Computer Numerical Control Milling Machine Research Articles

Virtual instrumentation-based data collection and analysis for CNC machining process

Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

Comparative investigation and optimization of cutting tools performance during milling machining of titanium alloy (Ti6Al4V) using response surface methodology

The purpose of this paper is to study the optimization of the cutting performance of three different cutting inserts, during the machining operation of titanium alloy (Ti6Al4V) by making use of the response surface methodology (RSM) on a computer numerical control (CNC) milling. The cutting tools employed for the optimisation of the cutting performance during machining operation are silicon, aluminium, oxygen, nitrogen (SiAlON), cubic-boron nitride and carbide cutting inserts. Scanning electron microscope (SEM) was used for the determination of the tool wear for the cutting inserts being compared during machining of Ti6Al4V, and the cutting parameters, which are cutting speed (Vc), feed per tooth (fz) and depth-of-cut that were evaluated from the cutting tools as per the manufacturer’s design specifications. The determination of the tool wear on the cutting inserts was achieved by using the SEM, while the machining operation for the experimental trails was performed from the CNC milling machine, where face milling operation was executed. The optimization process showed that carbide cutting inserts yielded the best performing results and were considered the most significant choice of cutting insert in machining Ti6Al4V when compared to SiAlON and CBN cutting inserts. This choice was from the cutting tool life obtained where a cutting tool life of 29 min was obtained from a use of carbide cutting inserts; 28 min resulted from a use SiAlON cutting inserts and 26 min from a use of CBN cutting inserts. This work finds appropriate value in assisting the machinists in the selection of the best most performing and cost-effective cutting tool.

Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling

Computer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP’s setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA).

Roughness Test of Aluminum A5052 before and after Forming by SPIF Process

Single point incremental forming process, which is also known as SPIF, that forms from one direction. SPIF process was used for milling an aluminum metallic sheet. In this paper, an aluminum (A5052) sheet was formed in a conical shape by the SPIF process. The A5052 sheet was deformed by the computer numerical control (CNC) technology in the T60 series milling machine. The conical shape was deformed by the spiral toolpath in the T60 series milling machine. At the surface of the A5052, lubricant grease was continuously applied manually for reducing the friction generated due to milling. Also, for decreasing the tool rotational force friction on the A5052 sheet there was the use of lubricant. Moreover, 0.1mm step-down is used for the tool path because it takes more time for forming a metallic sheet, however, it deformed the sheet smoothly. The 0.5mm step-down g-code was extracted from fusion 360. Then the metallic sheet A5052 surface was compared before and after deformation. For testing a metallic sheet A5052 the formability parameter was analyzed in the experiment part. The experiment was performed to find the optimal smooth surface from the CNC milling machine. The initial roughness value and the microscopic image were presented in the investigational part. Likewise, the comparison of points for the highest and lowest rough point was further discussed and compared throughout the experiment.

Increasing the Performance of Computer Numerical Control Machine via the Dhouib-Matrix-4 Metaheuristic

The Computer Numerical Control (CNC) machine represents a turning point in today's production which has high requirements for product accuracy. The CNC machine enables a high flexibility in work and time saving and also reduces the time required for product accuracy control. Moreover, the CNC machine are used for several activities, most often for turning, drilling and milling operations. Usually, the productivity of any CNC machine can be increased thanks to the minimization of the non-productive of tool movement. In this paper, the results of a new metaheuristic named Dhouib-Matrix-4 (DM4) with an application on the NP-hard problem based on the Travelling Salesman Problem are presented. DM4 is used for increasing the performance of the CNC Machine by optimizing a tool path length in the drilling process performed on the CNC milling machine. The proposed algorithm (DM4) achieves a solution closed to the optimum, compared with the results obtained with the Ant Colony Optimization algorithm and the results found with the manual programming in G code by using a control unit for the selected CNC milling machine.

