Computer Numerical Control Machine Research Articles

Properties of Tungsten Carbide Rings When Compressed to Failure or Cut with a Diamond Grinding Bit: A Biomechanical Study.

Tungsten carbide rings are increasingly popular modern jewelry items. Tungsten carbide is an extremely dense, hard metal. Previously described methods to remove tungsten carbide rings include using locking pliers to compress the ring or cutting the ring with a high-speed dental drill. A universal mechanical testing system (MTS) machine was used to diametrically compress tungsten carbide rings 9 mm in length and 2.4 mm in thickness with a 23.4-mm outer diameter and a 21.0-mm inner diameter while measuring the force required to fracture the rings. A computer numerical control (CNC) machine was used to cut tungsten carbide rings with a diamond grinding bit with and without a flow of normal saline solution. An infrared thermal camera was used to record the temperature at the site of contact between the ring and the grinding bit and at a site one-quarter of the circumference along the ring. A mean force with 95% confidence interval of 3.7 ± 1.2 kN was required to fracture the tungsten carbide rings via diametral compression (p = 0.05). The rings failed at a mean displacement with 95% confidence interval of 0.32 ± 0.13 mm (p = 0.05). The maximum temperature observed at the site of contact between the ring and grinding bit averaged 160.2°C including cases with and without coolant. The time to reach maximum temperature and the duration of maximum temperature differed significantly between the cases with and without coolant (p = 0.0007 and p = 0.0108, respectively). Tungsten carbide rings fractured with minimal displacement using a small amount of force via diametral compression. The brittle fracture pattern of the rings produced minimal comminution. Tungsten carbide rings reached extreme temperatures when cut with a high-speed diamond grinding bit despite cooling with normal saline solution. Previously documented methods to remove a tungsten carbide ring include breaking the ring by compressing it with pliers or cutting it off with a high-speed dental drill. Clinicians should be aware of potential complications of current methods to remove tungsten carbide rings.

A review of artificial intelligence application for machining surface quality prediction: from key factors to model development

Abstract This article presents a comprehensive review of the state-of-the-art advancements in applying artificial intelligence (AI) techniques, especially machine learning and deep learning models, to forecast surface quality in computer numerical control (CNC) machining. Surface quality is influenced by a wide range of factors, which makes its prediction a complex and significant challenge. The factors affecting surface quality are reviewed and categorized into two key elements—tool center positioning errors and the interaction between the tool edge and workpiece materials. As highlighted in recent research of less than five years, the factors are systematically organized into the key elements and presented in tabulated form. Then, particular emphasis is placed on how recent AI techniques have incorporated these factors, addressing the capability of machine learning and deep learning methods to handle the complexity and variability inherent in machining surface quality prediction (MSQP). Moreover, further review is conducted to highlight how advanced AI techniques, particularly transfer learning techniques, have enabled accurate and adaptive MSQP despite data scarcity conditions due to costly experiments and diverse machining conditions. By comprehensively reviewing recent studies from the perspective of the analysis results of key elements affecting surface quality and the inherent characteristics of data-driven AI techniques, this paper identifies the strengths and limitations of various machine learning and deep learning approaches applied in MSQP. Based on the insights into the state of the art, future research directions are discussed for improving prediction accuracy, computational efficiency, and real-time monitoring in the domain.

Catch-up of CoPs in GIS and IVC: evidence from Chinese CNC machine tools

Abstract In the era of innovation globalization, realizing technology catch-up poses a challenge to late-coming countries. This paper integrates the conceptual framework of the global innovation system (GIS) and the model of the innovation value chain (IVC) and takes the high-grade computer-numerical-control(CNC) machine tools’ catch-up in China as a case study to analyze the characteristics and mechanisms for the catch-up of complex product systems (CoPs) in different globalization historical stages from the perspective of research and development (R&D), transformation, and commercialization. We find that strengthening the GIS–IVC-based resource integration of innovation for R&D, pilot and demonstration plants, and commercialization is the key factor in promoting the catch-up of CoPs in emerging economies, and effective guidance of innovation policies is essential for promoting the catch-up of CoPs in the global era.

Enabling high-throughput quantitative wood anatomy through a dedicated pipeline.

