The selection of optimal cutting parameters, like the number of passes, depth of cut for each pass, feed and speed, is a very important issue for every machining processes. In workshop practice, cutting parameters are selected from machining databases or specialized handbooks, but the range given in this sources are actually starting values, and are not the optimal values (Dereli et al., 2001).
Optimization of cutting parameters is usually a difficult work (Kumar and Kumar, 2000), where the following aspects are required: knowledge of machining; empirical equations relating the tool life, forces, power, surface finish, etc., to develop realistic constrains; specification of machine tool capabilities; development of an effective optimization criterion; and knowledge of mathematical and numerical optimization techniques (Sönmez et al., 1999).
In any optimization procedure, it is a crucial aspect to identify the output of chief importance, the so-called optimization objective or optimization criterion. In manufacturing processes, the most commonly used optimization criterion is specific cost, which has been used by many authors, from the beginning of the researches in this branch (Taylor, 1907) to some of the most recent works (Liang et al., 2001; Wang et al., 2002; Saravanan et al., 2003; Cus and Balic, 2003; Amiolemhen and Ibhadode, 2004).
Sometimes, other criteria like machining time (Chua et al., 1991), material removal rate (Ko and Kim, 1998; Chien and Tsai, 2003) or tool life (Molinari and Nouari, 2002) have been used too. However, these single-objective approaches have a limited value to fix the optimal cutting conditions, due to the complex nature of the machining processes, where several different and contradictory objectives must be simultaneously optimized.
Some multi-objective approaches have been reported in cutting parameters optimization (Lee and Tarng, 2000; Zuperl and Cus, 2003; Cus and Balic, 2003), but mainly they use a priori techniques, where the decision maker combines the different objectives into a scalar cost function. This actually makes the multi-objective problem, single-objective prior to optimization (Van Veldhuizen and Lamont, 2000).
On the other hand, in the a posteriori techniques, the decision maker is presented with a set of non-dominated optimal candidate solutions and chooses from that set. These solutions are optimal in the wide sense that no other solution in the search space are superior to them when all optimization objectives are simultaneously considered (Abbass et al., 2001). They are also known as Pareto-optimal solutions.
Comparing citations by technique, in the last years, evidences the popularity of a posteriori techniques (Van Veldhuizen and Lamont, 2000). In dealing with multi-objective optimization problems, classical optimization methods (weighted sum methods, goal programming, min–max methods, etc.) are not efficient, because they cannot find multiple solutions in a single run, thereby requiring them to be applied as many times as the number of desired Pareto-optimal solutions.
On the contrary, studies on evolutionary algorithms have shown that these methods can be efficiently used to eliminate most of the above-mentioned difficulties of classical methods (Soodamani and Liu, 2000). In this paper, a multi-objective optimization method, based on a posteriori techniques and using genetic algorithms, is proposed to obtain the optimal parameters in turning processes.
As can be remarked in the exposed sample, a posteriori multi-objective optimization offers greatest amount of information in order to make a decision on selecting cutting parameters in turning. By means of Pareto frontier graphics, several different situations may be considered, facilitating the choice of right parameters for any condition.
The proposed micro-GA has shown obtain several, uniformly distributed points, in order to arrange the Pareto front, at a reasonably low computational cost. Aspects like diversity maintenance and constraints handling have been successfully sorted for the studied problem.
Cost analysis can complement the Pareto front information, and it helps the decision-making process. The proposed model must be enlarged to include more constraints, such as cutting surface temperature.