Effects of CNC Processing Parameters on Surface Quality of Wood-Based Panels Used in Furniture Industry

The processing of wood-based panels such as plywood, particleboard and fiberboard, which are widely used in the furniture industry, with CNC (Computer Numerical Control) milling machines has been increasing recently. Even though CNC milling machines have many advantages for furniture producers, it is difficult to set process parameters to obtain the desired surface quality of the material. Therefore, it is necessary to determine the most suitable of these parameters for the surface quality of each wood-based panel. This study aimed to determine the effects of processing parameters on the surface quality of plywood, particleboard and medium density fiberboard (MDF) panels processed in CNC milling machines. Furthermore, the average surface roughness values of these panels were compared after CNC processing. Three spindle rotational frequencies (10.000, 14.000 and 18.000 rpm), three feed rates (5, 7, and 9 m/min) and two cutting tool diameters (2 and 5 mm) were selected as CNC processing parameters. To determine the surface quality of wood-based panels, the surface roughness measurements were performed according to DIN 4768 standard and three surface roughness parameters (Ra, Rmax and Rz) were determined. According to the results of this study, it can be concluded that the surface roughness values of wood-based panels decreased with increasing spindle rotational frequency and feed rate, while they increased with increasing cutting tool diameter. Among the wood-based panels used in this study, the lowest average roughness values were obtained for plywood samples.

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

Experimental Investigation of Face Milling Surface Study on AA1100 by Using VMC

Abstract: Aluminum alloys are extensively used as a main engineering material in various industries such as automotive industries, the mould and die components manufacture and the industry in which weight is the most important factor. Surface roughness is an important measurement of product quality since it greatly influences the performance of mechanical parts as well as production cost. The purpose of this research was to investigate the effect of the main factors of the surface roughness in aluminum semi-solid AA1100 face milling. The results of the research could be applied in the manufacture of automotive components and mold industries. This study was conducted by using computer numerical controlled milling machine with 40- millimeter diameters fine type carbide tool with twin cutting edge. The experimental results were evaluated using Taguchi technique. Machining timing, and Flatness were mainly influenced with feed. MRR was mainly influenced with DOC. Practically minimum flatness error obtained at low speed with maximum feed and depth of cut. During this operation very low surface finish 0.520 µm generally it can achieve cylindrical grinding process. Keywords: AA 1100, face milling, Ra, CMM, flatness error

Modelling of spindle energy consumption in CNC milling

In manufacturing industries, machine tools are frequently used and required a lot of energy to work. Spindle acceleration is a common process when machine tools are in use. It generates a high-energy intensive power peak. The total energy consumption of machine tools in the machining process is strongly affected by these high-power peaks of short duration. Many researchers have overlooked the energy consumption of spindle acceleration resulting into inaccuracies in the prediction of overall energy consumption of machine tools. Therefore, the present study aims to develop a model to predict the spindle acceleration energy consumption of computer numerical control (CNC) milling machines. The proposed model is based on the principle of spindle motor control and includes the computation of moment of inertia of the spindle drive system. To validate the effectiveness of the proposed model, machining experiments are carried out on a CNC milling machine. Without performing time-consuming experiments, the proposed models can be utilized to estimate the power, time, and energy consumption of spindle acceleration. The proposed model helps to determine total energy consumption during machining process correctly.

Optimisation of cutting parameters of new material orthotic insole using a Taguchi and response surface methodology approach

The milling process on a computer numerical control (CNC) machine is a crucial operation in the development of the modern manufacturing industry. Milling is the operation of peeling workpieces on machine tools that are often used in the manufacturing industry. The quality of the machining result is generally based on the surface roughness (Ra). The two main factors that have a significant effect on Ra are the cutting conditions and the irregularity of the machining process. A good surface quality is necessary to determine the optimal machining parameter settings for the resulting Ra to be truly low and as optimal as possible. Engineers can obtain this condition through a fair and correct optimisation technique. Optimisation techniques can be used to obtain the best results under certain scenarios. A statistical method approach such as the design of experiment by combining the Taguchi method and the response surface methodology (RSM) is an efficient method of obtaining optimal responses.An orthogonal array L18 2 × 34 was used in this study to select the optimal cutting parameters to manufacture an insole using a CNC milling machine. The Taguchi method was used to obtain a combination of cutting parameters, while the RSM was used as an optimisation technique to obtain the optimal Ra value through the formation of a second-order regression model.The research results indicate that the optimal cutting parameter conditions in the insole manufacturing process in a CNC milling machine with OA L18 2 × 34 are the spindle speed 12,500 rpm, step over 0.15 mm, feed rate 850 mm/min, and the Raster 90 toolpath strategy using the material type X. The optimal conditions were determined to obtain an Ra value of 5.328 μm.