Throughout their lifetime, trees store valuable environmental information within their wood. Unlocking this information requires quantitative analysis, in most cases of the surface of wood. The conventional pathway for high-resolution digitization of wood surfaces and segmentation of wood features requires several manual and time consuming steps. We present a semi-automated high-throughput pipeline for sample preparation, gigapixel imaging, and analysis of the anatomy of the end-grain surfaces of discs and increment cores. The pipeline consists of a collaborative robot (Cobot) with sander for surface preparation, a custom-built open-source robot for gigapixel imaging (Gigapixel Woodbot), and a Python routine for deep-learning analysis of gigapixel images. The robotic sander allows to obtain high-quality surfaces with minimal sanding or polishing artefacts. It is designed for precise and consistent sanding and polishing of wood surfaces, revealing detailed wood anatomical structures by applying consecutively finer grits of sandpaper. Multiple samples can be processed autonomously at once. The custom-built open-source Gigapixel Woodbot is a modular imaging system that enables automated scanning of large wood surfaces. The frame of the robot is a CNC (Computer Numerical Control) machine to position a camera above the objects. Images are taken at different focuspoints, with a small overlap between consecutive imagesin the X-Y plane, and merged by mosaic stitching, into a gigapixel image. Multiple scans can be initiated through the graphical application, allowing the system to autonomously image several objects and large surfaces. Finally, a Python routine using a trained YOLOv8 deep learning network allows for fully automated analysis of the gigapixel images, here shown as a proof-of-concept for the quantification of vessels and rays on full disc surfaces and increment cores. We present fully digitized beech discs of 30-35cm diameter at a resolution of 2.25 m, for which we automatically quantified the number of vessels (up to 13 million) and rays. We showcase the same process for five 30cm length beech increment cores also digitized at a resolution of 2.25 m, and generated pith-to-bark profiles of vessel density. This pipeline allows researchers to perform high-detail analysis of anatomical features on large surfaces, test fundamental hypotheses in ecophysiology, ecology, dendroclimatology, and many more with sufficient sample replication.

Fast and high precision control approach: polyline analysis and optimal NURBS interpolation for CNC machine tools

Abstract In computer numerical control (CNC) operations, smooth motions generated using a parametric curve such as non-uniform rational B-splines (NURBS), Bezier curve, and B-spline are crucial for surface quality and machine control. A parametric curve is formed by micro-lines in CAD/CAM software; however, instead of micro-lines, polylines are used to define a parametric curve to prevent an increase in NC-file size. It is also possible to see situations where smooth motion is not required due to production requirements. In these cases, point-to-point motion is necessary instead of smooth motion. In this study, a polyline analysis algorithm is proposed to classify the normal line and polyline within the NC-file. A global tool path smoothing method is also applied, where the data points in a polyline group are converted into NURBS because CAD/CAM software commonly uses NURBS as a parametric curve for precision control of CNC machines. In addition, this study presents an optimal feedrate scheduling algorithm for the NURBS curve that generates an optimal feedrate profile for each segment by considering critical parameters such as segment length, initial and final velocity of each segment, maximum jerk, acceleration, and velocity. As the proposed algorithm is compatible with existing feedrate scheduling methods in which different feedrate values at the transition point of the NURBS segments are determined, this study provides an important contribution to the literature. The proposed methods in this study are verified by operating a spherical surface using industrial NC-files on a three-axis CNC-milling machine. The proposed methods demonstrate effective performance in experiments that require precision production operations.

Benchtop Machining of Self-Standing Alumina Doughs for Low-Number Fabrication and Prototyping.

