CNC Machine Research Articles

Operational Modal Analysis of CNC Machine Tools Based on Flank-Milled Surface Topography and Cepstrum

Operational Modal Analysis of CNC Machine Tools Based on Flank-Milled Surface Topography and Cepstrum

Comparative Study of Conventional Machine Learning versus Deep Learning-Based Approaches for Tool Condition Assessments in Milling Processes

This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector machines (SVMs), and boosting ensemble decision trees, utilizing sensor data from a CNC machining center. These methods focus on the challenges and importance of feature selection, data preprocessing, and the application of tailored machine learning models to specific industrial tasks. Results show that SVM, with an accuracy of 96%, excels in handling high-dimensional data and robust feature extraction. In contrast, LSTM, which is appropriate for sequential data, is constrained by limited training data and the absence of pre-trained networks. Boosting ensemble decision trees also demonstrate efficacy in reducing model bias and variance. Conclusively, selecting an optimal machine learning strategy is crucial, depending on task complexity and data characteristics, highlighting the need for further research into domain-specific models to improve performance in industrial settings.

Preventive Maintenance on CNC Machines Using the OEE Method to Reduce Downtime at PT. MTAT

This study examines the performance of CNC machines at PT MTAT Indonesia from January to March 2023. Monthly production data, machine uptime, defect rates, and non-productive periods were collected to assess Overall Equipment Effectiveness (OEE). This study aims to analyze the effectiveness of preventive maintenance of CNC machines at PT MTAT Indonesia using the Overall Equipment Effectiveness (OEE) method to reduce downtime. This study uses monthly data from January to March 2023, including production uptime, defect rates, and non-productive periods, to calculate OEE. The analysis showed that the CNC machines achieved an average OEE of 86.52%, surpassing the global standard of 85%, indicating high efficiency and quality. The study used Pareto analysis to identify the main causes of downtime, finding technical and maintenance issues as the main contributors. By addressing these factors, PT MTAT Indonesia can further improve machine efficiency and productivity. This study contributes to this field by providing a comprehensive analysis of CNC machine maintenance and proposing strategies for continuous improvement.

Redesign User Interface at PT. Budi Jaya Banjarindo Used Method UCD

Budi Jaya Banjarindo Company operates in the machining and fabrication sector and has a production system (make to order), which means that the products produced are in accordance with the wishes or plans of consumers. This company uses machine equipment such as lathes, CNC machines, CNC Plasma Cutting, milling machines, hobbing and so on. Evaluation and redesign of the user interface system using Figma tools which will later be used by the workshop to optimize online product marketing. The system was designed using the UCD method. The final result of the design is a prototype which will then be assessed or tested using the SUS method to obtain an assessment from users. The SUS Score obtained was 65, based on these results it is included in the C range (range 60-70). These results indicate that the UI evaluation results are of good quality. Based on the results of the evaluation and redesign of the user interface by adding several additional features, it succeeded in meeting user needs and the test results were of good quality.

Switched capacitor voltage boost converter for BLDC motor speed control of electric vehicles

BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.

Advantages of stereolithographic 3D printing in the fabrication of the Affiblot device for dot-blot assays

In stereolithographic (SLA) 3D printing, objects are constructed by exposing layers of photocurable resin to UV light. It is a highly user-friendly fabrication method that opens a possibility for technology sharing through CAD file online libraries. Here, we present a prototyping procedure of a microfluidics-enhanced dot-blot device (Affiblot) designed for simple and inexpensive screening of affinity molecule characteristics (antibodies, oligonucleotides, cell receptors, etc.). The incorporation of microfluidic features makes sample processing user-friendly, less time-consuming, and less laborious, all performed completely on-device, distinguishing it from other dot-blot devices. Initially, the Affiblot device was fabricated using CNC machining, which required significant investment in manual post-processing and resulted in low reproducibility. Utilization of SLA 3D printing reduced the amount of manual post-processing, which significantly streamlined the prototyping process. Moreover, it enabled the fabrication of previously impossible features, including internal fluidic channels. While 3D printing of sub-millimeter microchannels usually requires custom-built printers, we were able to fabricate microfluidic features on a readily available commercial printer. Open microchannels in the size range 200–300 μm could be fabricated with reliable repeatability and sealed with a replaceable foil. Economic aspects of device fabrication are also discussed.Graphical abstract

Abrasive Waterjet Machining.

