CNC Machine Research Articles

Material Removal Rate of Face-Milled Bevel Gears Based on a Ring-Dexel Cutting Simulation

Abstract Face milling is not only a primary machining technique for the mass production of bevel gears, but it has also become a standardized process integrated into CNC bevel gear-cutting machines in the last two decades. Controlling suitable feed rates for face milling is one of the most direct and pivotal factors influencing processing efficiency for CNC machines. Despite being programmed through numerical codes, the feed rates provided by the gear machines most rely on experiential insights rather than optimization. Therefore, leveraging material removal rate (MRR) directly correlates with machining power and will hold immense potential for optimizing feed rates and enhancing efficiency. Because commercial software solutions cannot accurately predict the MRR for face milling operations, this paper uses a novel ring-dexel-based model for cutting simulation to address this issue. The main aim of this model is to provide a more precise prediction of the MRR across all face milling cuttings. By controlling the cutting depth and generating angle, the ring gear's plunging process and the pinion's single-roll generating process were successfully simulated. Thus, the MRRs through all cutting processes were calculated. Experimental results showed that tool torques are positively correlated with the MRRs. Finally, by appropriately increasing the cutting feed rate based on the MRR, the pinion and ring gear machining times were reduced by 44% and 18%, respectively.

Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

The intelligent operation and maintenance method and experimental research of CNC machine tool bearings

As the core components of high-precision rotary machine tools, machine tool bearings are prone to failure or damage, and their running state directly affects the safety and reliability of the entire equipment. Therefore, the paper proposes an intelligent operation and health management method for machine tool bearings based on Bayes-VMD-KPCA, Bayes-CNN-SVM, and health status evaluation index. Aiming at the complex working environment and high nonlinear degree of fault features of machine tool bearings, the Bayes-VMD-KPCA algorithm is used to perform variational mode decomposition of the original signal of the bearing fault, which avoids the influence of background noise and solves the problem of high dimension of multi-domain fault feature set. Second, the CNN-SVM fault diagnosis model uses a convolutional neural network (CNN) to deeply mine the extracted multi-domain fault features, and a support vector machine (SVM) can be used as a fault classifier to complete the diagnosis of various fault types of machine tool bearings. At the same time, the Bayes algorithm is introduced to tune the hyperparameters in the CNN-SVM model to further improve the prediction performance of the CNC machine tool-bearing fault diagnosis model. The results show that the intelligent operation and maintenance and health management method of CNC machine tool bearings proposed in this paper can effectively complete the fault type diagnosis and health status assessment of bearings, and its accuracy index reaches 99.33%. Compared with the single SVM model, Bayes-CNN model, Bayes-SVM model, and CNN-SVM model, the health effect is evaluated. The accuracy is increased by 6.33%, 4.33%, 2.66%, and 1%, respectively. It can be seen that the proposed method can more effectively realize the monitoring and health management of bearing fault.

A novel time-optimal linear toolpath smoothing method based on airthoid and circular splines for CNC machining

A novel time-optimal linear toolpath smoothing method based on airthoid and circular splines for CNC machining

A DT framework integrating human and artificial intelligence for power consumption prediction in CNC machining

A DT framework integrating human and artificial intelligence for power consumption prediction in CNC machining

A real-time dual NURBS interpolator with optimised control of flexible acceleration and deceleration for five-axis CNC machining

The limited computing capacity makes it difficult to plan a suitable feedrate profile in real-time for high speed and high accuracy machining of five-axis parametric toolpaths. In this paper, a real-time interpolation algorithm with optimised control of flexible acceleration and deceleration (acc-dec) for the dual NURBS toolpath is proposed. The toolpath is marked as subsegments with similar geometric properties by introducing the five-axis curvature. Machine kinematic and toolpath geometry constraints are considered in the kinematic parameter constraint model. Initial feedrate profiles are solved in a dynamic 3D window which preserves the motion performance of machine tools to a great extent. Convolution is used to smooth the initial feedrate profile to achieve a higher order continuity over the global range. Feedrate fluctuations caused by imprecise parameter interpolation are eliminated through modifying each interpolation periods. Resampling adjusts the position of interpolation points and unify the interpolation periods. All operations mentioned are in series and real-time is strictly guaranteed. Effectiveness of the developed algorithm is validated in simulations and also experimentally on a Self-developed-NC controlled 5-axis machine tool.

