On-line contour error estimation and control for corner smoothed five-axis tool paths

A realtime curvature-smooth interpolation scheme and motion planning for CNC machining of short line segments

The enveloping surface is formed of characteristic lines (also called contact line) at all different enveloping moment, a novel CNC (computerized numerical control) machining method based on enveloping characteristic for enveloping surface is proposed in this study. In the proposed method, the characteristic line is the machining unit, the CNC machining is driven by the characteristics lines, the CNC machining tool path distribution along the characteristics lines rather than the traditional CNC machining tool path such as contour layered,etc. In addition, the envelope characteristic automatic recognition algorithm and kinematics model of CNC machining are proposed to achieve the CNC machining based on enveloping characteristic. Finally, the proposed CNC machining method and traditional CNC machining method for single-enveloping surface and double-enveloping surface are conducted as, for instance, the machining experiment results confirm that the proposed methodology enables the machining precision and efficiency to be successfully improved. Hence, the novel CNC machining method proposed in this study is an effective technology to further improve the machining precision and efficiency of enveloping surface.

The contour error cross-coupling control is very important to improve contour accuracy for the five-axis computer numerical control machine. The change in cutter orientation can cause the nonlinear motion of cutter tip, so the contour error calculation and compensation are very complex. For the five-axis computer numerical control machine with dual rotary tables, the forward kinematics model and inverse kinematics model are set up, and the influence laws of feed axis deviation to cutter tip deviation and cutter orientation deviation are studied. Then the simple and accurate calculation models for cutter tip contour error and cutter orientation contour error are established. Finally, the novel contour error cross-coupling control method based on kinematics model is developed. The experimental results show that the developed method can effectively improve contour accuracy of the five-axis computer numerical control machine.

Contour error, primarily induced by the coupling of multi-axis dynamics and servo lag, is a critical factor limiting the machining accuracy of computer numerical control (CNC) machine tools during complex surface machining. Traditional theoretical modeling methods based on dynamics and parameter identification are difficult to cope with complex nonlinear errors. To address the challenges, this study proposes a data-driven deep learning method to achieve high-precision prediction of contour errors. In this paper, the VMC2100B vertical machining center is used as the experimental platform, and the high-speed dynamic coordinate data acquisition research is carried out with the help of dynamic coordinate acquisition technology, which provides strong data support for the training of deep learning model. A hybrid deep learning prediction model named CLA-Net (CNN-LSTMAttention Network) was constructed: 1D Convolutional Neural Networks (1D-CNN) were employed to extract local spatial features, Long Short-Term Memory networks (LSTM) were used to learn long-term temporal dependencies, and an Attention was introduced to dynamically focus on critical historical information. The experimental results show that the proposed CLA-Net model can control the mean square error (MSE) at about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 ~ \mu m}$</tex>, which verifies the effectiveness and superiority of the model in the prediction task. This study provides a new solution for the contour error prediction task of five-axis machine tools.

Computer numerically controlled (CNC) machines have long been known and used in various industrial sectors such as the aircraft, shipping, automotive, molding and others industries. Various types of existing CNC machines such as CNC turning, CNC milling, CNC shaping, CNC drilling, CNC laser cutting and others have been commonly used in various manufacturing industries. CNC machines for production units and training unit CNC machines can be used according to milling machining parameters, machine capacity, product quality targets and workpiece material types. In milling machining using a 3-A training unit CNC machine, the first step is to design a workpiece, design G and M codes, simulate it using software such as CAD / CAM and can also be done by simulating it with a pen plotter. After evaluating and the simulation results are in accordance with the workpiece design, it can be executed for product machining. Different designs of CNC machining products have different toolpaths. In general, there are three types of techniques for path generation tools, namely iso-parametric, iso-planner, and iso-scallop height. Tool path generation on UMSU CNC milling machining using the CNC TU-3A has been developed a toolpath generation suitable for the UMSU machining. The type of tool path generation technique to be used can be considered from several factors including the complexity of the product design, the type of milling tool used, product quality, machining time and other factors.

High accuracy contour error estimation and direct contour error control are two major approaches to reduce the contour error. However, two key factors make them complex for five-axis machine tools: the nonlinear kinematics and the coupling between the tool position and orientation. In this study, by finding the reference point nearest to the current actual position, and interpolating the point with two neighboring reference points and using the distance ratio, a new contour error estimation method for five-axis machine tools is proposed, which guarantees high accuracy while depending on only the reference points. By adding a weighted contour error on the tracking error in the workpiece coordinate system, and specifying a desired second-order error dynamics based on the error variable, an effective contouring control method is proposed, which can alleviate the problem: when the contour error components are introduced into the controller, the contour errors increase instead in some regions of the tracking trajectory. A series of experiments are performed on a tilting-rotary-table (TRT) type five-axis machine tool. The results reveal that the proposed estimation method has high accuracy, and compared with the case without contour error control, the proposed control approach can reduce the contour error along the whole trajectory.

Nonlinearity Error Analysis with the Application of the Envelope Surface in Five-axis CNC Machining

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.