The abrasive waterjet machining process was introduced in the 1980s as a new cutting tool; the process has the ability to cut almost any material. Currently, the AWJ process is used in many world-class factories, producing parts for use in daily life. A description of this process and its influencing parameters are first presented in this paper, along with process models for the AWJ tool itself and also for the jet-material interaction. The AWJ material removal process occurs through the high-velocity impact of abrasive particles, whose tips micromachine the material at the microscopic scale, with no thermal or mechanical adverse effects. The macro-characteristics of the cut surface, such as its taper, trailback, and waviness, are discussed, along with methods of improving the geometrical accuracy of the cut parts using these attributes. For example, dynamic angular compensation is used to correct for the taper and undercut in shape cutting. The surface finish is controlled by the cutting speed, hydraulic, and abrasive parameters using software and process models built into the controllers of CNC machines. In addition to shape cutting, edge trimming is presented, with a focus on the carbon fiber composites used in aircraft and automotive structures, where special AWJ tools and manipulators are used. Examples of the precision cutting of microelectronic and solar cell parts are discussed to describe the special techniques that are used, such as machine vision and vacuum-assist, which have been found to be essential to the integrity and accuracy of cut parts. The use of the AWJ machining process was extended to other applications, such as drilling, boring, milling, turning, and surface modification, which are presented in this paper as actual industrial applications. To demonstrate the versatility of the AWJ machining process, the data in this paper were selected to cover a wide range of materials, such as metal, glass, composites, and ceramics, and also a wide range of thicknesses, from 1 mm to 600 mm. The trends of Industry 4.0 and 5.0, AI, and IoT are also presented.

ANALYSIS OF THE EFFECTIVENESS OF THE DIVISION OF LABOR BETWEEN THE OPERATOR AND THE ADJUSTER OF CNC MACHINES DURING SINGLE-MACHINE MAINTENANCE

An assessment was made of the area of effective application of the division of labor of operators and adjusters of CNC machines to increase the efficiency of the use of workers. First of all, the analysis was carried out for highly qualified workers. It is shown that a significant economic effect from the division of labor of operators and adjusters on CNC machines can be obtained in medium-scale and large-scale production. This is possible by forming groups of adjusters of 2...3 people serving the corresponding group of machines. Dependencies have been identified that make it possible to determine the minimum ratio of workers that provides a given economic effect for single-machine service.

Optimization of CNC milling parameters using the response surface method for aluminum 6061

The manufacturing sector is constantly seeking ways to optimize the machining process, specifically for 3-axis CNC machines. This study aims to identify the optimal parameter values that result in the lowest roughness and the highest process capability in 3-axis CNC milling. The roughness level (Ra) of the product is primarily influenced by factors such as feed rate, spindle speed, and depth of cut. Additionally, the reliability of the machining process was analyzed to evaluate its ability to consistently achieve low roughness values and to validate the process capability of the VH850L3 series 3-axis CNC milling machine. The suggested approach for this analysis was the RSM central composite design method, which involved conducting experiments under various input conditions. The results indicated that the feed rate had the most significant impact on roughness, followed by the spindle speed, while the depth of cut had no effect. The parameters that resulted in the lowest roughness response were a spindle speed of 2589.76 rpm, a depth of cut of 0.159 mm, and a feed rate of 247.731 mm/min. These parameter values were tested on a 3-axis CNC machine, and the resulting data exhibited variations. Data processing revealed that the machine still performed optimally in the machining process, as indicated by the value of . However, the milling process deviates from the standard target, as the response value shows significant variation with a Cpk value 1.

Development of techniques improving functional and programming abilities of intelligent CNC machine tools

Development of techniques improving functional and programming abilities of intelligent CNC machine tools

Recent Patents on Thermal Characteristic Analysis and Modeling of Machine Tools

Abstract: Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

Fatigue life prediction and structural optimisation of linear precision sliding tables under variable loads

ABSTRACT A crucial component of CNC machine tools is the linear precision sliding table, but how to improve the support and fatigue resistance performance of the slide table base has been a problem to be solved. In this research, a variable density method based on interpolating functions is used to topologically optimise the slide table base for better load-carrying capacity. The minimal compliance subject to a specified mass percentage constraint is formulated, and a quadratic topology optimisation by expanding the solution domain yields an innovative base structure. A comprehensive assessment of the structural performance of the new base was performed, including maximum von Mises stresses and cumulative fatigue damage for the reference load case. The fatigue resistance of the optimised slide table base in the reference condition proved to be much better than that of the original structure, while the new design also reduced the mass of the base. These findings conclusively show that the proposed topology optimisation methodology can propose novel base designs that can reduce the experimental cycle time in the design of the base.

Modification of 2fn-Recuv Test Bench

The objective of this study is to modify the 2fn-recuv test bench. The test bench is used to carry out drone flight simulations. It can provide a controlled environment to ensure the drone’s safety. The test bench allows precise data collection and analysis for an unmanned aerial vehicle (UAV). First, a literature review is conducted to understand and analyse the design of the previous test bench. The limitations of CNC machines are also being studied to ensure the modified design can be fabricated. Based on the findings and results, a 3D drawing is created, taking into account the need for machining. The first prototype is made using a 3D printer. It allows for initial testing and checking for possible failures of the design. The completed part of the prototype is then fabricated using a CNC machine. The new test bench is made of aluminium to give it more rigidity and strength. Stress analysis is done using Solidworks 2020 to ensure the test bench can withstand the weight of the drone. The test bench is tested using Labview to record the angle of each movement of the drone, such as pitch, roll, and yaw. The maximum and minimum values indicate the angle of the test bench for the drone’s flight simulation.