Internet of Things Based Cutting Tool Status Monitoring in a Computer Numerical Control Milling Machine

The Internet of Things (IoT) in the manufacturing industry shares machine data in real time. The majority of industrial data can be gathered and processed from machines and other remote IoT devices in a production system through data streaming. As sensors become smaller and more affordable, the Internet of things will attract more attention. It is possible to employ a wide range of sensors for monitoring, and with efficient open-source software, the status of the operation can be evaluated effectively. Our work aims at providing instructions for sending data (cutting tool status time) from Particle Photon devices connected to CNC milling machines to open-source software called ThingSpeak. This task is accomplished by integrating the Particle Photon device with the infrared sensor to the CNC milling machine. Three axis CNC vertical milling machine is used to manufacture the sample component. The status of the cutting tool, in this case the cutting time for each machining feature is monitored. Using infrared technology, the sensor detects whether the cutting tool is present. Particle photons measure machining time and communicate it to ThingSpeak. The ThingSpeak library will interpret the cloud information and displays it. With ThingSpeak software, we define the fields for various cutting tools and track every tool in real time. The time recorded using ThingSpeak software is found to be near to the time, which is monitored manually by the user. A major step in computer assisted process planning in manufacturing is monitoring the machining time for each cutting tool and it is successfully implemented in this research work.

STEP-NC Compliant Intelligent CNC Milling Machine with an Open Architecture Controller

A STEP-NC or ISO 14649 compliant machine controller is developed, using Open Architecture Control technology for a three-axis Computer Numerical Control milling machine in this research. The controller is developed on a Raspberry Pi single-board computer, using C++ language. This new development is proposed as a low-cost alternative to ISO6983 standard, ensuring continuous integration in the CAD/CAM/CNC chain in machining; thus, it broadens the spectrum of problems handled by conventional CNC systems. The new machine controller is intelligent enough to extract geometrical and manufacturing parameters, cutting tool data, and material data from the STEP-NC file. Accordingly, tool paths for machining can be generated in the controller itself. The shop floor level modification of parameters and the possibility of regeneration of new toolpaths is an added advantage of this new controller. The modified or improved version of the STEP-NC file can be sent back to the CAD/CAM system to close the CAD/CAM/CNC chain. Machine condition monitoring can be achieved by connecting sensors through an available slave I/O board. In the present development, the current drawn by each servo motor is fed back to the controller for cutting condition monitoring. A laboratory scale three-axis CNC milling machine is developed to test the performance of the newly developed controller. The accuracy of positioning, perpendicularity of axes and linearity of this machine are experimentally verified through standard tests. The STEP-NC compliance of the controller is tested and verified, using a STEP-NC program derived based on a sample program given in ISO 14649 standard.

Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm.

Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.

Development of Accuracy Evaluation Method for Open Loop Educational CNC Milling Machine

<p>The open-loop educational computer numerical control (CNC) milling machine requires accuracy verification in order to ensure its accuracy and functionality. In this paper, the new verification method is proposed based on the length measurement using the fringe counting of He-Ne laser interferometry. The ideal translation length is defined by the number of electric pulses generated by the micro-stepping driver, while the actual length is measured using He-Ne laser interferometry. In the experimental process, the data of the fringe pattern of the He-Ne laser and the number of electric pulses which drive the stepper motors were simultaneously acquired using an oscilloscope. Hence, the data has been analyzed to obtain the ideal and actual lengths by using an in-house program developed in Python. By comparing the actual translation and ideal length, the accuracy of the educational CNC milling machine has been evaluated to be 50 µm for the length up to 200 mm.</p>

5-Axis CNC Micromilling for Rapid, Cheap, and Background-Free NMR Microcoils.