Cold isostatic pressing, gel casting, and protein coagulation are the most common techniques to produce green bodies prior to computer numerical control (CNC)-based machining for the near-net-scale shaping of ceramics. These methods typically involve various additives and entail several steps to create a green body that is capable of withstanding machining forces. Here, utilizing a single additive, we first introduced a facile benchtop method to generate self-standing, malleable doughs of alumina in under 2 min. We then optimized the parameters of CNC machining to obtain surfaces with minimum surface roughness and produced custom-sized crucibles as a showcase for low-number production. To consolidate a dough from highly loaded suspensions of alumina, we employed a poly(ethylene glycol)-grafted random copolymer of acrylic acid and N-[3-(dimethylamino)propyl]methacrylamide at 0.75 wt % with respect to the weight of alumina powder. We surveyed machining parameters with spindle speeds ranging from 5000 to 30000 rpm and cutting speeds from 1000 to 1800 mm/min using 1 and 2 mm tool sizes. The highest surface quality, characterized by the minimal surface roughness as evaluated by profilometry, was achieved at a spindle speed of 20000 rpm and a cutting speed of 1200 mm/min with a 1 mm tool and at a spindle speed of 15000 rpm and a cutting speed of 1800 mm/min with a 2 mm tool. Upon sintering, the hardness of the machined samples was measured to be 15.16 ± 1.15 GPa. Additionally, we demonstrated the recycling of alumina (up to 30 wt % of alumina content) sourced from intentionally broken parts in the green state. The recycling scheme contributes to the lowering of the use of resources and emphasizes the possibility of a greener future for ceramic production on a broader scale. Overall, this cost-effective and easy-to-implement methodology starts at the materials formulation level and parametrization of the machining paves the way for immediate industrial adaptation.

Minimizing the drilling robot arm movement by the advanced Dhouib-Matrix-4 metaheuristic

Drilling holes on a Printed Circuit Board is a complicated task and it is usually ensured via a Computer Numerical Control (CNC) machine using a drilling robot arm. Commonly, to increase the performance of the CNC, the movement of its robot arm is minimized via the optimization of the holes drilling route. Hence, different metaheuristics are used to plan the shortest drilling route. In this purpose, we designed a novel metaheuristic namely Dhouib-Matrix-4 (DM4) and in this paper DM4 is enhanced with a Tabu list (T) and adapted to plan the shortest route for the drilling robot arm: the proposed method is entitled Dhouib-Matrix-4-Tabu (DM4-T). In fact, DM4-T combines two techniques in a multi-start structure: (i) An initial basic solution is generated using the novel Dhouib-Matrix-TSP1 heuristic (ii) this initial solution is intensified via the innovative Far-to-Near local search technique with the Tabu list. Experimental results indicate the performance and robustness of DM4-T to solve several instances up to 2152 holes. Indeed, DM4- T outperforms several existing metaheuristics in the literature such as Genetic Algorithm, Ants Colony Optimization, Integrated Genetic Simulated Annealing and Modified Shuffled Frog Leaping Algorithm.

Product Quality Output Measurement for Preventive Maintenance on Computer Numerical Control (CNC) Machines at an Electronic Manufacturing Industry

Computer Numerical Control (CNC) machines remove material from a blank or workpiece using digital controls to produce custom-designed parts. Maintaining their accuracy and precision under challenging conditions after long-term usage is crucial. This study aims to evaluate CNC product quality using Overall Equipment Effectiveness (OEE) and enhance long-term performance through data-driven approaches. The method of this study focuses on analyzing scrap rate data, employing a u-chart to monitor stability, and applying machine learning regression models—K-Nearest Neighbour (KNN) and Random Forest (RF)—to forecast scrap rates. These forecasts help identify when preventive maintenance is necessary, preserving machine precision over time. This study also applied visualization of results with Microsoft Power BI to enhance data interpretation, aiding quick responses to potential problems. Results indicate that RF outperforms KNN in predicting scrap rates. Stacking these models further improves accuracy, offering a more reliable decision-making tool for anticipating quality issues. By detecting anomalies early, manufacturers can implement timely maintenance, minimizing downtime and prolonging CNC machine lifespan. In conclusion, integrating scrap rate analysis, statistical process control, and advanced machine learning techniques can maintain product quality and reduce inaccuracies. Companies should include more proactive maintenance planning by employing better forecasting.

Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.

Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards.

"Man vs. Machine: 3D Milling of Auricular Frameworks".

