Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Abstract

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

Designing and implementation of a novel online adaptive control with optimization technique in hard turning

“…Since ACO systems need to be executed in real time, PSO can be a good choice to be implemented in optimization unit of an ACO system. 8 In PSO, each particle has a cognitive factor and a social factor. Cognitive factor determines the trajectories of particles, whereas social factor forces the particle to find its best position and affects the speed of particle.…”

“…ACO systems adjust cutting parameters to maximize a given performance index, which can be a function of production cost and material removal rate (MRR), under certain necessary constraints. 8 In the literature, several research works in the field of using ACO systems in machining operations have been reported. Ko and Cho 9 suggested a method for adaptive optimization of cutting parameters for maximizing MRR in milling operation.…”

“…Tool wear was measured offline in each pass of machining and the appropriate cutting parameters were selected according to defined cost function and measured wear values. Coppel et al 8 devised an ACO methodology in micro-milling which optimized production cost subjected to surface quality of machined parts. Tool wear monitoring (TWM) was performed using a dynamometer.…”

“…The majority of performed works lacked a reliable tool wear measuring system, so the proposed optimal condition estimated by these methods could not be used industrially with high precision. 8 Moreover, most of the research works performed dealt with milling process, and a comprehensive investigation in the field of application of ACO systems in turning process has not been presented yet. Only a few accurately performed research works can be noted such as works presented by Coppel et al 8 and Silva et al, 13 which both are allotted to milling operation.…”

Development of in-situ adaptive controller for end milling based on vibration feedback

“…A review of the AC system is presented in [12]. Three types of ANC's namely identified [13], first one is Adaptive control with constraint (ACC), the second one is Geometry adaptive control (GAC) and the third one is Adaptive control with optimization (ACO). The adaptive controller industrialized in the present study fit into the third classification in which the cutting parameters are calculated and controlled in imperative to optimize a definite index of cutting performance, such as decrease of vibration, amassed of efficiency, enhancement of surface finish, and control of the tool flank wear.…”