The superior mass sensitivity of microcoil technology in nuclear magnetic resonance (NMR) spectroscopy provides potential for the analysis of extremely small-mass-limited samples such as eggs, cells, and tiny organisms. For optimal performance and efficiency, the size of the microcoil should be tailored to the size of the mass-limited sample of interest, which can be costly as mass-limited samples come in many shapes and sizes. Therefore, rapid and economic microcoil production methods are needed. One method with great potential is 5-axis computer numerical control (CNC) micromilling, commonly used in the jewelry industry. Most CNC milling machines are designed to process larger objects and commonly have a precision of >25 μm (making the machining of common spiral microcoils, for example, impossible). Here, a 5-axis MiRA6 CNC milling machine, specifically designed for the jewelry industry, with a 0.3 μm precision was used to produce working planar microcoils, microstrips, and novel microsensor designs, with some tested on the NMR in less than 24 h after the start of the design process. Sample wells could be built into the microsensor and could be machined at the same time as the sensors themselves, in some cases leaving a sheet of Teflon as thin as 10 μm between the sample and the sensor. This provides the freedom to produce a wide array of designs and demonstrates 5-axis CNC micromilling as a versatile tool for the rapid prototyping of NMR microsensors. This approach allowed the experimental optimization of a prototype microstrip for the analysis of two intact adult Daphnia magna organisms. In addition, a 3D volume slotted-tube resonator was produced that allowed for 2D 1H-13C NMR of D. magna neonates and exhibited 1H sensitivity (nLODω600 = 1.49 nmol s1/2) close to that of double strip lines, which themselves offer the best compromise between concentration and mass sensitivity published to date.

A virtual educational model on a CNC milling machine including and excluding two methods of fuzzy controllers

Purpose Training centers and labs offer many applications suitable for beginners who want to know how to set and operate a computer numerical control (CNC) milling machine. However, few applications address a basic understanding of the machining process founded on mathematical principals in line with new high-speed and high-precision machining technologies. The purpose of this paper is to present a complex mechanism in a simplified way, explaining the subject at an elementary level. Design/methodology/approach The authors have developed an application of the CNC milling machine in a Matlab/Simulink package, obtaining the appropriate parameters mathematically. The project developed an analytical method using Matlab code to test the step response (the actual cutting force) under various parameters to ensure comparability of the designed model. The analytical results are in line with the developed model. The Matlab/Simulink user interface allows the application to better explain machining for educational purposes. Furthermore, by combining this mathematical model and the fuzzy controller, the high-speed constant-force milling control model has a user interface for data entry. The addition of two kinds of fuzzy controllers (look-up table and Mamdani) achieve a more educational environment compared with existing models. Findings The developed technique can be used in CNC milling machine centers and laboratories. For virtual training purposes, this paper provides a two-stage educational model, giving students the necessary feedback on what they have learned at each stage from the beginning use of the CNC milling machine, with and without the controller. The system also offers to track the step-response analysis method. This method overcomes the shortage of milling processes modeled by the traditional transfer function, which more accurately establishes the relationship between cutting force and cutting parameters. Practical implications This technique can be used in the CNC machine centers and laboratory for teaching beginner students and trainees. Real data from the workshop had been used. Originality/value The earlier versions of this manuscript were presented in: JVE International LTD. Vibroengineering Procedia. +2017. 14.; IEEE 4th International Conference on Information Science and Control Engineering (ICISCE) +2017.