Autologous rib harvest with manual framework production is the current gold standard for microtia reconstruction. Recent clinical success with implantation of cadaveric costal cartilage grafts opens the possibility of point of care auricular framework production. This paper assesses the feasibility and efficiency of 3D milling of cadaveric costal cartilage for auricular framework production. A Nagata/Firmin style auricular framework was manually carved en bloc out of soap and 3D scanned to design a milling toolpath on a desktop 3-axis computer numerical control (CNC) machine. An en bloc framework was then milled from cadaveric costal cartilage. Time to mill a complete framework was recorded. The dimensional/volumetric analyses were performed. The main outcome measures were total time to mill a complete auricular framework out of cadaveric cartilage and dimensional/volumetric comparisons to the model ear. Total milling time for the cartilage framework was approximately 7 min. Finalizing steps took an extra 20 min. Total time to produce a final framework ready for implantation was approximately 27 min, compared to the traditional 1-2 h by manual carving. All dimensional comparisons were within 2 mm to the manually carved model. Volumetric analysis showed 71% similarity. En bloc cadaveric costal cartilage framework milling is both feasible and efficient. 3D milling significantly reduces framework production time and allows for accurate reconstruction of the complex ear geometry, which can translate to cost savings, optimized patient safety, and potential for patient-specific reconstruction. The next step toward achieving clinical application is ensuring framework sterility.

Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.

Laser Powder Bed Fusion Manufactured Hydrostatic Journal Bearings for Turbomachinery: Design and Measurements of Static Load Testing With Air, Water, and Liquid Nitrogen

Abstract Modern turbomachinery demands simplified components and compact shaft support systems. This study introduces novel hydrostatic journal bearings incorporating multiple tilting pads and fluid feeding features, which are manufactured with Direct Metal Laser Melting (DMLM) technology, an advanced laser powder bed fusion manufacturing process. Two test bearings, fabricated by fusing extremely thin layers of Inconel 718 powder, incorporate fluid external feed pressurization features without the need for recesses. One test bearing features unique internal feeding hole configurations, which combine multiple holes with an angled injection feature, while the other test bearing includes distinctive porous layers on the bearing pad surfaces. This paper provides a comprehensive overview of the design, fabrication process, and static load characteristic measurements of these innovative bearings. A simple static load test rig is used to measure the flow rate, temperature, and stiffnesses of the test bearings while supplying compressed air, water, and liquid nitrogen with increasing static loads. The performance of these bearings is compared with counterparts produced using computer numerical control (CNC) machining, a conventional subtractive manufacturing process. In general, the bearings lubricated with liquid nitrogen exhibit a higher load capacity compared to those lubricated with air. This study confirms the static load performance of metal three-dimensional printed hydrostatic journal bearings, marking a significant advancement in the application of additive manufacturing for turbomachinery bearings. The findings offer valuable insights into the potential of additive manufacturing in producing bearings for extreme operating conditions and various lubricants.

Optimization strategy of computer numerical control machining process parameters in biomanufacturing mold

With the rapid development of biomanufacturing technology in the medical and pharmaceutical fields, the demand for high-precision and high-quality molds has surged. When Computer numerical control (CNC) machining biomanufacturing molds, the optimization of process parameters becomes the key to improve efficiency and quality. The purpose of this study is to explore the optimization strategy of CNC machining process parameters to achieve the best surface quality, dimensional accuracy and machining efficiency. Through literature review, the spindle speed, feed speed and cutting depth are selected as the key parameters, and the multi-objective optimization model is constructed by response surface method, which is solved by genetic algorithm. The experiment shows that the process parameters of CNC system in mold manufacturing are cutting speed 100 (m/min), feed rate 0.2 (mm/rev) and cutting depth 0.5 (mm), which will effectively reduce the manufacturing cost, and effectively control the alarm times within 35 times in different processing equipment, greatly reduce the risk. The optimization strategy can significantly improve the surface quality and productivity of the mold and reduce the cost. The comparative analysis verifies the effectiveness of the method, which provides new theoretical and technical support for CNC machining in the field of biomanufacturing.

Identification of optimal cutting parameters regarding lacquer wettability of CNC processed MDF panels by artificial neural network

ABSTRACT The primary objective of this study was to evaluate the surface roughness and lacquer wettability of medium-density fiberboard (MDF) panels processed on computer numerical control (CNC) machine. Then, it is aimed to determine the optimal CNC cutting parameters by modeling the data obtained from the experiments with ANN. For these purposes, two cutting tool types, two toolpath strategies, three spindle speed and four cutting depth values were used as CNC cutting parameters. The roughness and lacquer wettability tests were performed on the surfaces of MDF specimens after CNC machining. Using the obtained experimental data, the ANN models with the highest predictive ability were determined and the optimum CNC cutting parameters were defined according to surface roughness and lacquer wettability. The smoothest surfaces were obtained for the spiral router bit with offset toolpath strategy, 22,500 rpm spindle speed and 1.6 mm cutting depth, and for the straight router bit with raster toolpath strategy, 21,500 rpm spindle speed and 1.7 mm cutting depth. The optimal cutting parameters that gave the best lacquer wettability were an offset toolpath strategy, 20,500 rpm spindle speed and 1.5 mm cutting depth for spiral router, and a raster toolpath strategy, 20,500 rpm spindle speed and 3.1 mm cutting depth for straight router.

