept is used in MO-Jaya algorithm. In order to ensure that the newly generated solutions are within the parameter bounds a parameter-bounding strategy is used in MO-Jaya algorithm. The results of MO-Jaya algorithm are compared with the results of GA, NSGA, NSGA-II, BBO, NSTLBO, PSO, SQP and Monte Carlo simulations. The results have shown the better performance of the proposed algorithm. Flowchart for the MO-Jaya algorithm.Display Omitted A new multi-objective optimization algorithm named as MO-Jaya algorithm is proposed.Multi-objective optimization aspects of modern machining processes are considered.A Pareto optimal set of solutions along with a Pareto front is obtained for each of the considered machining processes.The MO-Jaya algorithm may also be applied to multi-objective optimization problems of other manufacturing processes.

Surface Grinding Process Optimization Using Jaya Algorithm

Optimization problem of an important traditional machining process namely surface grinding is considered in this work. The performance of machining processes in terms of cost, quality of the products and sustainability of the process is largely influenced by its process parameters. Thus, choice of the best (optimal) combination machining parameters is vital for any machining process. Hence, in present work a new algorithm is used for solving the considered optimization problem. The Jaya algorithm is a simple yet powerful algorithm and is a algorithm-specific parameter-less algorithm. The comparison of results of optimization show that the results of Jaya algorithm are better than the results reported by previous researchers using GA, SA, ABC, HS, PSO, ACO and TLBO.

Modeling and Design of Experiments of Laser Cladding Process by Genetic Programming and Nondominated Sorting

Laser deposition of materials represents a modern additive technology that has a number of advantages over remaining technologies for depositing metallic materials. Besides a low-energy input, a quality bond, and minimal heat-affected zone, this technology is also characterized by the good mechanical properties of the deposited material that is a result of rapid cooling. Despite the prospects, this technology is still at the developing phase. New materials and techniques for determining optimal process parameters are being introduced. In this article, we developed a system for modeling (predicting) the properties of the deposited material and used design of experiments (DOE) for the laser cladding process parameter selection. Based on the experimental data obtained during cladding process, models were made for predicting the volume and roughness of the deposited material. Genetic programming was used for modeling the process. Then, a set of several thousand possible combinations (settings) of the machine parameters was produced on the basis of the obtained model. The most appropriate machine (process) parameters were selected in terms of deposition speed, powder efficiency, and surface roughness. These parameters were determined by nondominated sorting. The results offer the operator of the machine a set of appropriate process parameters that enable the production of high-quality products.

Programming of CNC Milling Machines Using Particle Swarm Optimization

This article proposes asystem for theautomatic programming of a CNC milling machine by particle swarm optimization (PSO). In the presented research, each individual swarm particle presents a possible numerical control (NC) program. Voxel representation of machining area was used. Bresenhams algorithm was implemented, for the rasterization of the cuts. Optimisation with PSO was carried out within a voxelized machining area. The system automatically finds the NC program for optimal machining. The NC program guarantees an optimal selection of tools, the shortest possible work and rapid motions, and minimization of the manufacturing time, thus achieving a reduction in machining costs and increased productivity. Testing using test workpieces and 2.5 D milling confirmed the efficiency of the proposed approach. The proposed intelligent system is easily adaptable for programming other types of CNC machines by PSO.

Genetic programming approach to determining of metal materials properties

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 Siebels 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.

Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching–Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.

Intelligent design of an unconstrained layout for a flexible manufacturing system

The presented research removes common constraints regarding the design of layout of flexible manufacturing system, and the subsequent search for a good solution is left solely to artificial intelligence. The proposed system is composed of a creative subsystem which can use different evolutionary optimization methods, and a subsystem for evaluating layouts. In the presented work the subsystem for creation uses a particle swarm optimization method for the creation/modification of solution sets. Evaluation of solution quality is made using intelligent search of the shortest travel paths within the layout. This system has proved to be innovative since it proposes very good solutions which are oriented to the main task of the system and are not simplified because of human limitations.