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. Althoug… Show more
“…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.…”
Section: Proposed Aco Proceduresmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.
“…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.…”
Section: Proposed Aco Proceduresmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.
“…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.…”
This study establishes a tool condition monitoring methodology builds on the vibration signal attained via data acquisition system which is integrated with the in house developed adaptive controller for an end milling. As the quality of the products and the machine tool performance are the key parameters in maintaining machine stability. Proposed Adaptive control optimization system is validated with the experimentation trials and data analysis on 3 axis CNC milling machine. The rotational speed of the spindle and vibration signals is found to be reactive to milling cutter condition and therefore capable of sustaining the set-out methodology. A novel hybrid transformation, coupled with FFT and HHT is proposed to distinguish between a source of variation for adaptive control optimization, cutting region with the non-cutting region. In this study, decisions are made to evaluate the tool condition by combining all related information into a rule base. The investigation trajectories unveil the established system be able to accomplish the mechanism properly as anticipated.
Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed.
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