Computer Numerical Control Machine Research Articles

Literature Review on the Utilization of Rice Husks: Focus on Application of Materials for Digital Fabrication.

To achieve a sustainable society, it is important to use biological resources effectively to the extent that they are renewable. Rice husk, which is abundantly produced in various regions, is a useful biomass resource. To promote their use further, it is important to expand the fields in which they are used. Therefore, this study reviews the research on rice-husk-based materials that can be used in digital fabrication, such as those used with 3D printers and Computer Numerical Control (CNC) machines, which have become increasingly popular in recent years. After outlining the characteristics of each machining method, the authors surveyed and analyzed the original research on rice-husk-based materials for 3D printers and particleboard available in digital fabrication machines for 2D machining. This review identifies issues and proposes solutions for expanding the use of rice-husk-based materials. It also indicates the need for further research on various aspects, such as the workability and maintainability of the equipment.

Mixed tiny path smoothing method based on sliding convolution windows for CNC machining

The machining toolpaths of complex free-form surface parts are generated using computer-aided manufacturing (CAM) systems and programmed by a series of mixed tiny paths composed of tiny lines, arcs, and non-uniform rational B-spline (NURBS). The mixed tiny paths need to be smoothed to eliminate the tangential discontinuities at corners. This paper proposes a mixed tiny path smoothing method based on sliding convolution windows (MSSCW). In this method, the mixed tiny path is firstly converted into a positional signal along the travel length in the spatial domain, and then sliding convolution windows, which centered on each original interpolation point, are used to generate high-order continuous smoothed toolpath. Finally, the smoothing error is checked and corrected by modifying the length duration of the sliding convolution windows. Compared with conventional path smoothing method, the MSSCW method is suitable for various computer numerical control (CNC) machining toolpaths, and the continuity of the smoothed toolpath can be adjusted according to user requirements. Consequently, the feasibility and effectiveness of the proposed method are validated in simulations and experimental results which realize the improvement of the machining quality of complex free-form surfaces.

Adaptive Quantization Range Division Technique for Electronic Control Data Compression in CNC Machine Tools

With the development of new technologies such as artificial intelligence and big data, Industry 4.0 in manufacturing has been launched. As the core pillar of industrial manufacturing, computer numerical control (CNC) machine tools face significant challenges in data acquisition transmission and storage due to their complex structure, high volume of data points, strong time-series characteristics, and large amounts of data. To address the shortcomings of existing compression algorithms in quantization methods for large amounts of data in the instruction-domain, this paper proposes a quantization method based on distortion rate evaluation and linear fitting entropy reduction transformation, which aims to compress state signals such as the load power and load current while ensuring the availability of the data. This approach provides technical support for the transmission of high-frequency big data and meets the lightweight data acquisition requirements of digital twins for CNC machine tools. Compared to the empirical approach, this approach was more accurate and more computationally efficient.

Hybrid manufacturing by additive friction stir deposition, metrology, CNC machining, and microstructure analysis

Aerospace flight panels must provide high strength with low mass. For aluminum panels, it is common practice to begin with a wrought plate and remove the majority of the material to attain the desired structure, comprising a thinner plate with the desired pattern of reinforcement ribs. As an alternative, this study implements hybrid manufacturing, where aluminum is first deposited on a baseplate only at the rib locations using additive friction stir deposition (AFSD). Structured light scanning is then used to measure the printed geometry. This geometry is finally used as the stock model for computer numerical control (CNC) machining. This paper details the hybrid manufacturing process that consists of: AFSD to print the preform, structured light scanning to generate the stock model and tool path, three-axis CNC machining, and post-process measurements for part geometry and microstructure.

High-Quality Positioning Strategy for Biaxial Contour Machining System With Nonlinear Model Predictive Control

The trajectory tracking ability is an important index to measure the computer numerical control (CNC) machining tools. In this paper, the nonlinear model predictive control strategy (NMPC) is proposed to improve the trajectory tracking ability of the biaxial contour machining system (BCMS). The position-, velocity-, and current-loop of the permanent magnet synchronous motors (PMSMs) of the BCMS are considered as the complete control object to construct a single-loop nonlinear model predictive controller. Meanwhile, the matching non-linear disturbance observer (NL-DOB) is designed to estimate the uncertainty disturbances caused by the parameters mismatch, external load changes and harmonic disturbances in real-time. And the estimated disturbance values are used as feed-forward compensations, which can improve the steady-state performance of the system. At last, the positioning performance comparison between the proposed strategy and the conventional ones are carried on the testbench. The experimental results indicate that the positioning errros can be reduced by up to 40% with the proposed algorithm.