Hierarchical Control in Mechatronic Technological Systems

The topic of hierarchical control of technological machines is one of the most relevant in mechanical engineering technology. The most difficult issue in this area is the organization of interactions between different control levels, on the one hand, and the choice of automatic control methods for each of these control levels (control by deviation, control by disturbance, mixed control, etc.), on the other. In this article, in relation to machining technology, a method and corresponding device are proposed that make it possible to implement the control of cutting force parameters (axial cutting force and cutting torque) in an automatic control system for the deviation of cutting torque by changing the axial cutting force (lower level of control). The lower-level control ensures the required quality of the surface layer (surface integrity) of the machined parts. At the same time, the required dimensional accuracy of parts is ensured at the upper level of control, which is implemented by the CNC system of the machine. At the upper level, automatic control is carried out based on the deviation of the kinematic parameters of the movement of the working parts of the CNC machine (acceleration, speed, displacement). Control switching from upper to lower level and back is carried out without using a spindle linear axial movement sensor. Instead of this expensive sensor, a limit switch (a closed and opened pair of contacts) is used, which fixes the lowest axial position of the spindle (and cutting tool). Based on the signal of closing the specified contacts of the limit switch, a transition from the lower control level to the upper one is carried out. Thus, the upper-level system operates only when these contacts are closed, and the lower-level system operates only when they are open. In relation to the upper-level system, the lower-level control system implements the control “by disturbance” principle, also known in control theory as the “disturbance compensation principle”.

Time-Bound Optimal Planning in CNC Machine Considering Machining Safety

Time-Bound Optimal Planning in CNC Machine Considering Machining Safety

Effect of Machining Parameters on the Surface Roughness of Hot Die Steel

H13 tool steel is frequently used in hot work dies, hot forging, hot extrusion, and mold materials for casting ferrous and nonferrous materials. Surface roughness is of key importance in case of dies and moulds, as it insures the finish of the final product. After studying the work done by the researchers in field of machining and its impact over surface roughness, it was found that machining behaviour of H13 steel at the annealed state is not being studied. The surface finish obtained during turning of H13 steel are not studied on a greater scale, so the present study includes the analysis of finish (surface roughness) generated. Turning operation is carried out on the CNC machine at dry condition with an aim to find systematically the effect on surface roughness by distinct machining variables like feed rate, depth of cut and speed. RSM model using Box Behnken design is used to predict the roughness developed during turning of annealed H13 steel using carbide tool. It was found that feed rate and the depth of cut have a direct relation with the surface finish and the value of surface roughness increases with increase in these parameters. On the other side, cutting speed has a less impact over the surface roughness & is inversely related.

Application of PCA-LSTM algorithm in predicting component faults in CNC machining centers

Abstract In order to further explore the relationship between component failures and CNC machining center failures, a method of predicting component failures in advance was proposed to reduce the likelihood of machining center failures and improve CNC machining efficiency. In this paper, the principal component analysis method is used to optimize the Long short-term memory neural network to build a network model to study and predict the fault status of NC machining center components in work. The use of Principal Component Analysis (PCA) to extract the main features of machine tool faults can improve the speed of iterative training networks during the machining process and reduce the input dimension and prediction difficulty of LSTM. The extracted data is used as input for the LSTM network. The fault type is output. The comparison of diagnostic results between PCA-LSTM, LSTM, and BP neural networks shows that PCA-LSTM is a high-precision prediction model. The root mean square difference is reduced to 1.572. The predicted error results can effectively reflect the changes that occur in the machining center components with the occurrence of faults. It can be seen that it is feasible to use PCA-LSTM as a tool for predicting the faults of CNC machining center components.

