OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives

MODELING AND OPTIMIZATION OF MACHINING PERFORMANCE MEASURES IN FACE MILLING OF AUTOMOTIVE ALUMINUM ALLOY A380 UNDER DIFFERENT LUBRICATION/COOLING CONDITIONS FOR SUSTAINABLE MANUFACTURING

The use of cutting fluids in machining process is very essential for achieving desired machining performance. Due to the strict environmental protection laws now in effect, there is a wide-scale evaluation of the use of cutting fluids in machining. Consequently, minimal quantity lubrication (MQL), which uses very small quantity of cutting fluids and still offers the same functionality as flood cooling, can be considered as an alternative solution. This thesis presents an experimental study of face milling of automotive aluminum alloy A380 under four different lubrication/cooling conditions: dry cutting, flood cooling, MQL (Oil), and MQL (Water). Experiments were design using Taguchi method for design of experiments. Empirical models for predicting surface roughness and cutting forces were developed for these four conditions in terms of cutting speed, feed and depth of cut. Optimization technique using Genetic Algorithms (GA) was used to optimize performance measures under different lubrication/cooling conditions, based on a comprehensive optimization criterion integrating the effects of all major machining performance measures. Case studies are also presented for two pass face milling operation comparing flood cooling condition with MQL. The comparison of the results predicted by the models developed in this work shows that the cutting force for MQL (Oil) is either lower or equal to flood cooling. The surface roughness for MQL (Oil) is comparable to flood cooling for higher range of feed and depth of cut. A comparison of the optimized results from the case studies, based on value of utility function, shows that the optimum point for two pass face milling operation having MQL (Oil) as finish pass has highest utility function value

Multi-Objective Optimization of Input Machining Parameters to Machined AISI D2 Tool Steel Material

Poor surface finish on die and mould transfers the bad quality to processed parts. High surface roughness is an example of bad surface finish that is normally reduced by manual polishing after conventional milling machining process. Therefore, in order to avoid disadvantages by manual polishing and disadvantage by the machining, a sequence of two machining operations is proposed. The main operation is run by the machining and followed by Rotary Ultrasonic Machining Assisted Milling (RUMAM). However, this sequence operation requires optimum input parameters to generate the lowest surface roughness. Hence, this paper aims to optimize the input parameters for both machining operations by three soft-computing approaches – Genetic Algorithm, Tabu Search, and Particle Swarm Optimization. The method adopted in this paper begins with a fitness function development, optimization approach usage and ends up with result evaluation and validation. The soft-computing approaches result outperforms the experiment result in having minimum surface roughness. Based on the findings, the conclusion suggests that the lower surface roughness can be obtained by applying the input parameters at maximum for the cutting speed and vibration frequency, and at minimum for machining feed rate. This finding assists manufacturers to apply proper input values to obtain parts with minimum surface roughness

Adaptive control optimization in micro-milling of hardened steels-evaluation of optimization approaches

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimiza- tion problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumula- tive distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘ closed ’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technolog- ical limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing op- eration; therefore, the economic objectives are minimized un- der imposed maximum probabilities of failure. In both situa- tions, the efficiency and robustness of optimal solution