Tool remaining useful life prediction considering wear state based on hybrid attention network

Accurate prediction of the remaining useful life for the cutting tool is a key part of the predictive maintenance of computer numerical control machines. However, the wide variety of tools makes the process of modeling different tool wear regularities redundant and cumbersome. In addition, it is difficult to deal with the input characteristics of multi-sensor monitoring signals in a targeted manner. To solve the above problems, a hybrid predictive model with squeeze-and-excitation (SE) module is proposed. Combined with adaptive feature extraction based on convolutional neural network and observation based on bidirectional gated recurrent unit, accurate multivariate regression prediction is achieved. The SE module enhances the focus on crucial features. Finally, through the design of the tool wear experiment and the combination of the public dataset, the accuracy and generalization ability of the proposed model are verified under different tool types and different working conditions.

Image to G-Code Conversion using JavaScript for CNC Machine Control

This paper presents a JavaScript-based approach for image-to-G-code conversion in CNC (Computer Numerical Control) machine control. The developed code enables the translation of images and text into machine-readable instructions for precise reproduction using CNC machines. The code implements image loading, preprocessing, binarization, thinning, and G-code generation functionalities. It offers adjustable parameters for CNC and image settings, allowing customization and optimization of the machining process. Experimental evaluations confirm the code's efficiency, accuracy, and usability. The results demonstrate reliable image preprocessing, effective binarization techniques, successful image thinning, and precise G-code generation. The code's accessibility, user-friendly interface, and real-time preview capabilities further enhance its usability. This JavaScript-based approach contributes to the integration of digital workflows into CNC machining, offering a promising solution for accurate and efficient fabrication.

A Feedrate Planning Method in CNC System Based on Servo Response Error Model

Reducing servo response error and further making reduction on contour error is crucial for high-precision computer numerical control (CNC) machine tools. For a permanent magnet synchronous motor (PMSM) servo system, there is always a response lag in feedrate tracking, which would introduce response error into the machining trajectory. Therefore, it is necessary to improve the performance of feedrate planning and interpolation for trajectory path. In this paper, a novel contour error compensation strategy is proposed. Compared with the mainstream methods, the proposed method offers a simplified alternative to existing contour error estimation techniques. Through a three-closed-loop control structure of a PMSM servo system, a response error model is founded. Afterwards, an improved S-model feedrate planning method is introduced according to the servo response error compensation. This predicted error is subsequently compensated in each interpolation cycle, resulting in a reduction of contour error. Finally, simulations and experiments are performed to demonstrate that the contour error can be reduced in both the ‘∞’-shaped Non-Uniform Rational B-Spline (NURBS) curve path and the butterfly-shaped NURBS curve path using the proposed method.

Nonlinear prediction and analysis of the precision remaining useful life of the key meta-action unit of CNC machine tools with incomplete maintenance

The accuracy of the PRUL (precision remaining useful life) prediction is of great significance for the accuracy of active maintenance of CNC (computer numerical control) machine tools. KMAU (key meta-action unit) is the meta-action unit that has a significant impact on the quality characteristics of CNC machine tools. Therefore, it is taken as the research object, and a new method for predicting the PRUL is proposed. Firstly, considering the nonlinearity and uncertainty of the KMAU precision degradation process, the nonlinear Wiener process is used to establish its precision degradation model, and the model parameters are updated online by Bayesian principle. Secondly, the precision of the KMAU will be partially restored under the incomplete maintenance, and the composite NHPP (non-homogeneous Poisson process) is improved by GPIM (generalized proportional intensity model) to describe the change of KMAU's precision. Then, the precision comprehensive degradation model of the KMAU is built by combining the nonlinear Wiener degradation model. Finally, the maximum likelihood estimation method is used to estimate the parameters of the proposed model, and the PDF (probability density function) of the PRUL of CNC machine tool KMAU is obtained according to the time that the precision degradation first reaches its failure threshold (called the first arrival time), so as to predict the PRUL of the KMAU. The method proposed in this paper comprehensively considers the impact of nonlinearity, uncertainty, and incomplete maintenance on the accuracy of PRUL prediction for CNC machine tools, and has high accuracy. The KMAU of CNC machine tool turntables made in China is taken as an example, and the correctness, rationality, and superiority of the proposed method are verified by multiple method comparisons and experimental.

