Computer Numerical Control Machine Research Articles

Effect of aluminium filler concentration on delamination in GFRP composite with optimized machining conditions using ANN-genetic algorithm

Drilling is the most common operation that is performed on the composite material to facilitate the fastening of a composite with other components in an assembly. The present investigation investigates the most significant parameter that affects the quality of the drilled hole in glass fiber-reinforced composites. Epoxy resin is used in the matrix phase. Aluminum powder is used as a filler material in the epoxy matrix to reduce delamination. Laminated composites were fabricated by the hand-layup process with 0%, 3%, and 6 vol% aluminum fillers. Holes were drilled in the composites with the help of a computer numerical controlled drilling machine equipped with a dynamometer. Spindle speeds of 1000, 3000, and 5000 rpm, and feed rates of 50, 100, and 150 mm min−1 were the variables involved in the study. The thrust force and torque experienced during the machining process were measured. An artificial neural network model was executed using python programing language. The data obtained from the model was optimized using genetic algorithm to arrive the optimized machining conditions that would result in least delamination due to drilling.

Optimization of wood machining parameters using artificial neural network in CNC router

This study aims to determine the optimal CNC (Computer Numerical Control) machining conditions using an artificial neural network. For this purpose, Fagus orientalis, Castanea sativa, Pinus sylvestris, and Picea orientalis wood samples at 8%, 12%, and 15% moisture content (MC) were machined on a CNC router in both across and along the grain directions. Based on the experimental data of surface roughness and cutting power analyses, a total of 16 models were used. These were selected in hundreds of models that have the lowest error. The spindle speed, feed rate, and the number of cutter teeth were chosen to be different with the literature based on the length of cutter mark. As a result, optimum machining parameters were determined for each wood MC.

A novel method of key meta-action unit integrated identification for CNC machine tool reliability

There are many parts in CNC (computer numerical control) machine tool. Considering the time and economy, it is impossible to analyse them one by one. In order to reduce the blindness of researchers in choosing analysis objects, it is necessary to identify the key parts of CNC machine tool. MAU (meta-action unit) is the smallest structure that can reflect the function and performance of CNC machine tool, so it is taken as the research object. A new method for key MAU identification of CNC machine tool is proposed, as follows. Firstly, all the meta-action units were obtained by the FMA (function-motion-action) decomposition method, and the harmfulness was selected to analyze the influence of MAU on the reliability of CNC machine tool. Secondly, fuzzy theory is integrated into RPN (risk priority number) to obtain the harmfulness of MAU. Thirdly, the subjective weight of the harmfulness about the MAU was obtained by the combination of MAHP (modified analytic hierarchy process) and optimal sequence diagram method, and the objective weight was obtained by CRITIC (criteria importance though intercrieria correlation) method. Fourthly, the subjective and objective weights were integrated by a linear weighting method, and the result were taken as the comprehensive weight of the harmfulness of MAU. Finally, the harmfulness of the MAU could be obtained by its evaluation value and weight, and the MAU with greater impact on the reliability of CNC machine tool could be obtained by Pareto principle, which is the key MAU. A CNC machine tool made in China is analysised, and the effectiveness of the proposed method is verified by the comparison of various methods and experimental analysis.

Adaptive Backlash Compensation for CNC Machining Applications

The mechanical transmission employed inside the computer numerical control (CNC) machine electromechanical system usually has an inherent backlash. As a position-controlled system is commonly used for the electromechanical system, the backlash limits the performance of the motion control system due to its nonlinearity and discontinuity. This paper proposes an effective method to adaptively detect and compensate for the backlash effect in real time, in which the end-effect load position of the CNC machine is estimated and controlled by the position-controlled servo system, in order to eliminate the influence of backlash on the contour path performance. The simulation results obtained from the model of a realistic CNC machine show the successful elimination of the error between the reference and the end-effector position and a significant improvement in the control system performance.

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

• A new FDD loss function to suppress the noises is designed. • Construct the PCDF module to enhance the robustness of the network. • The unsupervised anomaly detection of machine tools under noises. • The proposed HRCAE performs effective in the CNC machine tool. Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.

