Optimization Of Cutting Parameters Research Articles

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

The increasing demand for energy is leading to global depletion of fossil fuels and growing environmental pressures, which are issues that need to be addressed. Machine tools are basic energy-consuming equipment in manufacturing systems. However, existing theoretical models ignore tool wear as well as workpiece material properties. This makes it difficult to further improve the accuracy of the model. Therefore, this study begins with the point of view of energy dissipation in the metal material removal process. A milling power model for computer numerical control (CNC) machines, considering tool wear and workpiece material properties during machining, is established. At the same time, milling is taken as the research object. A multi-objective cutting parameter optimization model is established to ensure the surface quality of the workpiece. In addition, the cutting energy consumption is taken into account in the developed models. Based on the multi-objective manta ray foraging optimization algorithm (MOMRFO), the Pareto-optimal solution set under multiple cutting conditions is solved. Finally, the experimental results of optimized parameters are compared with empirical parameters. The average prediction accuracy of the proposed energy consumption prediction model is above 91%. The experiments show that machining quality improves by optimizing the cutting parameters, with SEC, MRR, and Ra increasing by more than 44%, 53%, and 38%, respectively. The goals of reducing energy consumption and increasing productivity are achieved.

A data and mechanism hybrid driven cutting parameter optimization method considering the machine tool and coolant condition flexibility

A data and mechanism hybrid driven cutting parameter optimization method considering the machine tool and coolant condition flexibility

Research on the stability prediction for multi-posture robotic side milling based on FRF measurements

In robotic side milling, frequent chatter extremely restricts the acquisition of high surface quality due to weak stiffness, and cutting parameters optimization guided by stability boundary is regarded as an effective solution to solve the chatter problem. In this research, the influence mechanisms of stability were analyzed by evaluating the structural static stiffness and dynamic parameters, and the main factor was characterized as regenerative chatter by means of stability measurements and the theoretical prediction model. The distance-driven multi-posture frequency response function (FRF) prediction model was improved in terms of the dominant modal. Grey correlation analysis was carried out to investigate the influence law of robotic joints to modal parameters, and the difference between far-distance posture and near-distance posture was re-characterized by cross-validation of FRF measurements. Finally, the third-order Hermite–Newton approximation was employed to solve the dynamic model by considering process damping effect, and the results showed the prediction accuracy of the constructed stability boundary was over 85%.

Multi-agent reinforcement learning method for cutting parameters optimization based on simulation and experiment dual drive environment

Improving production efficiency while ensuring product surface quality is a constant focus of manufacturers. Cutting parameter optimization is an important technique for ensuring high-efficiency and high-quality production. In this paper, a novel method for cutting parameter optimization that integrates multi-agent reinforcement learning with a dual-drive virtual machining environment is proposed. First, a feature extraction, fusion and generation model for cutting simulation and experimental data is proposed to solve the problem of incomplete data acquisition in the production process. Second, a Markov decision model for optimizing cutting parameters is defined, and a virtual machining environment driven by both simulation and experimental data is constructed. Third, a novel multi-agent reinforcement learning method called Q-MIX-MATD3, in which a twin delay deep deterministic policy gradient, value function decomposition and a teacher model are combined, is proposed to explore the cutting parameter optimization policy by interacting with the virtual machining environment. Finally, the proposed method is verified on a commutator production line. Moreover, the results show that the accuracy of the virtual machining environment driven by both simulation and experiment increases by more than 5 %, response efficiency increases by 31 %, and Q-MIX-MATD3-based cutting parameter optimization method reduces time cost by 98 % and achieves the optimization effect of the classical optimization method.

Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion

Considering that titanium alloy is a critical aviation resource, ensuring its machining quality is of significant importance. Surface roughness remains a key parameter for surface quality inspection. This article introduces a multi-sensor titanium alloy milling monitoring system aimed at accurately monitoring surface quality during titanium alloy processing. Principal component analysis is conducted on three-dimensional milling force and vibration. We propose a multi-objective cutting parameter optimization procedure to simultaneously optimize multiple cutting parameters to improve surface roughness and tool life. Considering the high dependence of visual measurement on the light source, we strive to mitigate this limitation and improve prediction accuracy by considering one-dimensional and two-dimensional feature values. We establish a multidimensional signal feature surface roughness prediction system based on milling parameters, milling vibration, milling force, and texture image features. Using particle swarm algorithm and machine learning models, the undetermined parameters in the prediction system are obtained. The results show that the prediction accuracy of the multi-signal feature fusion surface roughness prediction system is 99.12%, with a mean square error of less than 0.01. The research can provide some theoretical guidance for the accurate monitoring of the surface quality of titanium alloy processing.

