Numerical Control Machine Research Articles

Characterization and optimization analysis on surface finish and energy consumption in turning of Al-4032/GMP MMC produced by stir casting

Control of energy expenditure in computer numerical control (CNC) machining is needed for eco-friendly operation of machining operations. Production industries deeply emphasize the improvement of productivity and saving in cost. The aluminum-based metal matrix composites (AMCs) can be substituted for metals for their mechanical properties in several applications. However, to bring in composite to usage, generally demand certain machining operation, which is quite challenging. This paper presents fabrication, characterization, and machining (turning) of Al-4032/6%GMP (granite marble powder) composite. Optical micrographs (OM) and scanning electron microscopy (SEM) with energy dispersive x-ray analysis (EDAX) have been used for the portrayal of the composite microstructure, surface topography, and fracture behavior. Also, mechanical characterization (tensile strength, micro-hardness, and impact strength) of the fabricated AMC has been carried out. The AMC samples have been examined for machinability for CNC turning operation. Cutting speed, feed rate, and depth of cut are the cutting parameters selected for the machining experiments. The response surface methodology (RSM)-based desirability function has been used to obtain the best combination of parameters for achieving the desired objectives. Experimental results demonstrate the built-up edge (BUE) formation on cutting insert and interfacial bonding of particles on workpiece surface at low cutting speed. Generally, smooth cutting operation has been observed at cutting speeds above 100 m/min. Best surface finish (1.38 μm) and minimum energy consumption (0.27 kWh) have been observed at cutting speed 176 m/min, feed rate 0.15 mm/rev, and depth of cut 0.45 mm.

Intelligent Single-Board Computer for Industry 4.0: Efficient Real-Time Monitoring System for Anomaly Detection in CNC Machines

The Industry 4.0 technology relies on Single-board computers and the Internet of Things (IOT) and Machine Learning (ML). In addition, sensory detectors, controllers, and a communication interface are employed to address the demands of distant supervision and operational management. In today's industrial environment, understanding machines and giving effective interpretation and prognostics is a challenging issue. This paper presents an effective on-process identification tool for monitoring and advising the operator based on sensor parameters. The data is analysed with the Fast Fourier Transform (FFT) inference and machine learning strategies to detect the production calibre of an industrial Computer Numerical Control (CNC) machine, including vibration, temperature, humidity, and operating temperatures. The vibration parameter is provided to the FFT algorithm to produce frequency, and the diameter dataset is also provided manually from the hole diameter in the job piece to correctly monitor and inspect the product quality to prognostics to the fault in machines. Improper machine settings cause varied vibrations and changes in parameters, whereas our Industry 4.0 module detects and warns about faulty parameters. The device is put through its trials using three distinct machine learning approaches, and the results are collected. ML combines the outcomes of many baseline estimators to provide better results. This research work utilizes the Linear regression model since it has a high-power detection ability and minimizes variance as well as bias. The Single board computer using FFT and linear regression monitoring gives greater data accuracy of 97.6 percent on comparing the outcomes of 5.4%improved efficiency than K-Nearest Neighbourhood (KNN), 95.5% Random Forest Network (RN), and 95% Support Vector Machine (SVM) algorithms. The proposed system was validated via the deployment of suitable test scenarios, illustrating the technique's effectiveness in manufacturing environments.

Feature Points Recognition of Computerized Numerical Control Machining Tool Path Based on Deep Learning

In the processes of feed rate planning, interpolation and tool path optimization of Computerized Numerical Control (CNC) machining, feature point recognition of tool path is an essential operation. Accurate identification of feature points is the premise for partitioning the tool path that affects the accuracy and efficiency of CNC machining. Current research on feature point recognition of tool path mainly relies on the hand-crafted approach, of which the result is sensitive to the values of some manually defined threshold. Therefore the approach heavily relies on the human experience. In this paper, a novel deep learning-based approach is presented that can automatically and precisely recognize the feature point of the tool path. A set of geometric descriptors is first defined for each Cutter Location (CL) point and then conversed to images that feed to the deep learning pipeline. A residual learning-based Convolutional Neural Network (CNN) called Feature Point CNN (FP-CNN) is designed that takes the conversed images as input and the recognized results as output. Extensive experiments on some industrial parts are conducted to validate the effectiveness and the advantage of the proposed network. Results show that the proposed approach has good performance in identifying accuracy and recall, which is much superior to two types of benchmarks and does not involve any human intervention.