Study of machining characteristics of 45C8 carbon steel alloy in CNC turner

The present study evaluates machining characteristics of Carbon Steel Alloy (45C8) by conducting experimental investigation using computer numerical control machining (CNC turner). Carbon Steel Alloy 45C8 Material possesses some excellent advantages like high quality weldability, high corrosion resistance, high strength even at elevated temperature. This alloy is selected for present study as it has multi-dimensional application areas i.e. axles, spindles, automotive components, general engineering components, studs, bolts, shafts, screws, rollers and connecting rods etc. For these applications highly precise machining is necessary. Such needs can be fulfilled by using CNC Machining as it is capable of producing high surface finish and machining complex as well as intricate geometries. The main aim of this study is to machine Carbon Steel Alloy 45C8 using CNC because of above mentioned features of CNC and its suitability for machining 45C8 Material. In this study, the effect of three process parameters i.e. Cutting Velocity, Feed Speed, Depth of Cut on process performance parameter such as Material Removal Rate (MRR) is investigated. A Taguchi based L27 design of experiment (DOE) is applied to determine the effect of significant parameters on machining performance.

Experimental investigation of the effect of sectional airfoil profile deviation on propeller noise

The geometry of the sectional airfoil has a determinative impact on the aeroacoustic characteristics of propellers. However, there are always slight deviations in the practical profiles due to manufacturing tolerance, wear loss, and limitations of processing techniques, which can potentially introduce uncertainties to aeroacoustic measurements. To this end, a systematic investigation is conducted on a benchmark propeller with a diameter of 217.2 mm and several of its variants in an anechoic wind tunnel. The variants are redesigned by modifying the sectional airfoil shapes with varying finite trailing-edge thicknesses. High-accuracy computer numerical control machining is employed to ensure the subtle geometrical differences between the blades. Force measurements indicate that the aerodynamic performances are insensitive to the slight variations of the sectional geometry, as expected. As for the acoustic performance, both the tonal and broadband noise are slightly affected when the axial flow speed is lower than 5 m/s. By contrast, a discernible noise reduction above 3 dB can be achieved due to the finite trailing-edge thickness. The noise source features are also investigated using a wavelet-based beamforming method, confirming that the noise reduction is caused by the weakened trailing-edge noise around the tip. This study is beneficial for the quantification of uncertainties in propeller noise measurements. It also suggests that adjusting trailing-edge thickness might be an useful approach in reducing propeller noise in practical applications.

Polymer Membrane Modified with Photocatalytic and Plasmonic Nanoparticles for Self-Cleaning Filters.

In this study, we developed a filtering material for facial masks, which is capable of trapping and subsequent inactivation of bacteria under white light emitting diodes (LED) or sunlight irradiation. Such a functionality is achieved via the modification of the composite membrane based on porous polymer with photocatalytic (TiO2) and plasmonic (Ag) nanoparticles. The porous polymer is produced by means of a computer numerical control machine, which rolls a photoresist/thermoplastic mixture into a ~20-µm-thick membrane followed by its thermal/ultraviolet (UV) hardening and porosification. TiO2 nanoparticles are prepared by hydrothermal and sol-gel techniques. Colloidal synthesis is utilized to fabricate Ag nanoparticles. The TiO2 photocatalytic activity under UV excitation as well as a photothermal effect generated by plasmonic Ag nanoparticles subjected to LED irradiation are studied by the assessment of methylene blue (MB) decomposition. We demonstrate that, in contrast to the filter of the standard facial medical mask, the polymer membrane modified with spray-coated TiO2 and Ag nanoparticles prevents the penetration of bacillus subtilis from its top to bottom side and significantly inhibits bacterial growth when exposed to LED or sunlight.

BWOA based metaheuristic approach for uncertain nonlinear milling CNC machine system

AbstractThe scientific advances in the machining process inculcate intelligence in computer numerical control (CNC), to enhance reliability and quality production. The study made in this article has been directed towards the metaheuristic‐based intelligent control of a nonlinear uncertain CNC machine for milling purposes. The presence of cutting force and frictional type constraints, along with parametric uncertainties, degrade the production quality in milling operations. Moreover, enhanced performance of milling operation can be achieved via intelligent control of position and velocity of machine table and tool. In this work, the black widow optimization algorithm (BWOA) driven proportional–integral–derivative (PID) and PI controllers are employed for the position and the velocity of machine table and tool, respectively. Servo and regulatory are the two important control problems that are considered, analysed, and addressed in this work. The most challenging task of simultaneous tuning of PI and PID controllers is also addressed in this brief. Finally, a vivid comparative analysis of five state‐of‐the‐art optimization techniques is also performed, and the obtained results demonstrate the superiority of the proposed control scheme. While dealing with the servo performance of x‐axis, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time, and for regulatory performance, it gives a 98.45% improvement in undershoot. Moreover, for z‐axis control, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time and for regulatory performance gives a 100% improvement in undershoot compared to published literature.