Hybrid manufacturing cost models: Additive friction stir deposition, measurement, and CNC machining

Based on its potential to reduce lead times, hybrid manufacturing, which often includes both additive manufacturing and machining processes, is receiving more attention from manufacturers as they seek to increase their supply chain resilience and efficiency. A new solid-state additive manufacturing, referred to as additive friction stir deposition (AFSD), has shown the potential to become an important process for hybrid manufacturing. To justify the selection of a hybrid manufacturing approach, the cost needs to be estimated for comparison to conventional approaches. Historically, hybrid manufacturing costs have been difficult to estimate due to the complexity and diversity of the manufacturing processes. This paper proposes cost models that include additive friction stir deposition, structured light scanning, milling, and turning, which can be combined in hybrid manufacturing process planning. These cost models are demonstrated in a case study and cost estimates are compared for hybrid and conventional (machining-only) manufacturing approaches. For the selected case, the hybrid manufacturing process cost was $1007.58, while the conventional milling process cost was $833.60. The results of the case study show that both labor and material costs must be considered to make an informed decision between hybrid and conventional manufacturing approaches.

An unconstrained and non-redundant identification method of geometric errors and compensation of machine tools by X-AX Laserbar

Efficient and accurate measurement and identification of geometric errors are crucial for improving the precision of CNC machine tools. The X-AX Laserbar, as a novel tool for indirect measurement, has not been extensively studied for the identification of geometric errors in machine tools. In this paper, the geometric error model for a three-axis machine tool is established to illustrate the multilateration measurement principle of the laserbar, and a non-redundant and unconstrained identification method is proposed to identify these geometric errors. This method avoids the use of redundant parameters and additional constraints by employing pose error twists to describe the geometric errors. These pose error twists are identified in a transitional coordinate system, and then the geometric errors will be identified in the machine coordinate system by deriving the relationship between the pose errors and geometric errors. The proposed method is validated with the VMC-850E three-axis machine tool. The geometric error measurement using a laserbar is completed in about 40 min, showing great efficiency. The experimental results indicate that the proposed method is capable of accurately identifying the 17 geometric errors required for error compensation. The identified geometric errors are then applied to the machine tool’s accuracy improvement through error compensation. The results show that the actual geometric errors are controlled to a low level. The proposed method can efficiently measure the geometric errors of three-axis machine tools and contribute significantly to improving their geometric accuracy.

High-resolution time-series classification in smart manufacturing systems

High-resolution time-series data is increasingly prevalent in today’s smart manufacturing systems driven by the proliferation of sensors and data acquisition technologies. However, classification tasks on such data face challenges like collinearity, model instability, poor generalization, and overfitting. This paper investigates techniques capable of handling high-resolution time-series classification (TSC) tasks in the manufacturing domain. High-resolution time-series data presents challenges impacting the concepts essential for classification algorithms. Additionally, increased noise and the extensive feature space further complicate classification performance, emphasizing the need for innovative solutions to process this data efficiently on resource-constrained devices. In this paper, we address this research gap by defining the problem in depth, investigating possible solutions, and evaluating a subset of solutions in a manufacturing case study. We utilize a CNC machining dataset with over 100,000 data points per time-series. We examine algorithms representing two main approaches: feature engineering for data transformation and dimensionality reduction, and advanced machine learning and deep learning models to capture long-term dependencies. Specifically, we implement Discrete Fourier Transform, Symbolic Aggregate Approximation, Symbolic Fourier Approximation, ARSENAL, DrCIF, and ResNet algorithms for TSC. Experimental results highlight the trade-off between accuracy and computational expense. While ARSENAL and DrCIF achieve high accuracy, their runtimes are notably high. Feature engineering methods offer competitive accuracy with very low runtimes, and ResNet strikes a compelling balance by surpassing benchmarks in accuracy while maintaining reasonable computational costs. These findings can aid in adopting appropriate tools to successfully harness manufacturing big data to derive actionable insights for process improvements and optimization. The novelty of this research lies in addressing the specific challenges of classification tasks on high-resolution time-series data within the manufacturing domain, exploring diverse techniques, applying them to a case study, and providing a nuanced analysis of algorithmic performance with practical implications.

Simulation of Motion Nonlinear Error Compensation of CNC Machine Tools With Multi-Axis Linkage

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified.