Identification of the model parameter for milling process simulation with sensor-integrated disturbance observer

This study presents a novel parameter identification method using a disturbance observer for computer numerical control (CNC) machine tools with ballscrew-type feed drive systems, which are generally adopted in industrial applications. In the proposed method, the disturbance force acting on the feed drive system was estimated using a multi-inertial disturbance observer using both CNC servo and accelerometer signals. Model-based milling simulation is performed with reference to the estimated disturbance forces, and hence model parameter identification can be systematically achieved. In the milling process model, the structural vibration and cutting forces caused by machine-process interactions are considered. Furthermore, the model is considered the disturbance forces transmitted to the feed drive system via a mechanical structure. To verify the proposed method, a basic end-milling experiment was conducted. It was confirmed that the multi-inertial disturbance observer assisted by the accelerometer is equivalent to the dynamometer in terms of the disturbance force estimation within the frequency range of 600 Hz and less. The model parameters identified using the proposed method were almost identical to those obtained using conventional methods. However, the identification results of the modal parameters and tool eccentricity were slightly different from those estimated using the conventional methods. The milling simulation using the identified parameters agreed with the measurement results obtained using the dynamometer. Hence, inverse analysis of the in-process data can effectively improve the identification accuracy of the model parameters.

An Improved Robust Thermal Error Prediction Approach for CNC Machine Tools

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, the adaptive LASSO method enjoys the oracle property of selecting temperature-sensitive variables. After the temperature-sensitive variable selection, the XGBoost algorithm is further adopted to model and predict thermal errors. Since the XGBoost algorithm is decision tree based, it has natural advantages to address the multicollinearity and provide interpretable results. Furthermore, based on the experimental data from the Vcenter-55 type 3-axis vertical machining center, the proposed algorithm is compared with benchmark methods to demonstrate its superior performance on prediction accuracy with 7.05 μm (over 14.5% improvement), robustness with 5.61 μm (over 12.9% improvement), worst-case scenario predictions with 16.49 μm (over 25.0% improvement), and percentage errors with 13.33% (over 10.7% improvement). Finally, the real-world applicability of the proposed model is verified through thermal error compensation experiments.

Application of Generative Adversarial Network and Diverse Feature Extraction Methods to Enhance Classification Accuracy of Tool-Wear Status

The means of accurately determining tool-wear status has long been important to manufacturers. Tool-wear status classification enables factories to avoid the unnecessary costs incurred by replacing tools too early and to prevent product damage caused by overly worn tools. While researchers have examined this topic for over a decade, most existing studies have focused on model development but have neglected two fundamental issues in machine learning: data imbalance and feature extraction. In view of this, we propose two improvements: (1) using a generative adversarial network to generate realistic computer numerical control machine vibration data to overcome data imbalance and (2) extracting features in the time domain, the frequency domain, and the time–frequency domain simultaneously for modeling and integrating these in an ensemble model. The experiment results demonstrate how both proposed modifications are reasonable and valid.

Thermal error modeling of electric spindle based on particle swarm optimization-SVM neural network

