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1.
Simulated annealing, genetic algorithm, and particle swarm optimization techniques have been used for exploring optimal machining parameters for single pass turning operation, multi-pass turning operation, and surface grinding operation. The behavior of optimization techniques are studied based on various mathematical models. The objective functions of the various mathematical models are distinctly different from each other. The most affecting machining parameters are considered as cutting speed, feed, and depth of cut. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, temperature, and cutting force.  相似文献   

2.
Enhancing the performance of manufacturing operations represents a significant goal, especially when cost savings are linked with economies of scale to be exploited. In the area of machining optimization, the selection of optimal cutting parameters subjected to a set of technological constraints plays a key role. This paper presents a novel hybrid particle swarm optimization (PSO) algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization technique consists of a PSO-based framework wherein a properly embedded simulated annealing (SA), namely an SA-based local search, aims both to enhance the PSO search mechanism and to move the PSO away from being closed within local optima. In order to handle the numerous constraints which characterize the adopted machining mathematical model, a constraint violation function integrated with a suitable objective function has been engaged. In addition, a twofold strategy has been implemented to manage the equality constraint between the provided total depth of cut and the number of passes to be performed. Firstly, an accurate problem encoding involving only five cutting parameters has been performed. Secondly, a proper repair procedure that should be run just before any solution evaluation has been engaged. Five different test cases based on the multi-pass turning of a bar stock have been used for comparing the performance of the proposed technique with other existing methods.  相似文献   

3.
In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.  相似文献   

4.
Micro end milling with fine grained carbide end mills is an economical way to machine small and medium lots of micro components. Considering the sensitivity of the slender end mills which are very costly compared to conventional end mills, it is imperative that the machining parameters are chosen appropriately so as to ensure maximum tool life and minimum possible production cost without violating any of the imposed constraints. Unlike in conventional end milling operations the tool life in micro end milling operations increases with axial depth of cut till it equals the tool diameter and this makes it even difficult to ascertain the appropriate depth of cut to machine a specific component. In this paper the influence of depth of cut on tool life is illustrated and depth of cut is also considered as one of the decision variables in the optimization problem. More over in this paper Genetic Algorithms (GA) based on natural laws of evolution is used to optimize the cutting parameters. Finally a test case is presented to give a clear picture of the application of the optimization algorithm.  相似文献   

5.
Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for turning operations with constant diameters only. All Computer Numerical Control (CNC) machines produce the finished components from the bar stock. Finished profiles consist of straight turning, facing, taper and circular machining.This research work concentrates on optimizing the machining parameters for turning cylindrical stocks into continuous finished profiles. The machining parameters in multi-pass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost.In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint, tool tip temperature, etc. Due to high complexity of this machining optimization problem, six non-traditional algorithms, the genetic algorithm (GA), simulated annealing algorithm (SA), Tabu search algorithm (TS), memetic algorithm (MA), ants colony algorithm (ACO) and the particle swarm optimization (PSO) have been employed to resolve this problem. The results obtained from GA, SA,TS, ACO, MA and PSO are compared for various profiles. Also, a comprehensive user-friendly software package has been developed to input the profile interactively and to obtain the optimal parameters using all six algorithms. New evolutionary PSO is explained with an illustration .  相似文献   

6.
Honing is a material removal process widely used in manufacturing of engine cylinders, compressors, valves, bearings, and hydraulic cylinders. The surface topography generated by honing has a profound effect on the tribological performance of the honed surface, since the cross-hatch pattern on the workpiece surface can be used to retain oil or grease to ensure proper lubrication and minimize wear. The number of contact grains and the grain depth of cut are important indicators of interactions between the tool and workpiece in the honing process. They are helpful in understanding chip formation, tool wear, and optimization of the cross-hatch pattern to improve the tribological performance of the honed surface. This article presents a physics-based model for predicting the number of contact grains and the grain depth of cut in honing. It describes the model development and studies the influences of abrasive grain size, abrasive concentration, nominal contact area, yield strength of workpiece material, and static load on the number of contact grains and the maximum grain depth of cut. Results from pilot experiments are used to verify the model.  相似文献   

7.
In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.  相似文献   

8.
The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.  相似文献   

9.
In this paper, to facilitate manufacturing engineers have more control on the machining operations, the optimization issue of machining parameters is handled as a multi-objective optimization problem. The optimization strategy is to simultaneously minimize production time and cost and maximize profit rate meanwhile subject to satisfying the constraints on the machine power, cutting force, machining speed, feed rate, and surface roughness. An efficient fuzzy global and personal best-mechanism-based multi-objective particle swarm optimization (F-MOPSO) to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling. The proposed F-MOPSO algorithm is first tested on several benchmark problems taken from the literature and evaluated with standard performance metrics. It is found that the F-MOPSO does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimization problems. Upon achieving good results for test cases, the algorithm was employed to a case study of multi-pass face milling. Significant improvement is indeed obtained in comparison to the results reported in the literatures. The proposed scheme may be effectively employed to the optimization of machining parameters for multi-pass face milling operations to obtain efficient solutions.  相似文献   

10.
The significant cutting disturbances appearing in hard turning processes cause shifting of the process dynamics. Therefore, in this paper the turning process is evaluated by radial force variation analysis, as a function of depth of cut, tool nose radius and effective lead edge angle, through static and dynamic indicators. The tool/workpiece contact zone is, in the case of hard turning, mostly limited within the tool nose radius region. Therefore in this paper, geometry of the tool/workpiece contact line is analyzed. The depth of cut is calculated as a geometric difference of prior and instantaneous tool pass profiles. The calculated values of the depth of cut are time dependant, and can vary by 60%. Various process monitoring techniques have been used to identify and confirm these variations, as well as quantify the level of process stability. The results obtained confirm the assumption that effective lead edge angle and radial force are influenced by depth of cut, feed rate and tool nose radius. Additionally, it is shown that low values of depth of cut and geometry of prior pass-machined surface valleys shift the hard turning process to a dynamically more sensitive level as compared the case of soft machining.  相似文献   

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