Multi-Objective Cutting Parameter Optimization Method for the Energy Consumption and Machining Quality of Computerized Numerical Control Lathes

In order to achieve minimum energy consumption in computerized numerical control (CNC) lathe processing under the premise of ensuring the imposed roughness of the machined surface, a black hole-continuous ant colony optimization algorithm (BH-ACOR) is proposed to optimize the turning parameters. Taking turning specific energy and surface roughness as the optimization objectives, a turning test was designed. Subsequently, a multi-objective mathematical model of the cutting stage was formulated through the application of the least-squares method to fit the test data. The black hole algorithm was introduced to mitigate the shortcomings of the continuous-domain ant colony algorithm, which easily falls into a local optimum, so as to put forward a kind of BH-ACOR that is applicable to multi-objective optimization. The algorithm was applied to the multi-objective mathematical model in the turning stage to determine the optimal cutting parameters. Through simulation and test verification, the validity and practicability of the proposed method are further proved.

Cutting Parameter Optimization in Milling of Glass Fiber and Carbon Fiber Reinforced Composites to Reduce Cutting Force

Abstract Composite materials are often preferred in many engineering applications because of their lightness and high-strength properties. However, in addition to the advantages of composite materials, processing problems exist. The processing of fiber-reinforced composite materials is more complex than other metal materials. The forces that arise during milling cause undesirable results, such as tool wear and energy loss. In this study, the cutting parameter optimization was made by measuring the force during carbon fiber and glass fiber-reinforced epoxy matrix composites milling. Cutting velocity (90, 120, and 150 m/min) and feed per tooth (0.1, 0.15, and 0.2 mm/tooth) were selected as cutting parameters. Experiments were planned according to the Taguchi method with two parameters and three levels. Optimization of the parameters was evaluated using the signal/noise ratio approach. The effectiveness of parameters on results was determined by analysis of variance (ANOVA). Optimum levels were found as 120 m/min for cutting velocity and 0.1 mm/tooth for feed per tooth both glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) composites. According to the ANOVA results, the contribution rates of the force parameters were 81.05 % for cutting velocity and 15.03 % for feed per tooth in GFRP composites. The contribution rates of the force parameters were 43.69 % for cutting velocity and 46.18 % for feed per tooth in CFRP composites. The surfaces of the milled samples were examined with an optical microscope to investigate the damage. It was observed that the surfaces of CFRP samples had a better surface quality than GFRP samples.

Cutting parameters optimization and cutting performance of Ti(C,N)-based cermets by reactive hot-pressing from Co–Ti–C–BN system in dry turning austenitic stainless steels

Cutting parameters optimization and cutting performance of Ti(C,N)-based cermets by reactive hot-pressing from Co–Ti–C–BN system in dry turning austenitic stainless steels

Real-Time Cutting Temperature Measurement in Turning of AISI 1045 Steel through an Embedded Thermocouple—A Comparative Study with Infrared Thermography

During machining processes, a high temperature is generated in the cutting zone due to deformation of the material and friction of the chip along the surface of the tool. This high temperature has a detrimental effect on the cutting tool, and for this reason, it is of the utmost importance to assess the cutting temperature in real time during these processes. Despite all the advances and investigation in this field, accurately measuring the cutting temperature remains a great challenge. In this sense, this work intends to contribute to solving this problem by experimentally evaluating the potential of the developed approach for embedding thermocouples into the rake face of cutting tools for measuring cutting temperature in real time during dry turning of AISI 1045 steel for different cutting parameters and comparing the obtained results with infrared thermography measurements at the exact same point. A well-defined, smooth micro-groove with good surface quality was produced by laser surface modification. Then a laser-welded K-type thermocouple was fixated in the micro-groove with a MgO ceramic adhesive, ensuring protection from wear and chips, which allowed the creation of WC-Co cutting inserts with the ability to measure cutting tool temperature with a maximum error of 0.96%. Results showed that, despite yielding the same trend, the tool temperature measured by the IR thermographic camera was always lower than the temperature measured by the K-type embedded thermocouple. The proposed embedded thermocouple method proved to be a reliable, precise, accurate, and cost-effective approach for real-time temperature measurement capable of providing useful information for cutting parameter optimization, thus allowing increased productivity and tool life.