High-speed electric spindle is an important part of computer numerical control (CNC) machining equipment. The thermal displacement generated by the electric spindle during operation is the main reason that affects the machining stability and machining accuracy of the electric spindle. Compensating the thermal error of the high-speed electric spindle can effectively improve the CNC machining. Improve equipment processing performance. Therefore, it is particularly important to establish the accuracy of the thermal error prediction model. Taking the A02 high-speed electric spindle as the research object, ANSYS is used to analyze the thermal characteristics of the electric spindle, and the temperature and thermal displacement monitoring points of the electric spindle are arranged according to the simulation results, and the temperature and thermal displacement data of the monitoring points under different rotational speeds are collected; using K-means to classify temperature measurement points, uses the gray relation analysis degree to determine the correlation between the temperature measurement point and the thermal displacement data, and selects 4 temperature-sensitive points from 10 temperature measurement points. Finally, particle swarm optimization (PSO) is used to optimize the penalty factor and kernel function of support vector machine (SVM), and the PSO-SVM prediction model is established to compare with the neural network prediction model of SVM and genetic algorithm (GA) optimized SVM. The results show that PSO-SVM has better robustness, stability, and generalization ability.

Mathematical model and simulated annealing algorithm for setup operator constrained flexible job shop scheduling problem

In the vast majority of the published article on flexible job shop scheduling problems (FJSP), machines are the only resources with limited capacities. There are also a sizable number of research articles in FJSP in which workers (machine operators) are constraining resources in addition to machines. In those articles, a worker performs the sequential steps of the production process and must stay with a machine. However, we argue that with the increasing adoption of numerically controlled machines with self-controlling capabilities, operators become machine tenders rather than individuals performing the sequential steps of the production process. Hence, the assumption of machine tenders as constraining resources that can result in the idling of expensive numerical controlled machines cannot be justified. Moreover, the replacement of machine tenders with automation and robotics increasingly becomes standard practice. In contrast, skilled setup operators remain critical and constraining resources since automating their tasks cannot be easily achieved. This paper proposed a mathematical model for a new setup operator constrained flexible job shop scheduling problem (SOC-FJSP) where setup operations are assumed to be anticipatory (detached). Contrary to a machine tender, a setup operator needs to stay with the machine only while performing setup. Once setup is completed, a setup operator becomes free and available to perform setup operations on a different machine. The assumption of a setup being detached from operations allows the overlapping of a setup operation of a job with the setup and processing of its preceding operation, enabling makespan reduction and better utilization of machine tools and setup operators. To solve the proposed mathematical model, we develop a simulated annealing (SA) algorithm. We further expand the model and the algorithm to account for sequence-dependent setup time and workload balancing among the setup operators. Extensive numerical studies were conducted to illustrate the various attributes of the proposed mathematical model and the convergence behavior of the proposed algorithm.

Flow determination via nanoparticle strain sensors for easy Lab on Chip integration

With the advancement of microfluidic and Lab on Chip systems, new challenges arise regarding efficient system monitoring as well as the detailed tracking of fluids within the system’s microfluidic channels. In this paper, a strain-sensing module offering facile integration with most microfluidic-systems is used for the determination of flow in a microfluidic chip. The sensor is based on platinum nanoparticle networks that are produced via a modified-sputtering technique; the nanoparticles self-assemble on top of flexible polyolefin substrates that also serve as the sealing layer of the microfluidic channels (fabricated on printed circuit board substrates or PCBs, by means of milling and computer numerical control machining). By introducing varying flow rates, pressure within the microfluidic channel increases thus resulting in the straining of the polyolefin layer and in modifying the resistance of the module. Numerical calculations, which take into account the bidirectional connection of fluid flow with the deformation of the sealing layer, are used to extract the channel geometry maximizing the sealing layer strain. Flow determination or flow-sensing experiments have also underlined the importance of polyolefin to PCB bonding-strength, which was greatly improved by oxygen plasma treatment. The module is able to detect flow rates as low as 5 μL/min, corresponding to a strain of 0.00337%, and showing a sensitivity of 0.021 (μL/min)−1, showcasing a) a competitive limit of detection (LoD), and b) low-cost, low-power requirements and easy integration with existing microfluidic systems, either as an autonomous unit or by integrating it on its sealing material.

Advances in Adaptive Scheduling in Industry 4.0

The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.

