Parameter Determination of Milling Process Using a Novel Teaching-Learning-Based Optimization Algorithm

Abstract

Cutting parameter optimization dramatically affects the production time, cost, profit rate, and the quality of the final products, in milling operations. Aiming to select the optimum machining parameters in multitool milling operations such as corner milling, face milling, pocket milling, and slot milling, this paper presents a novel version of TLBO, TLBO with dynamic assignment learning strategy (DATLBO), in which all the learners are divided into three categories based on their results in “Learner Phase”: good learners, moderate learners, and poor ones. Good learners are self-motivated and try to learn by themselves; each moderate learner uses a probabilistic approach to select one of good learners to learn; each poor learner also uses a probabilistic approach to select several moderate learners to learn. The CEC2005 contest benchmark problems are first used to illustrate the effectiveness of the proposed algorithm. Finally, the DATLBO algorithm is applied to a multitool milling process based on maximum profit rate criterion with five practical technological constraints. The unit time, unit cost, and profit rate from the Handbook (HB), Feasible Direction (FD) method, Genetic Algorithm (GA) method, five other TLBO variants, and DATLBO are compared, illustrating that the proposed approach is more effective than HB, FD, GA, and five other TLBO variants.

The Optimization of Machining Parameters for Milling Operations by Using the Nelder–Mead Simplex Method

“…When i and j are different, each function value can be written by x i and x j , and the centroid point between these values can be defined by C. To find the optimum value when variables change, there are three operations. Reflection, contraction, and expansion were used to find an optimum value [16].…”

“…Before restarting the process, new x i should be replaced by (x i + W)/2. A failed contraction is much rarer but can occur when a valley is curved, and one point of the simplex is much farther from the valley bottom than the others [16]. The operations of reflection, contraction, and expansion are not significantly affected by the change in coefficient α, β, and γ.…”

“…Tolouei and Bidhendi [7] optimized the case study using the method of feasible direction, and feasible starting points were determined by the machining handbooks. The case study proposed by Tolouei and Bidhendi [7] was subsequently used by researchers such as Yildiz [12], Abhishek [13], Deepak [14], Gomez and Jurado [15], and Zhai et al [16] utilizing different optimization methods.…”

“…Gomez and Jurado [15] utilized a modified Shuffled Frog-Leaping Algorithm to find the optimal machining parameters in milling processes. Zhai et al [16] used a Novel Teaching-Learning-Based Optimization Algorithm with a dynamic assignment learning strategy (DATLBO) to select the optimum machining parameters in multi-tool milling operations. In this research, the Nelder-Mead simplex method [17] was utilized to optimize the milling process using the same case study in Tolouei and Bidhendi [7] to determine whether a better solution can be achieved.…”

Optimization of Multi-Pass Face Milling Parameters Using Metaheuristic Algorithms

“…Its optimization performance was later validated with respect to GA technique. Zhai et al [12] proposed an improved TLBO algorithm, in the form of TLBO with dynamic assignment learning strategy (DATLBO), for a multi-tool milling process in order to maximize profit rate under several machining constraints. Its optimization performance was also compared with that of other techniques.…”