Compact Wideband Groove Gap Waveguide Bandpass Filters Manufactured with 3D Printing and CNC Milling Techniques.

This paper presents for the first time a compact wideband bandpass filter in groove gap waveguide (GGW) technology. The structure is obtained by including metallic pins along the central part of the GGW bottom plate according to an n-order Chebyshev stepped impedance synthesis method. The bandpass response is achieved by combining the high-pass characteristic of the GGW and the low-pass behavior of the metallic pins, which act as impedance inverters. This simple structure together with the rigorous design technique allows for a reduction in the manufacturing complexity for the realization of high-performance filters. These capabilities are verified by designing a fifth-order GGW Chebyshev bandpass filter with a bandwidth BW = 3.7 GHz and return loss RL = 20 dB in the frequency range of the WR-75 standard, and by implementing it using computer numerical control (CNC) machining and three-dimensional (3D) printing techniques. Three prototypes have been manufactured: one using a computer numerical control (CNC) milling machine and two others by means of a stereolithography-based 3D printer and a photopolymer resin. One of the two resin-based prototypes has been metallized from a silver vacuum thermal evaporation deposition technique, while for the other a spray coating system has been used. The three prototypes have shown a good agreement between the measured and simulated S-parameters, with insertion losses better than IL = 1.2 dB. Reduced size and high-performance frequency responses with respect to other GGW bandpass filters were obtained. These wideband GGW filter prototypes could have a great potential for future emerging satellite communications systems.

A comprehensive review of machine learning techniques in computer numerical controlled machines

Machine learning (ML) is significant advancements in computer science and data processing systems that may be utilized to improve almost all technology-enabled services, goods, and industrial applications. Machine learning is a branch of computer science and artificial intelligence. It emphasizes the use of data and algorithms to mimic the learning process of machines and improve system accuracy. To extend the life of the cutting tools used in machining processes, machine learning algorithms may be used to forecast cutting forces and cutting tool wear. In order to improve productivity during the component production processes, optimized machining parameters for CNC machining operations may be obtained by applying cutting-edge machine learning algorithms. Furthermore, the appearance of Advanced machine learning algorithms can forecast and enhance machinable components to raise the caliber of machinable parts. Machine learning is applied to prediction approaches of energy consumption of CNC machine tools in order to analyses and minimize power usage during CNC machining processes. The use of machine learning and artificial intelligence systems in CNC machine tools is examined in this paper, and future research projects are also suggested in order to offer an overview of the most recent studies on these topics. As a result, the research filed can be moved forward by reviewing and analyzing recent achievements in published papers to offer innovative concepts and approaches in applications of machine learning in CNC machine tools.

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).

A framework for practically effective creation of postprocessors for 5-axis CNC machines with all possible configurations and working mechanisms

The 5-axis CNC (Computer Numerical Control) machining plays an important role in manufacturing, especially in making parts of complex shapes such as turbine or propeller blades. However, there are always challenges of generating 5-axis CNC machining programs when working with different 5-axis CNC machines of various structures; and 5-axis CNC programs must be automatically generated by CNC postprocessors which calculate the inverse kinematics and convert the Cutter Location data to the CNC programs that are used for operating 5-axis CNC machines. Since the family of 5-axis CNC machines has a wide spectrum of machine configurations, with hundreds of mechanisms of 5 degrees of freedom, it is therefore practically challenging for engineers and CNC machine operators to create CNC postprocessors for specific 5-axis CNC machines. It is more challenging to create CNC postprocessors if engineers or CNC operators do not have strong backgrounds and professional skills in mathematical modeling and kinematics of machines. This paper presents a universal and intuitive framework and practical guidance to create CNC postprocessors for all 5-axis CNC machines, with the focus on a novel mathematical formulation of inverse kinematics of three main groups of 5-axis CNC machines. The case studies of creating CNC postprocessors with the commercially available 5-axis CNC machines were successfully demonstrated, in which the simulation scenarios and experiments were implemented to verify the created CNC postprocessors. The proposed frameworks and guides for generating CNC postprocessors can be conveniently and effectively applied in industrial practices, without the required strong backgrounds and skills for engineers or CNC operators in mathematical modeling and kinematics of machines, especially mathematical modeling of multibody systems.