Accurate vibration-free robotic milling electric discharge machining

Traditional computer numeric control (CNC) machines have high accuracy but are limited by workpiece size and axes. Therefore, many researchers attempted to equip six-axis industrial robots (IRs) with milling end-effectors to explore the robot’s large and flexible working envelope. However, IRs lack stiffness and have limited force, resulting in low accuracy, poor surface roughness (SR), and low material removal rate (MRR). On the contrary, electric discharge machining (EDM) is a non-conventional process capable of shaping complex profiles in any conductive material. Since the EDM process has no physical contact between the electrode and the workpiece, it can machine diverse hard-to-cut materials accurately and free of vibration. However, to this day, EDM is found in limited conditions of CNC machines only. Thus, this research investigates a synergistic combination of IR and milling EDM. Adopting advanced CAM tools and offline programming, it examines pre-designed electrodes working analogue to conventional milling to perform the desired machining by intricate cutting paths. The results deliver a robotic machining technique able to cut hard materials using small industrial robots yet free of vibration and not pose dependent.

Integrative design and processing of a conformal optical window pair in a cylindrical isolator.

In order to perform the flow visualization of a shock train structure by the schlieren imaging method in the cylindrical isolator, to the best of our knowledge, a novel integrative design and processing scheme of an aluminum alloy pipe with an acrylic conformal optical window pair are proposed. The optical ray tracing and wavefront correction methods were applied to design the inner cylindrical surfaces and outer aspherical cylindrical surfaces of the optical window pair for parallel light correction based on the conjoint analysis with the processing capability. Under the tolerance analysis and the optimization of the machining path, the integrative model was fabricated on a three-axis computer numerical control machine using two-axis turning and fast tool servo machining. The wavefront aberration (peak-to-valley value) and wavefront aberration (RMS) of the optical window pair were corrected within 12.189 and 2.658λ (λ=632.8nm) in the observation area which met the requirements of high-precision schlieren observation.

Methodology to eliminate errors in machining processes through Augmented Reality Applications

Today, precision machining companies must implement new technologies to meet the requirements of the aerospace and automotive industries to maintain their competitiveness in the market and create new business opportunities. It has currently been observed that machining companies face errors in their processes that affect the quality of the products, the operation of the process, the useful life of the machines, and the tools. This research project aims to develop a methodology to reduce errors in Computerized Numerical Control machining (CNC) processes with Augmented Reality Applications (AR-Apps) that help operators interpret manufacturing drawings and assembly operations optimize Setup operations and ensure the reliability of the machining. It begins with a questionnaire applied to a sample of machining companies from Chihuahua Mexico City. To identify the factors that generate critical errors in the machining processes. The main results obtained in this paper are reduction of downtime, elimination of scrap, increment cutting tools life, optimize the machining process, increase productivity, and improved product quality. AR -apps implementation offers a low-cost alternative solution that operators and technicians can use very easily because it is a powerful tool that helps work more efficiently through virtual environments, reducing errors and optimizing the process of machining.

Design and research of digital twin machine tool simulation and monitoring system

The numerical control machine tool is an important part of manufacturing. The speed and accuracy of the machine tool simulation and the monitoring of machine physical model movement, model collision, and operation health are the most common bottlenecks. The previous studies on these problems are discrete and poor in real time. In this paper, combined with the digital twin (DT) technology, a perception-monitor-feedback system architecture is constructed. Based on the improved Gilbert–Johnson–Keerthi (GJK) distance algorithm, the collision information of tool and machine tool can be better detected during the simulation and monitoring of machine tool movement, and the more real workpiece shape can be displayed. Based on the model synchronous motion driven by the production perception data and its potential information extraction, the tool wear online monitoring is carried out. The tool wear online monitoring is performed based on the production perception data–driven model synchronous movement and extraction of its potential information. Finally, the usability and efficiency of the system are verified by an example of a typical shaft part processed by a numerical control machine tool.

