MULTI-OBJECTIVE OPTIMIZATION OF ABRASIVE FLOW MACHINING PROCESSES USING POLYNOMIAL NEURAL NETWORKS AND GENETIC ALGORITHMS

Abrasive flow machining (AFM) is an economic and effective non-traditional machining technique, which is capable of providing excellent surface finish on difficult to approach regions on a wide range of components. With this method, it has become possible to substitute various time-consuming deburring and polishing operations that had often lead to non-reproducible results. In this paper, group method of data handling (GMDH)-type neural networks and Genetic algorithms (GAs) are first used for modelling of the effects of number of cycles and abrasive concentration on both material removal and surface finish, using some experimentally obtained training and testing data for brass and aluminum. Using such polynomial neural network models obtained, multi-objective GAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are then used for Pareto-based optimization of AFM considering two conflicting objectives such as material removal and surface finish. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of AFM can be discovered by the Pareto-based multi-objective optimization of the obtained polynomial models. Such important optimal principles would not have been obtained without the use of both GMDH-type neural network modelling and multi-objective Pareto optimization approach.     

OPTIMIZATION OF ELECTRO-CHEMICAL MACHINING PROCESS PARAMETERS USING GENETIC ALGORITHMS

ECM and ECM-based processes (derived and hybrid processes) are one of the most widely used advanced machining processes (AMPs) to make complicated shapes of varying sizes in the products made of electrically conducting but difficult-to-machine materials such as superalloys, Ti-alloys, alloy steel, tool steel, stainless steel, etc. These materials are extensively used in aerospace, automobile, space, nuclear, defense, cutting tools, dies and mold making applications. ECM offers some unique advantages over other conventional and advanced machining processes but its use incurs relatively higher initial investment cost, operating cost, tooling cost, and maintenance cost. Use of optimum ECM process parameters can significantly reduce the ECM operating, tooling, and maintenance cost and will produce components of higher accuracy which is very important in some critical areas such as aerospace, space, defense, nuclear applications. Therefore, choice of optimum process parameters is essential to ensure the most cost-effective, efficient, and economic utilization of ECM process potentials. This paper describes optimization of three most important ECM process parameters namely tool feed rate, electrolyte flow velocity, and applied voltage with an objective to minimize geometrical inaccuracy subjected to temperature, choking, and passivity constraints using real-coded genetic algorithms. Comparison of the obtained optimization results with the results of past work in this direction shows an improvement in terms of geometrical accuracy.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm (GA) embedded with deterministic timed Petri nets(DTPN) for reconfigurable production line(RPL) is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.