Informatization model of Guqin based on grasshopper and CNC machining

ABSTRACT The integration of Computer-Aided Design (CAD) and Computer Numerical Control (CNC) machining has brought modern manufacturing techniques into the craft of Guqin crafting, introducing the concept of Industrialized Standardized Guqin Crafting (ISGC). This study proposes the concept of Guqin Information Modeling (GIM), on the basis of which, the parametric modeling method of Guqin is proposed to realize the parameter control of the key position of Guqin; combined with the CNC machining technology, it verifies the production of Guqin of different materials and shapes, summarizes the method of selecting the tools for CNC machining of Guqin and the machining parameters of each process, and puts forward the Industrialized Standard of Guqin Carving (ISGC). The proposal of GIM and ISGC provides information technology support for the research of the sound quality of Guqin and the protection of Guqin.

Design and building CNC engraving router machine using Arduino Uno with GRBL control system

Computer Numerical Control Machines are one of the advances in automatically operated machining technology that can meet the need for complex and high-precision products. CNC machine parts generally have a mechanical and electrical system, also called a machine controller, that uses a Human Machine Interface at a high price. One of the software used to control the CNC machine is Arduino Uno, a PC-based GRBL System with a low price and the same function. This software is not far from the advantages of the HMI panel. Operating the machine with this software is also not that difficult. In this research, we intend to make a CNC machine using the Arduino Uno control system and software with a PC-based GRBL System, making it easier for operators to operate it. The results of the research that has been carried out show that the CNC has a size of 800x600x50mm with a system that is operated well and precisely

Study on the analytical verification method of geometric error models

Abstract Geometric error compensation is a widely used and effective approach for enhancing the accuracy of Coordinate Measuring Machines (CMMs) and Computer Numerical Control (CNC) machines. The research in this field primarily focuses on the challenging task of developing geometric error modelling techniques. Currently, there are several well-established methods for modelling geometric errors. However, there is scarce availability of methods that can validate the established geometric error models. Therefore, this article proposes an analytical validation method for geometric error models based on the error transformation equation. The principle of the method is elaborately explained, and its correctness is verified through specific model verification example. The method proposed in this article enables rapid and accurate model correction and iteration, effectively reducing the economic and time costs of model validation in the current geometric error compensation process. Therefore, the method provides guidance for standardising the modelling process and could be widely applied to various structural forms of measurement and machining systems.

Fault diagnosis of computer numerical control machine tools table feed system based on digital twin and machine learning

Fault diagnosis of computer numerical control machine tools table feed system based on digital twin and machine learning

Implementation and Evaluation of a Smart Machine Monitoring System under Industry 4.0 Concept

Implementation and Evaluation of a Smart Machine Monitoring System under Industry 4.0 Concept

AI-based spatially resolved parameter prediction in laser metal deposition for increased process stability

In the additive manufacturing of components using powder-based laser metal deposition (LMD), the heating of the volume during buildup is a decisive factor for process stability and contour accuracy. If the process parameters remain constant, this intrinsic heating leads to deviations in the deposited layer thickness during the process because of changes in the melt pool volume. This leads to contour deviations and potentially causes the process to fail if the process parameters are no longer within the suitable range. Particularly in the case of complex geometries, this previously required time-consuming process development for adapted process parameters and buildup strategies. This paper examines the potential of data-driven approaches to enhance the stability and precision of LMD processes. To this end, a machine learning (ML) model is employed to optimize laser power settings. The objective of the study is to reduce the thermally induced geometric deviations that often occur during the LMD process by utilizing experimental data. The methodology employs the use of the alloy Inconel 718, renowned for its high strength and temperature resistance, in conjunction with the utilization of computer numerical control machines equipped with laser and imaging systems. The ML model is trained to predict the optimal laser power required to obtain consistent melt pool properties. The results demonstrate that the ML approach is an effective means of reducing geometric deviations.