Cutting Parameters Optimization for Minimal Total Operation Time in Turning POM-C Cylindrical Stocks into Parts with Continuous Profile Using a PCD Cutting Tool

Turning is a complex machining process that can be characterized by a number of performances for a given machining system, workpiece material, cutting tool, and selected cutting regime. In addition to the characteristics of the machined surface quality, the estimation of machining time is particularly important for manufacturers, since machining time is directly related to other important performances of the turning process, such as productivity, cost, and energy consumption. In this paper, a model for estimation of total operation time in turning of a part with continuous profile, made of polyoxymethylene copolymer (POM-C), using a polycrystalline diamond (PCD) cutting tool, was developed. Face centred central composite design (CCD) and Box–Cox transformation approaches were applied for that purpose. The developed model was then used as the objective function in the proposed optimization model, which also included three practical constraints related to quality of the machined surface (surface roughness and workpiece deflection) and machinability aspects of the workpiece material (favourable chip forms). Nonlinear and linear models, used as constraints, were developed based on the results of experimental investigation of turning of POM-C using a PCD cutting tool. The total operation time estimation model showed good agreement with the results of tool path simulations in CAM software and validation experimental trial in real manufacturing environment. By applying the optimal solution, 44% of the total time being saved for machining of a single part can be achieved, compared to the recommended cutting parameter values, which indicates significant optimization benefits in turning industrial plastics.

Cutting Parameter Optimization based on Online Temperature Measurements

The deformation of metallic materials during the machining operation requires a significant amount of energy. During the chip formation process and due to the plastic deformation of the metal and the friction along the tool-part interface, the thermal loads generated are strongly impacted by the cutting factors. Thus, the choice of optimized cutting conditions is essential to control the quality of the work required. The aim of the present experimental study is to optimize the cutting parameters using temperature measurements. The average temperature of the cutting tool is studied using a FLIR A325sc type infrared camera. Optimal cutting parameters for each performance metric were obtained using the Taguchi techniques.

Cutting parameters optimization of hybrid fiber composite during drilling

Abstract Delamination is the most important problem encountered in drilling composite materials. This problem can be minimized by optimizing the cutting parameters. In this study delamination behavior of glass/carbon/aramid fiber reinforced hybrid composite was investigated during the drilling operation. Experiments were planned according to Taguchi’s L27 orthogonal array. Cutting velocity (20 m min−1, 40 m min−1, and 60 m min−1), feed rate (0.04, 0.08, and 0.12 mm rev−1), and cooling type (wet, air, and dry) were selected as cutting parameters. Thrust force was measured during the drilling operation, and the delamination factor for entrance was calculated after the tests. Optimization of the parameters was evaluated using the signal/noise ratio approach. The effectiveness of parameters on results was determined by analysis of variance (ANOVA). In addition, regression equations were obtained with the response surface method (RSM). According to the optimization results, the thrust force can be reduced with low cutting velocity and low feed rate under dry conditions. It was concluded that low cutting velocity and the feed rate should be applied under dry conditions to reduce delamination.

Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization

The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing production costs, and improving the quality of parts. Therefore, the research on cutting parameter optimization has important theoretical value and application value. In this paper, in order to find the optimal cutting parameters, the cutting model is solved by the improved monarch butterfly optimization (IMBO) algorithm, and the optimized cutting parameters are obtained. By establishing the mathematical model of cutting, the constraint conditions of actual machining are introduced into the model. In order to solve the model, some ideas of particle swarm optimization (PSO) and differential evolution (DE) are added to the traditional monarch butterfly optimization (MBO) algorithm. The MBO algorithm is improved to deal with multiobjective optimization problems. The IMBO algorithm is used to optimize the cutting model. The experiment shows that the optimized cutting parameters can significantly reduce production cost and maintain high production efficiency. Compared with NSGA-II algorithm and other swarm intelligence optimization algorithms, it shows that the IMBO algorithm has certain advantages in multiobjective optimization.

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing.

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

Effect of Optimization Software on Part Shape Accuracy and Production Times during Rough Milling of Aluminum Alloy

Reducing production costs during machining processes can be implemented in several ways, including various methods of cutting parameters optimization. The aim of the described research was to evaluate the effectiveness of software that optimizes the NC (Numerical Control) code generated from a CAM (Computer Aided Manufacturing) system. The experiments were carried out in rough milling. For the experiment, a sample with five pockets was designed and fabricated using the original NC programs from the CAM system and optimized NC programs. Evaluated criteria were the impact on the accuracy of the produced surfaces and degree of production time savings. When using the optimization software, despite more intensive cutting conditions, deviations on the side surfaces of the pockets were reduced from 3 to 23%. On the horizontal surfaces, for two pockets, there was an increase in deviations of a maximum of 7.8%, and for the remaining three pockets, there was a decrease in deviations ranging from 8.3 to 36%. The optimized NC programs achieved time savings ranging from 12.8 to 15.9%. This knowledge is important for manufacturers, as it allows shortening production times and thereby reducing related costs.

