Computer Numerical Control Machine Tools Research Articles

A Novel Method for the Measurement of Geometric Errors in the Linear Motion of CNC Machine Tools

In order to improve the accuracy of the linear motion of computer numerical control (CNC) machine tools, a novel method based on a new type of 1-D (1-dimensional) artifact is proposed to measure the geometric errors. Based on the properties of the displacement measurement of a revolutionary paraboloid and the angle measurement of plane mirrors, the 1-D artifact can be applied to identify position errors and angle errors. Meanwhile, the concrete 6 degrees-of-freedom error identification method is described in this paper in sufficient detail. Through measuring the 1-D artifact horizontally and vertically using the machine tool, the geometric errors can be obtained by calculating the deviation between the characteristic parameter of the 1-D artifact measured by the machine tool and that measured by a more precise method, for example, laser interferometry. Experiments were carried out on a coordinate measuring machine, and the validity and accuracy of the method were discussed by comparing the result with the identification error measured by a laser interferometer.

Trajectory smoothing method using reinforcement learning for computer numerical control machine tools

Abstract Tool-path codes output by computer-aided manufacturing software for high-speed machining are composed of discontinuous G01 line segments. The discontinuity of these tool movements causes computer numerical control (CNC) inefficiency. To achieve high-speed continuous motion, corner smoothing algorithms based on pre-planning methods are widely used. However, it is difficult to optimize smoothing trajectories in real-time systems. To obtain smooth trajectories efficiently, this paper proposes a neural network-based direct trajectory smoothing method. An intelligent neural network agent outputs servo commands directly based on the current tool path and running state in every cycle. To achieve direct control, motion feature and reward models were built, and reinforcement learning was used to train the neural network parameters without additional experimental data. The proposed method provides higher cutting efficiency than the local and global smoothing algorithms. Given its simple structure and low computational demands, it can easily be applied to real-time CNC systems.

A predictive maintenance cost model for CNC SMEs in the era of industry 4.0

Within the subject area of maintenance and maintenance management, authors identified a deficiency in studies focussing on the expected value from adopting predictive maintenance (PdM) techniques for machine tools (MTs). Authors identified no studies focussing on presenting a PdM value analysis or cost model specifically for small-medium enterprises (SMEs) operating computer numerically controlled (CNC) MTs. This paper’s novelty is addressing SMEs’ minimal representation in literature by explanatorily collecting data from SMEs within the focal area via surveys, modelling and analysing datasets, then proposes a cost-effective PdM system architecture for SME CNC machine shops that predicts cost savings ranging from £22,804 to £48,585 over a range of 1–50 CNC MTs maintained on a distributed numerically controlled (DNC) network. It affirms PdM’s tangible value creation for SME CNC machine shops with predicted positive impacts on their MT cost and performance drivers. These exploratory research findings corroborate SMEs pooling together to optimise their CNC MT maintenance cost through the recommended system architecture. Finally, it introduces opportunities for further PdM research taking into account SMEs’ perspective. The paper’s industrial application is confirmed from the surveyed SMEs that demonstrated their current utility of PdM; then anonymous positive feedback on the online dashboard, shared with participant companies, confirmed the research results supported SMEs in considering exploring the path to adapting PdM. It is anticipated that beneficiaries of this research will be maintenance managers, business executives and researchers who seek to understand the expected financial and performance impact of adopting PdM for a MT’s overall life cycle costs.

Key point selection in large-scale FBG temperature sensors for thermal error modeling of heavy-duty CNC machine tools

Thermal error is one of the main factors that influence the machining accuracy of computer numerical control (CNC) machine tools. It is usually reduced by thermal error compensation. Temperature field monitoring and key temperature measurement point (TMP) selection are the bases of thermal error modeling and compensation for CNC machine tools. Compared with small- and medium-sized CNC machine tools, heavy-duty CNC machine tools require the use of more temperature sensors to measure their temperature comprehensively because of their larger size and more complex heat sources. However, the presence of many TMPs counteracts the movement of CNC machine tools due to sensor cables, and too many temperature variables may adversely influence thermal error modeling. Novel temperature sensors based on fiber Bragg grating (FBG) are developed in this study. A total of 128 FBG temperature sensors that are connected in series through a thin optical fiber are mounted on a heavy-duty CNC machine tool to monitor its temperature field. Key TMPs are selected using these large-scale FBG temperature sensors by using the density-based spatial clustering of applications with noise algorithm to reduce the calculation workload and avoid problems in the coupling of TMPs for thermal error modeling. Back propagation neural network thermal error prediction models are established to verify the performance of the proposed TMP selection method. Results show that the number of TMPs is reduced from 128 to 5, and the developed model demonstrates good prediction effects and strong robustness under different working conditions of the heavy-duty CNC machine tool.

A modal parameter identification method of machine tools based on particle swarm optimization

The dynamic characteristics of the computerized numerical control (CNC) machine tool directly affect its machining quality, and it is necessary to carry out the working mode analysis of the CNC machine tool. The traditional manual analysis and identification process is complicated and inefficient. In the industrial environment of big data, how to use the working modal analysis method to quickly and accurately obtain the dynamic characteristic parameters of the machine tool processing from these data has become the research difficulty at the current stage. This paper proposes an optimized particle swarm optimization algorithm to solve this problem. Based on the working modal analysis theory, the semi-self-power spectrum of the output signal can replace the frequency response function for modal parameter identification. The optimized semi-self-power spectrum signal is used as the objective function of the algorithm, and the ability of the algorithm to preprocess the data is optimized, so that the improved algorithm can automatically analyze the structural mode of the machine tool during processing. Comparing the experimental results, it is found that the natural frequency identification error of the cantilever beam is less than 1%, and the natural frequency identification error of the CNC milling machine is not more than 7%. The results show that the particle swarm optimization algorithm based on modal analysis theory can be applied to the automatic analysis of modal parameters under machine tool operating conditions, and it is efficient and accurate.