Research on the application of multiplication dimension reduction method in global sensitivity analysis of CNC machine tools

Three-axis computer numerical control (CNC) milling machines have a wide range of application in practical processing. The error transfer principle of machine tools is an important factor to improve the precision of machine tools. Based on multibody system theory and homogeneous transformation matrix theory, a geometrical error model of three-axis CNC milling machines is proposed in this paper. In addition, the global sensitivity analysis of machine tool errors was carried out to identify important errors. The analysis model has two layers of higher dimensional integration; hence, in the aim of solving this issue, a multiplication dimension reduction method was used in this paper. By this means, a one-dimensional integration model was obtained, and sensitivity analysis of machine tool geometric errors was realized. The present work provides a good theoretical basis for the optimization design and error compensation of machine tools.

Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

In this study, a tool path optimization problem in Computer Numerical Control (CNC) milling machines is considered to increase the operational efficiency rates of a company. In this context, tool path optimization problem of the company is formulated based on the precedence constrained travelling salesman problem (PCTSP), where the general form of the TSP model is extended by taking the precedence of the tool operations into account. The objective of the model is to minimize total idle and unnecessary times of the tools for internal operations. To solve the considered problem, a recent optimization algorithm, called Satin Bowerbird Optimizer (SBO), is used. Since the SBO is first introduced for the global optimization problems, the original version of the SBO is modified for the PCTSP with discretization and local search procedures. In computational studies, first, the performance of the proposed algorithm is tested on a well-known PCTSP benchmark problems by comparing the proposed algorithm against two recently proposed meta-heuristic approaches. Results of the comparisons show that the proposed algorithm outperforms the other two competitive algorithms by finding better results. Then, the proposed algorithm is carried out to optimize the hole drilling processes of three different products produced by the company. For this case, with up to 4.05% improvement on the operational times was provided for the real-life problem of the company. As a consequence, it should be noted that the proposed solution approach for the tool path optimization is capable of providing considerable time reductions on the CNC internal operations for the company.

A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy

Surrogate models are often used as alternatives to considerably reduce the computational burden of the expensive computer simulations that are required for engineering designs. The development of surrogate models for complex relationships between the parameters often requires the modeling of high-dimensional functions with limited information, and it is challenging to choose an effective surrogate model over the unknown design space. To this end, the ensemble models—combined with different surrogate models—offer effective solutions. This paper presents a new ensemble model based on the least squares method, which is a regularization strategy and an augmentation strategy; we call the model the regularized least squares ensemble model (RLS-EM). Three individual surrogate models—Kriging, radial basis function, and support vector regression—are used to compose the RLS-EM. Further, the weight factors are estimated by the least squares method without using the global or local error metrics, which are used in most existing methods. To solve the collinearity in the least squares calculation process, a regularization strategy and an augmentation strategy are developed. The two strategies help explore the unknown regions and improve the accuracy on one hand; on the other hand, the collinearity can be reduced, and the overfitting phenomenon that may occur can be avoided. Six numerical functions, from two-dimensional to 12-dimensional, and a computer numerical control (CNC) milling machine bed design problem are used to verify the proposed method. The results of the numerical examples show that RLS-EM saves a considerable amount of computation time while ensuring the same level of robustness and accuracy compared with other ensemble models. The RLS-EM used for the CNC milling machine bed design problem also shows good accuracy characteristics compared with other ensemble methods.

A General Empirical Energy Consumption Model for Computer Numerical Control Milling Machine

Energy consumption of computer numerical control (CNC) machines is significant and various empirical models have been developed to model the specific energy consumption (SEC) of CNC machines. However, most of the models are developed for specific machines and hence have limited applications in manufacturing industry. In this research, a general empirical SEC model for milling machine at certain power level is developed based on actual cutting experimental data. In this model, stand-by power and spindle power are used in the SEC model for the first time. The material removal rate (MRR) is used to represent the cutting parameter. The proposed model is fitted by the regression analysis and validated using experimental data. Results show that the proposed model can be applied on various milling machines with an average absolute residual ratio of 6%. The model is also validated through a series of cutting experiments on a machine center, with an accuracy of 91.5%, for the SEC calculation.