Generic Cutting Force Modeling with Comprehensively Considering Tool Edge Radius, Tool Flank Wear and Tool Runout in Micro-End Milling.

Accurate cutting force prediction is crucial in improving machining precision and surface quality in the micro-milling process, in which tool wear and runout are essential factors. A generic analytic cutting force model considering the effect of tool edge radius on tool flank wear and tool runout in the micro-end milling process is proposed. Based on the analytic modeling of the cutting part of the cutting edge in the end face of the micro-end mill bottom, the actual radius model of the worn tool is established, considering the tool edge radius and tool flank wear. The tool edge radius, tool wear, tool runout, trochoidal trajectories of the current cutting edge, and all cutting edges in the previous cycle are comprehensively considered in the instantaneous uncut chip thickness calculation and the cutter-workpiece engagement determination. The cutting force coefficient model including tool wear is established. A series of milling experiments are performed to verify the accuracy and effectiveness of the proposed cutting force model. The results show that the predicted cutting forces are in good agreement with the experimental cutting forces, and it is necessary to consider tool wear in the micro-milling force modeling. The results indicate that tool wear has a significant influence on the cutting forces and cutting force coefficients in the three directions, and the influences of tool wear on the axial cutting force and axial force coefficient are the largest, respectively. The proposed cutting force model can contribute to real-time machining process monitoring, cutting parameters optimization and ensuring machining quality.

OPTIMASI PARAMETER PEMOTONGAN CNC WET MILLING TERHADAP KEKASARAN PERMUKAAN STAINLESS STEEL AISI 304

Optimization of CNC wet milling cutting parameters for stainless steel AISI 304 was successfully attempted by using Taguchi method (S/N ratio and ANOVA). L9 orthogonal array is utilized for designing the experiments with 3 levels of spindle speeds and feed rates with an increment depth of cut. The effect of cutting parameters on surface roughness was examined. According to S/N ratio’s response table, the result obtained for spindle speed fervently indicates that spindle speed is the most pivotal factor for impacting the surface roughness than feed rate for stainless steel AISI 304. ANOVA results depicted that the contribution percentage of spindle speed for surface roughness is higher than feed. Optimum values of cutting parameters were endowed at a spindle speed of 3184 rpm and a feed rate of 1528,4 mm/min. The experimental values at optimum cutting condition were compared with predicted values and it reveals a significant congruity with the experimental results with a low error percentage. Therefore, this experiment aims to develop the productivity and quality of CNC milling operations for stainless steel AISI 304. In addition, number of studies for cutting parameter optimization stainless steel AISI 304 was not barely done yet. Hereafter, more investigations still needed to optimize the cutting parameters of this stainless steel AISI 304 for various machining operations specifically on CNC milling process.

Simulation analysis of titanium alloy micro-drilling based on thermo-mechanical coupling

With the increasing demand for high precision micro parts in machining field, the development of micro hole drilling technology, especially the finite element simulation technology is rapidly promoted. However, there are many limitations in the simulation of micro-drilling, such as element size, computational efficiency, chip forming, etc. In the present work, a finite element software, Abaqus has been used to simulate the thermal-mechanical coupling micro-drilling process of titanium alloy material, and the key technologies, such as twist bit modeling, material constitutive model, chip separation criterion and element division, were investigated. Through simulation, it was found that with the increase of rotational speed and the decrease of feed speed, chip shape gradually fragmented, in addition, thrust force and torque diminished. Since chip shape, thrust force and torque are important factors affecting drilling quality and tool life, the work could offer important guiding significance for cutting parameter optimization.

Improving Sustainability Index of Grey Cast Iron Finish Cutting Through High-Speed Dry Turning and Cutting Parameters Optimization Using Taguchi-Based Bayesian Method

Improving Sustainability Index of Grey Cast Iron Finish Cutting Through High-Speed Dry Turning and Cutting Parameters Optimization Using Taguchi-Based Bayesian Method

Effect of surface roughness on stress concentration factor of machined surface and cutting parameter optimization in boring of Al-Si piston alloy

Effect of surface roughness on stress concentration factor of machined surface and cutting parameter optimization in boring of Al-Si piston alloy