This chapter presents a novel approach for identification of the re-machining parameters. The chapter starts with an introduction about the significant role of re-machining at the reprocessing stage. Then, the related studies dealing with the selection of optimum machining parameters are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. A detailed description about the approach (i.e., firefly algorithm) can be found in the proposed methodology section. Right after this, an illustrative example is detailed in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.
TopIntroduction
Machining is the broad term used to describe removal of material from a workpiece, it covers several processes such as cutting, grinding, milling, turning, and drilling. Currently, the factors which most influence quality of machining process are: type of blank, machining technology, operations, sub-operations, machine tools, cutting tools, fixtures, measuring devices, etc (Vukelic et al., 2011). Re-machining has many similarities to traditional machining. At the most basic level, both involve cutting, grinding, milling, turning, forming, joining, and drilling. The major difference between the two involves the machining tolerances, i.e., how many dimensional allowances the casting producers need to leave for the machining works. In a re-machining process, the machining allowances should be considered carefully since the modification to the casting will me occur and it will be more difficult. In addition, the re-machining processes also largely depend on the non-traditional processes which utilizing electrical, chemical and optimal sources of energy. Consequently, the re-machining operation play an end role on remanufacturable parts/components adjusted to standard.
Nowadays, machining process is widely used, such as in aerospace, automobile and other industries (Kakati, Chandrasekaran, Mandal, & Singh, 2011), and offers many advantages, including less material waste, flexible feature size and shape, and high precision. Obviously, within a number of operations which influence output effects of reprocessing process, the re-machining operation plays prominent role due to it brings high quality precise benefits for remanufacturing industries in the global competitive market. In addition, the re-machining process also can play a significant role in determining product remanufacturing cost and impact on the environment (Nicolaou, Mangun, & Thurston, 2001). However, optimization of re-machining processes for remanufacturable parts/components requires a large amount of specific knowledge, such as tools and machines selection, effects of cutting conditions, the mechanical properties on materials, and the tool errors and machine tool deviations. To deal with these challenges, one of the considerations is by optimizing the machine process parameters (Baskar, Asokan, Saravanan, & Prabhaharan, 2005; Tolouei-Rad & Bidhendi, 1997).
In practice, many researchers have studied the effects of optimal selection of machining parameters (Kastner et al., 2013; Rao & Kalyankar, 2013; Yildiz, in press-a, in press-b; Yusup, Zain, & Hashim, 2012). They usually formulate the model as a multiple objective optimization problem (Cus, Zuperl, Kiker, & Milfelner, 2006). As the model is computationally expensive to solve, in this chapter, a new population-based non-traditional optimization algorithm (i.e., firefly algorithm (FA)) is applied to obtain model parameter estimates. Also, the propose of the present research is to introduce a new optimization approach to solve optimization problems in the remanufacturing industry. Computational results showed that the new approach is effective in machining operations.