Empirical model-building and response surface
Empirical model-building and response surface
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
A robust parameter design for multi-response problems
Journal of Computational and Applied Mathematics
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
A review of optimization techniques in metal cutting processes
Computers and Industrial Engineering
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Expert Systems with Applications: An International Journal
Applied Soft Computing
Expert Systems with Applications: An International Journal
Mechanical Design Optimization Using Advanced Optimization Techniques
Mechanical Design Optimization Using Advanced Optimization Techniques
| Hi-index | 12.05 |
Optimization of machining processes is of primary importance for increasing machining efficiency and economics. Determining optimal values of machining parameters is performed by applying optimization algorithms to mathematical models of relationships between machining parameters and machining performance measures. In recent years, there has been an increasing trend of using empirical models and meta-heuristic optimization algorithms. The use of meta-heuristic optimization algorithms is justified because of their ability to handle highly non-linear, multi-dimensional and multi-modal optimization problems. Meta-heuristic algorithms are powerful optimization tools which provide high quality solutions in a short amount of computational time. However, their stochastic nature creates the need to validate the obtained solutions. This paper presents a software prototype for single and multi-objective machining process optimization. Since it is based on an exhaustive iterative search, it guarantees the optimality of determined solution in given discrete search space. The motivation for the development of the presented software prototype was the validation of machining optimization solutions obtained by meta-heuristic algorithms. To analyze the software prototype applicability and performance, six case studies of machining optimization problems, both single and multi-objective, were considered. In each case study the optimization solutions that had been determined by past researchers using meta-heuristic algorithms were either validated or improved by using the developed software prototype.