Combined strategy for tuning sensor-less brushless DC motor using SEPIC converter to reduce torque ripple

The brushless DC motor (BLDCM) is widely used in computer numerical control (CNC) machines, aerospace applications and auto industry applications in the field of robotics. But it is still affected by the transmission torque ripple, which mostly depends on the speed and the transient line current at the transmission interval. This manuscript proposes a combined approach for tuning sensor-less brushless DC (BLDC) motors using a single-ended primary-inductor converter (SEPIC). The proposed technique is a combination of Golden Eagle Optimization (GEO) and Radial Basis Function Neural Network (RBFNN), hence it is called GEO-RPFNN. The control of speed and torque is to reduce the torque ripple in the motor. Here, the modified bridgeless single-ended primary-inductor converter is proposed to improve speed and torque control. The proposed method is used to adjust the parameters of proportional integral derivative (PID) controller and to improve the performance of PID controller. Therefore, the GEO–RBFNN technique is proposed to recover the control loop function. The proposed algorithm is explored to control the speed and torque error as BLDC motor. Nevertheless, the output of the proposed approach is subject to the input of speed and torque controllers. The proposed method is executed in MATLAB Simulink site. The performance of the proposed system is compared with existing FA and PSO methods. As per the state of comparison outcomes, the GEO–RBFNN gives better result than the existing techniques which has higher ability to conquer the related issues. The THD in stator current, power factor and torque ripple gives the value using proposed method is 1.26%, 0.9951 and 7.4.

Analysis of the Effect of Tool Geometry and Hole Quality on Drilling Carbon-Fibre Reinforced Polymer Composite Using ANSYS

This study focused on the analysis of the effect of tool geometry and hole quality on drilling carbon-fibre reinforced polymer (CFRP) composite using ANSYS. The experiment was conducted on computer numerical control machine (CNC) using high speed steel (HSS) twist drill bit and HSS-COBALT drill type. Taguchi L9 orthogonal array was used for the optimization analysis. Analysis of variance (ANOVA) was employed to determine the most significant control factors affecting the drilling process. The machining parameters involving tool type, point angle, spindle speed and feed rate were selected as control factors. While the dependable variables on workpiece were cutting force, stress and strain. The main interaction effect of the input variables on the predicted responses was investigated. The ANOVA/F-test results for cutting force with the contribution of each machining parameters revealed that the cutting speed was the dominant factor influencing cutting force developed with a contribution of 57 percent, The second factor affecting cutting force developed was point angle with a contribution of 5.7 percent, while f eed rate had the least effect on cutting force developed where its contribution of 1.4 percent. Based on the results observed, the optimal machining parameters that produced minimum stress was observed with HSSCOBALT drill at 118 degrees of point angle, 300 mm/min of feed and 500 rpm of speed. Also from Taguchi results, the optimal combination of process parameters to obtain maximum strain with HSS drill, 135 degrees of point angle, and 300 mm/min of feed and 1500 rpm of speed produced the optimal drilling condition for CFRC polymer within the scope of this research work.

A Genetic Programming-Based Iterative Approach for the Integrated Process Planning and Scheduling Problem

The integrated process planning and scheduling (IPPS) problem is studied in this article, in which operation sequencing, process plan selection, and machine selection are decided simultaneously. For different scenarios, three mixed-integer linear programming (MILP) models are designed. Then, in view of the workload of machines and processing times of jobs, two machine selection techniques are introduced to simplify the optimization of these MILP models. By exploring the structural properties of the MILP models, we put forward a novel lower bound to act as a measurement for the performance of the related algorithms. Considering the real-time requirement and complexity of instances in practice, we design a hybrid greedy heuristic based on a new decision structure of the problem and dispatching rules. Furthermore, in order to create effective dispatching rules to improve the hybrid greedy heuristic, enhanced genetic programming (GP)-based iterative approach is proposed. Experimental results indicate that our approaches are better than other available approaches for the IPPS problem and can reduce the computational time while providing high-quality solutions. <i>Note to Practitioners</i>&#x2014;Due to the widespread use of computer numerical control machines and flexible manufacturing systems in the modern manufacturing industry, the IPPS problem has been widely recognized among both academia and industry over the years. So far, many scholars have put forward several approaches to solve this problem. However, for the instances in practical applications, good solutions are still difficult to get in a reasonable time. The rule-based heuristics, on the other hand, have the advantages of practicability and high efficiency, are much more preferred in practice. Nonetheless, it not only takes plenty of time but requires domain expertise to construct valid dispatching rules for complicated production problems. As an evolution-based machine learning approach, GP has been widely utilized to create dispatching rules for certain scheduling problems. Therefore, in this study, an enhanced GP-based iterative approach is proposed to create dispatching rules for the rule-based heuristic. The computational effectiveness and efficiency of the heuristic based on dispatching rules created by GP are verified in experimental results, and it opens the way for handling the IPPS problem in dynamic environments and multiobjective systems in practice.