공작기계 이송축의 운동 물성치 분석 툴 개발

In this study, a tool is developed to analyze the motion properties of feed axes to minimize machining time and improve productivity in manufacturing fields such as aircraft component machining. The motion properties of jerk, acceleration, and velocity are used to calculate the feeding time under a certain distance for the axis of a CNC machine tool. This analysis tool categorizes feed distances based on acceleration and speed and implements a feed-time calculation algorithm based on each distance type. A user-friendly graphical user interface is developed for interaction with users. The use of this tool is expected to minimize the machining time and improve productivity in manufacturing processes such as aircraft component machining. Furthermore, applying this analytical tool to the identification of appropriate motion properties can increase the efficiency of manufacturing processes and improve product quality.

Analyze the effects of CNC machining parameters on the surface roughness (Rz) of Anatolian chestnut

The effect of processing parameters on the surface roughness parameter Rz (mean peak-to-valley height) in CNC machining of Anatolian chestnut (Castenia sativa Mill.) wood species used in the woodworking and furniture industry was investigated. The machining conditions were determined to obtain the lowest Rz value. The experi-ments were carried out at the speeds of 8000, 12000, and 16000 rpm and the feed rates of 1000, 1500, and 2000 mm/min with 3 and 4 machining layers by using two different cutters with a diameter of 8 mm. Rz measurements were made on the obtained surfaces using the needle scanning method according to TS EN ISO 21920-2 (2021). In the measurements perpendicular to the fibers, the lowest Rz values were obtained with cutter type 2 (the cutter of the three cutting edge straight end mill), at 16000 rpm speed, 1000/2000 mm/min feed rate, and four machining layers. In the measurements made parallel to the fibers, the lowest Rz values were obtained at the cutter type 2 (the cutter of the three cutting edge straight end mill), at the speed of 16000 rpm and the feed rate of 1000/2000 mm/min and 4th step-down. A smooth surface (lower Rz value) was obtained with fourth cuts (step down, cut layer) instead of third cuts.

Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model

Milling tool wear, a ubiquitous challenge in industrial automation and manufacturing, leads to diminished equipment utilization, escalating costs, and a decline in product quality. The prediction of tool wear is a complex and challenging task, as it involves numerous variables. This paper introduces a pioneering hybrid approach, the NCA-SMA-GRU model. It involves the hybridization of three major components and is designed to enhance the precision and expedite the process of tool wear prediction. The NCA component is adept at filtering and retaining the most relevant features associated with milling tool wear from the raw signals, and it also improves the model’s interpretability. Subsequently, SMA optimizes the GRU network’s hyperparameters, including the initial learning rate, hidden layer neurons, network training iterations, and the L2 regularization factor, to identify an optimal combination that bolsters predictive performance. The modeling steps and the development of fitness function are explained in detail. The model’s efficacy is rigorously evaluated using data from the 2010 High Speed CNC Machine Tool Health Prediction Contest (PHM 2010), which encompasses wear data from both single and multiple cutters. The performance is compared using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared (R2), and computational time. To assess the ability and characteristics of the proposed approach, other popular hybrid models are constructed for comparative analysis. The results demonstrate that the proposed model addresses the limitations of traditional prediction methods and provides insights into the development of deep learning and optimization algorithms for tool wear prediction.

Develop a postprocessor for 5-axis CNC machines head-table type and head-head type based on determining coordinate transformation matrices

Develop a postprocessor for 5-axis CNC machines head-table type and head-head type based on determining coordinate transformation matrices

A Method for Predicting Wear Behaviours of Roller Linear Guides Under the Influence of the Relative Humidity and Ambient Temperature

The wear of roller linear guides (RLG) has a direct impact on the tooling precision of CNC machine tool. Wear depends on working conditions: load P, speed V, work environment which includes relative humidity RH and ambient temperature T. In this paper the mathematical regression function describes the relationship between the wear amount U of RLG with the three factors P, RH and T built on the basis of experimental research. The results show that the wear regression function U has an exponential form, which is consistent with experiments about the influence of climatic environment. It describes the simultaneous impact of three factors P, RH, T that affect wear of CNC machine tool rolling guide. With Vietnam's hot and humid environmental conditions, relative humidity RH% = 51 - 99%, temperature T = 15 - 450C, and load P = 2 - 6kgf, the influence on rolling guide wear of normal load P is 2 times the humidity RH% and about 3.8 times that of temperature T. Therefore, it is possible to predict the change in wear amount according to two environmental factors when loading P at certain values.

Machining accuracy prediction and adaptive compensation method of CNC machine tool under absence of machining process sensing

Machining accuracy prediction and adaptive compensation method of CNC machine tool under absence of machining process sensing

Kinematics characterizing with dual quaternion and parametric modeling of geometric error terms based on measuring path planning of CNC machine tools

Kinematics characterizing with dual quaternion and parametric modeling of geometric error terms based on measuring path planning of CNC machine tools