Additive Optimization Method for Choosing CNC Machines for Technological Preparation of Machine-Building Production

The introduction of Industry 4.0 into the technological preparation of production leads to the optimization of processes, including the processes of selecting equipment and tools, reducing cycle time, and reducing costs. Technological preparation of production involves a set of works that make it possible to start manufacturing a new product in a given volume. Features of technological preparation of production when using CNC machines follow from the fact that a significant part of the work from the sphere of direct production is transferred to the area of its technological preparation. Technological preparation of production plays an important role in the successful functioning of a machine-building enterprise. Optimization of equipment selection allows you to increase productivity, reduce costs and improve product quality. The use of modern optimization methods and information technology allows you to make this process more efficient and accurate. The article provides a methodology for calculating the additive criterion for optimizing the selection of the best option for CNC machines.

Sensitivity analysis and compensation for tooth surface deviation of spiral bevel gear machine tool

To analyze the quantitative mapping relationship between the geometric errors of the five-axis CNC machine tool and the tooth surface deviation of spiral bevel gears, and identify the key geometric errors that affect the machining deviation of the spiral bevel gear tooth surface, a sensitivity analysis method for tooth surface machining deviation of spiral bevel gear based on screw theory and EFAST method is proposed. Firstly, the machining model of the spiral bevel gear tooth surface based on the five-axis CNC machine tool is established. Then, the geometric error transfer model of the machine tool is established based on the screw theory. Combined with the EFAST global sensitivity analysis method, the key geometric errors that affect tooth surface deviation are identified. Finally, the reliability and effectiveness tests are performed by comparing the results to the local sensitivity analysis method and simulating compensation for the key geometric errors. The result of compensation shows that the tooth surface deviation is reduced significantly according to global sensitivity analysis. The feasibility of applying sensitivity analysis to control the tooth surface deviation of spiral bevel gears has been demonstrated.

Fabrication and Performance Analysis of an Arduino-based Mini CNC Machine with Drilling and Engraving Tool

Abstract: The present work focuses on fabrication of a low cost numerically controlled machine with easily available materials and devices. The machine has been primarily fabricated to carry out a few low duty mechanical applications in a short period of time as well as at a minimal cost. Engraving tool of diameter mm and drill bit of diameter mm are used in our operations. Foam, Plywood, Aluminium sheet and Wood have been used as work-piece material. Study has been carried out to observe performance analysis on the above work-piece materials. Mathematical analysis has been done for different operating parameters like cutting speed, feed, depth of cut, machining time and MRR (Material removal rate) . It has been observed that for low duty applications, small size numerically controlled machines are more effective as well as advantageous compared to conventional large size cutting machines.

A novel feedrate scheduling method using modifying S-shaped feedrate profile with a round-off error elimination approach for CNC machining

A novel feedrate scheduling method using modifying S-shaped feedrate profile with a round-off error elimination approach for CNC machining

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

CNC Learning Innovation: Improving Students' Self-Efficacy and Creativity through Peer-PBL

The demand for increasingly diverse product designs and specifications in the manufacturing industry requires CNC machine operators to have high technical skills and strong self-efficacy and creativity. High self-efficacy allows operators to be more confident in applying their technical skills, while creativity helps them solve problems and challenges during the production process. This study aims to improve students' self-efficacy and creativity by applying the Peer-Project Based Learning (Peer-PjBL) model, which combines aspects of project-based learning with peer-to-peer learning. The method used in this research is Classroom Action Research (PTK), which consists of two implementation cycles. Each cycle involves planning, implementation, observation, and reflection stages. The subjects of this study were 20 students who took the CNC Machining Technology course at Yogyakarta State University. Data were collected using a questionnaire that measured self-efficacy and student creativity indicators and then analyzed descriptively. The results showed that applying the Peer-PjBL method significantly increased students' self-efficacy and creativity in each research cycle. This improvement can be seen in students' ability to face new challenges independently, as well as their ability to generate diverse creative ideas in solving problems. This research makes an important contribution to the development of learning methods in vocational education by emphasizing the improvement of technical abilities and non-technical skills, such as self-efficacy and creativity, which are needed in the modern world of work.