Study on Life Prediction Method of Ball Screw Base on Constructed Degradation Feature and IGWO-BiLSTM

Regarding the problem of reduced remaining useful life (RUL) due to wear of the ball screw in the feed system of CNC (computer numerical control) machine tools, a prediction method based on constructing the degradation feature vector of the signal data and the improved gray-wolf optimization with bidirectional long short-term memory (IGWO-BiLSTM) neural network regression model is proposed. Firstly, a time-domain analysis and the complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) were carried out based on the collected life cycle signal data of a ball screw. The time-domain feature vector and the energy feature vector of each IMF (intrinsic mode function) component after CEEMDAN decomposition were constructed. The Pearson correlation coefficient was used to filter feature vectors and construct the multivariate feature vector. Secondly, this paper improves the traditional gray wolf optimization algorithm, adds a search strategy based on dimension learning, and combines the improved algorithm with the BiLSTM model, based on the IGWO-BiLSTM theory. A regression model between feature vectors and the remaining life of a ball-screw system was established. Finally, the prediction model was established according to the proposed method and compared with the other five neural network models: LSTM, BiLSTM, BO-LSTM (Bayesian optimization of LSTM), BO-BiLSTM, and IGWO-LSTM. The results indicate that this method has high accuracy and good generalization ability for predicting the remaining life of a ball-screw system.

A Multi-Scale Tool Orientation Generation Method for Freeform Surface Machining with Bull-Nose Tool.

Free-form surface parts are widely used in industries, and they consist of intricate 3D surfaces such as molds, impellers, and turbine blades that possess complex geometrical contours and demand high precision. Proper tool orientation is crucial for ensuring the efficiency and accuracy of five-axis computer numerical control (CNC) machining. Multi-scale methods have received much attention and have been widely used in various fields. They have been proven to be instrumental and can obtain fruitful outcomes. Ongoing research on multi-scale tool orientation generation methods, which aim to acquire tool orientations that satisfy both macro- and micro-scale requirements, is significantly important for improving the machining quality of workpiece surfaces. This paper proposes a multi-scale tool orientation generation method that considers both the machining strip width and roughness scales. This method also ensures a smooth tool orientation and avoids interference in the machining process. First, the correlation between the tool orientation and rotational axis is analyzed, and feasible area calculation and tool orientation adjustment methods are introduced. Then, the paper introduces the calculation method for machining strip widths on the macro-scale and the roughness calculation method on the micro-scale. Besides, tool orientation adjustment methods for both scales are proposed. Next, a multi-scale tool orientation generation method is developed to generate tool orientations that meet the macro- and micro-scale requirements. Finally, to verify the effectiveness of the proposed multi-scale tool orientation generation method, it is applied to the machining of a free-form surface. Experimental verification results have shown that the tool orientation generated by the proposed method can obtain the expected machining strip width and roughness, meeting both macro- and micro-scale requirements. Therefore, this method has significant potential for engineering applications.

Analysis of DC magnetron sputtered coated with distinct inserts in CNC machining process

Machining is the process of cutting or removing materials from a work item with a cutting tool to get the desired form. CNC is widely used because of its compact size which enables more number of productions in all type of automotive industries. Many intricate parts required by various industries are created using computer numerical control (CNC) machining. While mass production is useful in some cases, which require selective parts in most industries. Businesses require CNC for their manufacturing process, which enables them for précised jobs with help of this CNC machine software. CNC lathes can make precise cuts quickly with a variety of tools. Hence, the major objective of this work is to extend the life and quality of the cutting tool by improving its hardness and wear resistance by using DC Magnetron Sputtering to coat a 1 μm thin film layer of Tungsten carbide on High speed steel insert. This insert is to be characterized for chemical composition, micro structured, grain distribution and hardness of its surface etc.