In contour following applications, one of the main concerns is how to reduce contour errors. The common approach to this problem is to design the contour controller based on the real-time contour error information. However, for the free-form contour following tasks, there is a lack of effective algorithms for calculating contour errors in real time. To overcome this difficulty, this study proposed a real time contour error estimation algorithm. Based on the estimated contour error obtained from the proposed algorithm, an integrated motion control scheme is employed to improve the machining accuracy for a contour following task. Experimental results indicate that both the proposed contour error estimation algorithm and the integrated motion control scheme exhibit satisfactory performances

CNC PCB Milling and Wood Engraving Machine incorporates the plan and usage of a CNC (Computer Numerical Control) machine to make PCB (Printed Circuit Board) and wood etching machine in a solitary set up. Creation of PCB in the present market is intensely costly when manufacture of testing circuits is considered. Along these lines, this CNC PCB Milling machine would be an effective instrument through which circuit can be scratched and penetrated at a reasonable cost. The creation of this CNC machine is to diminish the cost and multifaceted nature of machine. This venture manages the plan of programmed scaled down CNC machine for PCB drawing and processing. The proposed venture would be profoundly practical in contrast with that of customary manufacture hardware.

Computer Numerical Control (CNC) machining is a subtractive manufacturing technique that removes layers of material from a blank or workpiece to create a specific product. With increasing global competition, minimizing downtime during production is essential to maximize machine availability and productivity. This study investigates the application of machine learning models, specifically Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to forecast CNC machine downtime from multiple failure sources. The study uses data collected from 16 CNC machines at Company A in Malaysia over an extended period. The data contain key variables for each downtime event, such as machine ID, failure type, start date/time, end date/time, and downtime duration in minutes. Failure types are categorized into several groups, including mechanical, electrical, and tool malfunctions. After hyperparameter tuning, the XGBoost model outperformed the RF model, achieving a Mean Squared Error (MSE) of 0.4017, Root MSE (RMSE) of 0.634, and Mean Absolute Error (MAE) of 0.470 on the test set, while the RF model yielded higher errors, with an MSE of 1.2654, RMSE of 1.125, and MAE of 0.943. These results demonstrate the superiority of the XGBoost model over RF in predicting future CNC downtime, as indicated by its lower prediction errors. Future work should focus on refining the model with larger, more diverse datasets and exploring its integration into AI-based decision support systems to enhance machine availability and operational efficiency.

In recent years, the productivity of machine tools have been significantly improved by using computer based CAD/CAM systems for Computer Numerical Control(CNC) program generation. There are many CAM software in the market that provide tool path programming and can be applied to different types of machining. However, in most cases the efficiency of machining in CNC is still relied on the optimization of the tool path. Hence, this present paper presents a Genetic Algorithm Tool Path Optimization (GATPO) method using to obtain the shortest tool path for three axes CNC drilling machine. A user friendly Graphical User Interface (GUI) has been developed using MATLAB to input various parameters which adapts Travelling Salesman Problem(TSP) in determining the distance took during machining. Two kinds of crossover method have been applied, i.e., single-point crossover and two-point crossover to observe the effect of them. The simulation result ascertained that our proposed GA based tool path optimization is useful to find a lower distance of tool path generation for hole drilling process.

Five-axis machining has been widely used in aerospace, automobile and mold/die industries. It has many advantages compared to conventional three-axis machining, including higher metal removal rates, better surface finish, and more precise part surface in one setup. This research presents a new method for curve machining on five-axis Computer Numerical Control (CNC) machine tools. It uses the CNC interpolator approach, or called curve interpolator, which can produce accurate tool position as well as tool orientation. The interpolator calculates a new command in real time, the same time period needed for sampling the control-loop feedback devices. It performs trajectory planning and inverse kinematics transformation in each sampling period. To implement this curve interpolator, a 3-D parametric curve g-code must be defined for five-axis CNC machining. The comparisons for this real-time method and the conventional off-line method in terms of trajectory accuracy and feedrate variation are demonstrated in the end of this chapter.

The objective of all manufacturers is to produce high-quality products at reasonable prices. In high-volume mass production systems, optimizing the operation sequence (OS) not only reduces product cost but also increases production efficiency. An optimal OS can be achieved by trimming unnecessary segments of motion from the tool path (TP), which reduces production time. Currently, a computer numerical control (CNC) program can be generated for a given TP by the number of automatic programing packages (APPs) that are currently available. However, TP optimization is not included in most APPs. In this study, we propose genetic algorithms (GAs) to determine the optimal OS for the TP pass through a set of operations located asymmetrically in multiple levels and using one or more cutting tools. Then, the CNC program of the TP will be generated. The traveling salesman problem is introduced to define the OS problem. A GA based on the TSP formulation can determine the OS that achieves the shortest TP. The incorporation of a GA and TSP can be integrated into the APP for TP optimization before creating CNC programs.

This paper presents a manufacturing model of a flat-end mill using a five-axis computer numerical control (CNC) grinding machine. The profile of the helical groove can be precisely calculated using a given wheel profile and the relative movements between the workpiece and the grinding wheel. The results of the calculation of the numerical control (NC) data for all the grinding processes of the end mill are fully presented in the paper. The NC data for the machining end mill were generated from the developed program by using the given design parameters of tool geometry, wheel geometry, wheel setting, and machine setting. The effects of the design and manufacturing of the end mill were analyzed based on the computer simulation and experiment results. This study provides a practical and efficient model for the manufacturing of an end mill using a CNC grinding machine with simple programming techniques. The prediction of the end mill geometry for the designing of the proper configuration of the end mill before manufacturing and generating the NC code for machining in a CNC machine is necessary to save time and to reduce the manufacturing cost.