Core temperature measurement using ultrasound for high precision manufacturing processes

During machining processes, the temperature of the workpiece may vary due to different factors. One of such factors is the heat generated due to tool/workpiece friction. Temperature may also vary due to environmental conditions. These temperature variations can affect the dimensional accuracy of the manufactured workpiece. It is known that the expansion of a part is related to a change in its average temperature, which is influenced more by the internal, core temperature than the surface temperature. The surface temperature of the part being manufactured can vary significantly from the core temperature, especially during dry cutting processes. Therefore, to effectively control or compensate for the effects of temperature variation as it relates to material expansion, there is a need to measure the core temperature of the workpiece accurately. In this article, a novel ultrasonic phase-shift method for temperature measurement is used to measure the core temperature of a workpiece on a computer numerical control machine (CNC). The results show that the phase-shift ultrasonic thermometry method measures steel workpiece temperature during subtractive manufacturing processes with deviations of less than ±1°C when compared to the reference PT100 readings. This novel temperature measurement method can be used in different manufacturing processes as part of a temperature control or thermal error compensation system.

Computer-controlled finishing via dynamically constraint position-velocity-time scheduler

In a Computer Numerical Controlled (CNC) finishing process, the target material removal from an optical surface is guided by the convolution between the influence function of a machine tool and its dwell time at certain points over the surface. To reduce dynamics stressing and increase machining efficiency, the dwell time must be converted to varying velocities, which are the actual inputs to the machine tool controller. Conventionally, the conversion assumed constant acceleration and relied on linear motion interpolation, which caused discontinuities in velocities. This unsmooth motion affects the material removal distribution, and, thus, the accuracy of the finished surface shape. Many modern CNC machines support the smoother, cubic-polynomial interpolated Position-Velocity-Time (PVT) motion mode; however, the conventional scheduler may fail to provide suitable velocities for the PVT. This study answers this challenge by proposing a novel PVT-based velocity scheduler that achieves smooth motion while considering CNC dynamic limits. Firstly, the principle of the PVT is explained, and the PVT-based velocity scheduler is formulated. Secondly, a quadratic programming is used to optimize the velocities by imposing the CNC dynamic constraints and the C1 continuities (zeroth and first derivatives are continuous) simultaneously. Thirdly, the smoothness and accuracy of the scheduled velocities are studied on different kinds of tool paths via simulation. Finally, a sub-0.3 nm level surface finishing experiment using ion beam figuring is demonstrated to verify the feasibility of the proposed method. The PVT-based scheduler and simulator code is open-sourced.

Methods for Reducing Subdivision Error within One Signal Period of Single-Field Scanning Absolute Linear Encoder

Optical encoders are widely used in accurate displacement measurement and motion-control technologies. Based on different measurement methods, optical encoders can be divided into absolute and incremental optical encoders. Absolute linear encoders are commonly used in advanced computer numerical control (CNC) machines. The subdivision error within one signal period (SDE) of the absolute linear encoder is vital to the positioning accuracy and low velocity control of CNC machines. In our paper, we study the working principle of the absolute linear encoder. We proposed two methods for reducing the SDE of the absolute linear encoder, a single-field scanning method based on the shutter-shaped Moiré fringe, as well as a method for suppressing harmonics through a phase shift of index grating. We established a SDE measuring device to determine the absolute linear encoder's SDE, which we measured using a constant-speed approach. With our proposed methods, the SDE was reduced from ±0.218 μm to ±0.135 μm, which is a decrease of 38.07%. Our fast Fourier transformation (FFT) analysis of the collected Moiré fringe signals demonstrated that the third-, fifth-, and seventh-order harmonics were effectively suppressed.