Perancangan Mesin CNC Acrylic Cutting 3 Axis Dengan Menggunakan Laser Tube CO2

Almost all activities are carried out with the help of technology. It is as if technology has become a basic need for human life today. Of all the areas that have made progress, machinery is the one that has had the greatest effect. Lots of machines have emerged with new innovations with very diverse functions, one of which is the Computer Numerical Control (CNC) machine. A CNC machine is a machine used in the manufacturing industry to quickly produce large quantities of components for the engineering sector. As the name of the CNC itself, every work from CNC uses a computer system that has been well formed to produce goods that are in accordance with what is desired. Departing from some of the problems above, the author concluded to make a similar technology at a much cheaper price with a more practical work system, therefore the author made a 3 Axis Acrylic Cutting CNC Machine Using a CO2 Laser Tube".

Optimisation of the machining time required by insole orthotic shoes for patients with clubfoot using the Taguchi and response surface methodology approach

In this study, the application of the computer-aided reverse engineering system (CARE) to the novel design and manufacture of a comfortable insole for a clubfoot patient is presented. The Taguchi method (TM) and response surface methodology (RMS) were used to predict the machining time of the orthotic boot insole during both computer-aided manufacturing (CAM) simulation and computer numerical control (CNC) machining. Taguchi’s experimental design, presented as a matrix orthogonal array L2736, was acquired for controlling parameters, namely tool path strategy (A), spindle speed (B), step-down (C), step-over of the cutter (D), cutter diameter (E), and dimensional tolerance (F) of the insole size. In this method, the model generated by the RMS method evaluates the six parameters influencing the machining time. The objective of this study is to develop a regression model that demonstrates the relationship between the cutting parameters and insole machining time. The optimal parameters are A1B1C3D2E1F2, where A1 denotes raster finishing, B1 denotes a spindle speed of 10,000 rpm, C3 denotes a step-down of 850 mm, D2 denotes a step-over of 0.25 mm, E1 denotes a cutter diameter of 20–35 mm, and F2 deontes a tolerance of 0.75 mm. The experimental and calculated machining time (tm) results were 236 and 125.4 min, respectively. However, the real machining results were 334 and 152.25 min with error values of 46.86% and 54.42%, respectively. Meanwhile, with the tm RMS method, the simulated and calculated machining time results were 189.22 and 236.35 min, whereas the real tm values were 236.52 and 334.86 min with error values of 19.94% and 29.37%, respectively. This research obtains improvements of 19.82% (simulation time) and 29.19% (real-time).

Half-Height Pin Gap Waveguide-Based Slow-Wave Structure for Millimeter Wave Traveling-Wave Tubes

The design of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{W}$</tex-math> </inline-formula> -band traveling-wave tube (TWT) power amplifier based on a groove gap waveguide (GW) slow-wave circuit is presented in this article. The technology of GW is analyzed to aid the design of electromagnetic band gap-based slow-wave structures (SWSs) in the upper millimeter wave range of the spectrum while alleviating some of the typical fabrication challenges at these frequencies. The results of particle-in-cell (PIC) simulations numerically demonstrate a 10-GHz instantaneous 3-dB bandwidth in the range 89–99 GHz with a minimum power gain of 25 dB. A prototype of the complete SWS is manufactured via computer numerical control (CNC) machining and measured to verify the cold simulation results. Machining tolerances and surface roughness are also investigated. The design approach via groove GW is flexible and can be extended to alternative rectangular waveguide-based SWSs.

Superhydrophobic Rotation-Chip for Computer-Vision Identification of Drug-Resistant Bacteria.

The transport, distribution, and mixing of microfluidics often require additional instruments, such as pumps and valves, which are not feasible when operated in point-of-care (POC) settings. Here, we present a simple microfluidic pathogen detection system known as Rotation-Chip that transfers the reagents between wells by manually rotating two concentric layers without using external instruments. The Rotation-Chip is fabricated by a simple computer numerical control (CNC) machining process and is capable of carrying out 60 multiplexed reactions with a simple 30 or 60° rotation. Leveraging superhydrophobic coating, a high fluid transport efficiency of 92.78% is achieved without observable leaking. Integrated with an intracellular fluorescence assay, an on-chip detection limit of 1.8 × 106 CFU/mL is achieved for ampicillin-resistant Escherichia coli (E. coli), which is similar to our off-chip results. We also develop a computer vision method to automatically distinguish positive and negative samples on the chip, showing 100% accuracy. Our Rotation-Chip is simple, low-cost, high-throughput, and can display test results with a single chip image, making it ideal for various multiplexing POC applications in resource-limited settings.