Analysis and Design of Cooperative Control Solutions for Electrical Systems of Laser CNC Machines

In order to further improve the collaborative control efficiency of the electrical system of the laser CNC machine tool, an electrical system based on the ultrafast laser CNC machine tool is proposed. The design of the collaborative control scheme is discussed from three aspects: the overall control scheme design, hardware structure design, and control software implementation of the electrical system of the laser numerical control machine tool. The experimental results show that the length of the metal sheet processed by the electrical system of the laser CNC machine tool is 5.3 cm and the width is about 3.6 cm, and the actual processing aperture size will also change according to the set number of variable radius turns, and more precise processing can be enabled in the online test. The research results show that this electrical collaborative control system scheme has strong practicability and feasibility. With the help of system upgrades and optimization, the efficient integration of laser processing technology and numerical control technology can be better achieved. It should be noted that the machining process of the machine tool is also required to make in-depth improvements, which can maximize the operating efficiency and machining accuracy of the electrical system of the CNC machine tool. The laser CNC machine tool researched and developed in this paper verifies the rationality and feasibility of the collaborative control scheme through practical experiments, which can meet the processing requirements and realize the integration of laser processing technology and numerical control technology. High machining efficiency and machining accuracy.

Design of experiments to evaluate the equation to obtain the operating parameters in a 3-axis Numerical Control Machine

This article aims to evaluate the efficiency of the application of equations with which the operating parameters of the manufacturing process are obtained in a three-axis numerical control machine, in order to determine through analysis tests which is the most appropriate option when planning production in machines of this type. The methodology applied to obtain the results is based on the collection and analysis of data generated on a test bench to submit it to a design of 2K experiments, whose response variable is based on the roughness of the surface; using a roughness meter to determine the difference in finishes on a piece of aluminum 6160 T6 and jointly in the machining time; comparing theoretical vs real times. To obtain the comparative analysis, the revolutions per minute and the progress of the cutting tool will be considered as variables, it is expected that this will contribute to identifying the adjustment values that provide better results and standardize the correct use of the equations involved in obtaining the parameters.

Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

Determining the best cutting mode is a common problem for machining processes as well as for CBN (Cubic Boron Nitride) grinding on Computer Numerical Control (CNC) machines. It is even more important when it is necessary to choose a solution that meets many goals, which are in conflict. This paper presents the results of a multi-criteria decision-making (MCDM) study on CBN grinding of cylindrical-shaped parts on CNC milling machines. Three MCDM methods, including TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), and EAMR (Evaluation by an Area-based Method of Ranking) were applied in this work. Besides, MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were used to determine the weights of the criteria. In addition, the Taguchi method with L18 orthogonal array (6^1+3^3) design was used for the design of an experiment, which has four input factors including the depth of dressing cut, the spindle speed, the feed rate, and the wheel diameter. Two criteria, including the surface roughness (SR) and the material removal speed (MRS) were selected as the response outputs. The reason for choosing these two criteria is because SR and MRS are two very important output factors of a mechanical machining process as well as of the CBN grinding process on a CNC milling machine. In particular, these two criteria are always in conflict with each other. Small SR requirements will require small values of the feed speed and the depth of cut. This will lead to the reduction of MRS. From the results of this study, the use of different methods for MCDM was evaluated. In addition, rankings of alternatives have been given according to MCDM methods. Furthermore, the best alternative to guarantee both the minimum SR and the maximum MRS has been found