Five-Axis Contour Error Control Based on Numerical Control Data

Improving contour accuracy is one of the significant goals of industrial machining. This paper proposes a contour error estimation and compensation algorithm for five-axis computer numerical control (CNC) machine tools based on modified numerical control (NC) codes. The expected path analyzed by NC data and the actual trajectory collected by sensors are spatially mapped by the hidden Markov model (HMM). Next, an evaluation function that hybrids the tool tip position and tool orientation change trend is proposed as the index of contour error estimation. Finally, spatial iterative learning control (ILC) is used to compensate the contour error, and high-precision machining instructions are obtained after multiple iterations. Experiments with different trajectories are performed on a five-axis platform to verify the proposed algorithm’s effectiveness. The results show that the proposed algorithm without using planned trajectories, has the same good control effect as traditional methods, which must know the planning trajectory for simple trajectories. At the same time, the method proposed in this paper has better performance than existing algorithms based on tool tip position nearest principle at sharp corners. In conclusion, on the basis of not depending on the planning trajectories, this method has a better compensation effect for the overall accuracy of trajectories and is easier to implement in industrial applications.

A novel feed rate scheduling method with acc-jerk-continuity and round-off error elimination for non-uniform rational B-spline interpolation

Abstract Feed rate scheduling is a critical step in computer numerical control machining, as it has a close relationship with machining time and surface quality. It has now become a hot issue in both industry and academia. In this article, we present a novel and complete S-shape-based feed rate scheduling method for three-axis non-uniform rational B-spline (NURBS) tool paths, which can reduce high chord errors and round-off errors, and generate continuous velocity, acceleration, and jerk profile. The proposed feed rate scheduling method consists of three modules: a bidirectional scanning module, a velocity scheduling module, and a round-off error elimination module. The bidirectional scanning module aims to guarantee the continuity of the feed rate at the junctions between successive NURBS blocks, where the chord error, tangential acceleration, and tangential jerk limitations are considered. After the NURBS blocks have been classified into two cases by the previous module, the velocity scheduling module first calculates the actual maximum feed rate. It then generates the feed rate profiles of all NURBS blocks according to the proposed velocity profile. Later, the round-off error elimination module is applied to adjust the actual maximum feed rate so that the total interpolation time becomes an integer multiple of the interpolation period, which leads to the elimination of round-off errors. Finally, benchmarks are conducted to verify the applicability of the proposed method. Compared with the traditional method, the proposed method can save the interpolation time by $4.67$ to $14.26\% $.

Development of a Digital Portal as a Learning Resource Computer Numerical Control (CNC) Machining Engineering Course

This journal reviews the mindset of educators in Indonesia. Times have changed, technological developments have become more sophisticated, information flows so fast, but the mindset of educators has not changed much. Educators in the revolutionary era 3.0 are educators who act as facilitators. This means that educators cannot dominate the learning process. Students actively learn and educators facilitate the learning process. Meanwhile, educators in the revolutionary era 4.0 are students as teachers. In this era students can act as educators. Students become content creators, become producers, and become conductors. So what is the position of educators in the 4.0 revolution, educators must be able to activate, curate and develop learning resources with various digital channels. According to AECT (Association for Education and Communication Technology) learning resources are one of the keys to success in achieving learning goals. Based on these problems, this research is still very much needed. The type of research used is research and development (Research and Development). This study only looks at the feasibility and practicality of digital portals as learning resources for computer numerical control (CNC) machining techniques courses. The development of this learning product is arranged programmatically with a systematic sequence covering six stages, namely: literature study, development planning or design, product development, expert validation, trials, revisions, final product. The test subjects consisted of three material experts for the CNC machining engineering course, three learning design experts, three learning media experts, and a practicality test consisting of thirty students from the mechanical engineering education study program. Data about the quality of this development product is collected by questionnaire. The collected data were analyzed using qualitative descriptive analysis techniques. The research results show; (1) the expert test for the subject matter for the CNC machining engineering course is in the Valid qualification (91.67%); (2) the learning design expert test is in the Valid qualification (94.20%); (3) the media expert test is in the Valid qualification (94.52%); (4) test the practicality of digital portals as learning resources computer numerical control (CNC) machining techniques courses by student responses showed a level of practicality with a percentage of 95.8% in the very practical category. The results of this research study concluded that the digital portal as a learning resource for CNC machining engineering courses is feasible and practical to use in achieving student learning goals in the CNC machining engineering course in the Mechanical Engineering Education Department, Faculty of Engineering, Medan State University.

