This chapter proposes a hybrid approach for modelling and optimizing the oblique turning processes. Analytical modelling and statistical regressions are combined for predicting the values of the most important parameters involved in the oblique cutting process. The predictions of the model were validated by using experimental data, showing coincidence for a 95% confidence level. Then, an a posteriori multi-objective optimization is carried out by using a genetic algorithm. Three conflicting objectives, which represent the three pillars of the sustainability as defined in the triple bottom line, are simultaneously considered: the carbon dioxide emissions, the cost, and the cutting time. The outcome of the optimization process is a set of non-nominate solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsening the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.
TopIntroduction
Optimization is a very important issue in mechanical industry. Specially, in machining processes, where different aspects must be considered, selecting the most proper cutting conditions plays a key role for obtaining efficient and competitive products.
From the first published cutting processes optimization work (Taylor 1907), to the most current literature (Chethan et al. 2019, Radha Krishnan, & Ramesh 2020, La Fé et al. 2020) a big amount of research has been expended on this topic. Three main concerns arise from the reported studies: the optimization objectives, the models relating decision variables with targets and constraints, and optimization techniques. With regard to the first issue, most of the considered goals have been focused on economic aspects, such as cost and time. Sometimes, quality criteria, such as obtained surface roughness have been also included. As sustainability has become an important matter in contemporary industry, objectives focused on this point of view, have also appeared in the specialized literature. Specially, the co-called Triple Bottom Line (Peralta et al. 2016), which defines the three pillars of sustainability (i.e., environmental, economic and social aspects) has been considered as a useful tool for these analysis (La Fé et al. 2020, Hegab, Darras, & Kishawy 2018).
On the other hand, models portrait an important function into de optimization, as they guaranty the accuracy of the obtained solutions. The high complexity of the phenomena involved on the cutting processes makes it very hard to obtain reliable accurate models for describing the behavior of the involved variables. Most of the optimization approaches use empirical models which cannot be applied outside the data intervals used for fitting these models. Moreover, obtaining experimental data from cutting processes is expensive and time consuming. Other approaches have been proposed for modelling the cutting processes, such as finite element method (Bartarya, & Choudhury 2011, Abouridouane et al. 2016, Weng et al. 2017) and mechanistic approaches (Vinogradov 2014, Zhang, & Guo 2015, Abouridouan et al. 2016, Baohai, et al. 2016, Bai et al. 2017, D’Acunto et al. 2017, Fu et al. 2018, Gao et al. 2018, Weng et al. 2018). Both offer reasonable accuracy with few experimental data, but requires a lot of time for computing the outcomes (specially, the finite element method).
Finally, due to the complex nature of these models, the objectives functions and constraints do not fulfil the conditions of continuity, differentiability and unimodality, required for most of the optimization tools (Quiza et al. 2012). Therefore, heuristics techniques have been applied for solving these problems. They include genetic algorithms (Ganesan, & Mohankumar 2013, Batish et al. 2014, Kübler et al. 2015), simulated annealing (Wang et al. 2006, Baseri 2011), particle swarm optimization (Marko et al. 2014) and ant colony optimization (Vijayakumar et al. 2003), but all of them rely on the accuracy and reliability of the underlying models.