Corner smoothing for CNC machining of linear tool path: A review

Computer numerical control (CNC) machine tools are widely used in various industrial fields ranging from aerospace, automotive, ship building, and the die/mould to manufacture products. However, tool paths of most CNC machine tools are composed of a series of linear motion commands (G01), which will inevitably cause the discontinuity in curvature and feedrate at the junctions between adjacent linear tool path segments, deteriorating the surface quality with unfavorable marks and decreasing the machining efficiency. To solve this problem for obtaining the steady and continuous motions of machine tools, the local corners have to be smoothed. Generally, the existing corner smoothing methods can be classified as the global smoothing and the local corner smoothing, where the specially designed transition curves or the directly planned motion of machine tools are adopted to generate the geometrically corner-free tool path. Moreover, some new methods that focus on developing different transition or rounding strategies are also developed for further improving the kinematics performance of machine tools. In this paper, the recent advances and researches on corner smoothing methods are reviewed from different categories, and the conclusions, remaining challenges and future directions in corner smoothing are also presented.

Five-Axis Contour Error Control Based on Spatial Iterative Learning

In this paper, a contour error control strategy based on spatial iterative learning control (sILC) is developed for repetitive processing of five-axis computer numerical control (CNC) machine tools. A curve approximation method with an adaptive moving window is developed to achieve accurate tool position and orientation contour error estimation, and a five-axis contour error control strategy based on sILC is proposed. The compensation method is derived using an sILC algorithm to modify the geometric reference path instead of modifying the controller. The experimental results show that the proposed control strategy reduces contour errors of five-axis CNC machine tools, and it outperforms the traditional tracking error control. Note to Practitioners—This paper aims to propose an effective five-axis contour error control scheme. At present, most of the five-axis contour error control methods rely on the modification of the controller, which is not allowed in commercial CNC systems. Therefore, we propose a five-axis contour error compensation algorithm based on sILC, which modifies the system’s reference path. Experimental results show that the proposed method can reduce the five-axis contour errors, while maintaining the machining efficiency.

Reliability modeling and analysis of cycloid gear grinding machines based on the bootstrap-bayes method

To increase the reliability of cycloidal wheel grinding machines, reduce the failure rate of machine tools, and shorten maintenance times, a reliability modeling method for small-sample fault data is proposed based on the Bootstrap-Bayes method. The mean time between failures (MTBF) of a machine tool generally conforms to a Weibull distribution. Based on the historical fault information and similar fault information, the distribution function for the mean time between failures of a machine tool is determined. The fault information is expanded by the self-help method, and the two-parameter distribution interval of the distribution function is calculated by the Bayesian formula. An example reliability calculation for a cycloid gear grinding machine is given, including the failure analysis and maintenance methods of the gear grinding machine. This approach can also be used to simulate and analyze the reliability of other computer numerical control (CNC) machine tools.

Optimization and analysis of machining performance for the milling process during milling of W-Al-Si-C alloy material

This study determined the optimum HSS cutting tool technique parameters for milling W-Al-Si-C rods using Taguchi methodology. This paper explains the empirical results of the selection of appropriate cutting settings that assure lower power consumption in high-end Computer Numerical Control (CNC) machines. An experiment employing the Taguchi methodology on an extruded W-Al- Si-C rod was performed on a CNC lathe with cutting speed, feed rate, and depth of cut as the process parameters. The performance characteristics (energy usage) were quantified by a data collection system. Minor energy process parameters were selected after data analysis. Experimental results are presented to demonstrate the worth of the chosen methodology. A total of 350[Formula: see text]rpm, 0.37[Formula: see text]mm/rev feed rate, and 1[Formula: see text]mm of cut depth produced the best MRR result. The maximum material removal rate (MRR) is obtained at lower levels of spindle speed and depth of cut, i.e., 1.452[Formula: see text]g/sec.

Rapid Prototyping and Tooling Technology to Assist the Development of Low-Cost Dies

This case study aims toward the importance of the application of rapid prototyping and tooling technology in die design and manufacturing. Many conventional machining processes of die manufacturing, including computer numeric control (CNC) machining and electric discharge machining (EDM), are used by the local manufacturers of die, but these processes are time-consuming and expensive. There is a need to explore opportunities to reduce die manufacturing costs. This paper aims to assist the development of low-cost dies through the application of rapid prototyping and tooling technology. For this purpose, solid works software was used to design the die of a 4-hole Lego Block. 3D printing was incorporated in the existing capability of investment casting process for die manufacturing. A proper gating system design consideration was taken into account to ensure the accurate flow of casting material for achieving fine surface finish. From the results, it has been observed that gating system design with four ingates gives the optimum value of surface finish (~3.4156μm). The proposed rapid prototyping and tooling technology makes the practitioners (tool makers) capable of making dies with complex shapes, superior surface finishes, and dimensional accuracy in less or no time.