Modified IMC Technique for Nonlinear Uncertain Milling CNC Machine Tool System

The objective of this paper is to model a nonlinear computer numerical control machine tool (CNCMT) system for milling process. A modified internal model control (M-IMC) technique is proposed for position control of machine table. The study of milling process is mainly focused on the cutting mechanics and dynamics. The performance of CNCMT system is degraded due to the presence of disturbance like cutting force and parametric uncertainty. Considering the limitation of conventional internal model control (C-IMC) technique that is the poor performance under constant disturbance, an M-IMC technique is designed for the rejection of disturbance and position control of uncertain nonlinear CNCMT system. The performances of nonlinear uncertain CNCMT system using M-IMC technique is also compared with C-IMC and Ziegler–Nichols-tuned proportional–integral–derivative control techniques. The proposed M-IMC technique is designed to attain the better disturbance rejection capability, i.e., regulatory control with less control effort for the regulation of desired position of a machine table.

Kinematic modeling and machining precision analysis of multi-axis CNC machine tools based on screw theory

This paper presents the solution to evaluate and predict machining precision of a multi-axis computer numerical control (CNC) machine tool. The “function-motion-action” method is used to describe the motion relationships of a multi-axis CNC machine tool, and the multi-body system structure model of the machining system is further constructed. By analyzing the motion process of the machine tool based on the meta-action layer and considering the motion transfer relations in meta-action chains, the kinematic models of each motion shaft are established in twist exponential form. According to the structural and kinematic relationships of these motion shafts, the kinematic and kinematic error models of the machine tool can be easily obtained. Then, the distance error formula is used to deduce the general model of machining allowance error. A simulation test and practical experiment are performed on a type of CNC controlled 5-axis machining center. The experiment results show that the proposed method is universal, and it can greatly simplify the kinematic analysis of multi-axis CNC machine tools.

A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid

The micro-displacement measurement system with 2D/3D has become increasingly important in the field of scientific research and technology application. In order to explore the application of an optical surface in micro-displacement measurement, a novel and simple 2D micro-displacement measurement method based on the elliptical paraboloid was designed and subjected to experiment. The measurement system takes advantage of the elliptical paraboloid instead of a plane mirror in the optical structure of an autocollimator which has been ameliorated to adapt to curved surface measurement. Through the displacement of the light spot on the CCD (Charge Coupled Device) detector, the displacement of the target could be measured with a linear correlation coefficient of 0.9999. The accuracy of the system is about ± 0.3 μm in a wide range in two dimensions. The results were in good agreement with the theoretical analysis and indicated the potential applicability of the proposed system in the detection of geometric errors of CNC (Computerized Numerical Control) machine tools.

Sustainability Evaluation of Process Planning for Single CNC Machine Tool under the Consideration of Energy-Efficient Control Strategies Using Random Forests

As an important part of industrialized society, manufacturing consumes a large amount of raw materials and energy, which motivates decision-makers to tackle this problem in different manners. Process planning is an important optimization method to realize the object, and energy consumption, carbon emission, or sustainability evaluation is the basis for the optimization stage. Although the evaluation research has drawn a great deal of attention, most of it neglects the influence of state control of machine tools on the energy consumption of machining processes. To address the above issue, a sustainability evaluation method of process planning for single computer numerical control (CNC) machine tool considering energy-efficient control strategies has been developed. First, four energy-efficient control strategies of CNC machine tools are constructed to reduce their energy consumption. Second, a bi-level energy-efficient decision-making mechanism using random forests is established to select appropriate control strategies for different occasions. Then, three indicators are adopted to evaluate the sustainability of process planning under the consideration of energy-efficient control strategies, i.e., energy consumption, relative delay time, and machining costs. Finally, a pedestal part machined by a 3-axis vertical milling machine tool is used to verify the proposed methods. The results show that the reduction in energy consumption considering energy-efficient control strategies reaches 25%.

Measurement of error in computer numerical control machines and optimization using teaching–learning-based optimization algorithm

The accuracy of computer numerical control machine tools can be improved by identifying error sources affecting the overall position error and orientation errors. Because of their inevitable nature, the position errors cannot be entirely eliminated from the machinery, but they can be identified, measured and compensated during the manufacturing process of the components by developing and using a mathematical model. In this present work, different mathematical models have been developed for the errors measured by laser interferometer at different nominal positions of X, Y and Z axes both in forward and reverse direction movement as per VDI 3441 Germany standard. Using Akaike information criterion, the best model is selected for each axis and later the best model’s coefficients have been optimized by considering both minimizing sum square errors and maximizing R2 values using teaching–learning-based optimization algorithm. Technique for Order Preference by Similarity to the Ideal Solution method has been adopted to convert the dual objectives into a single objective. An improvement of 1%–71% in R2 values was reported to prove the effectiveness of the proposed optimization algorithm, Teaching–Learning-Based Optimization algorithm, with the same sum square error values.

Time domain load extrapolation method for CNC machine tools based on GRA-POT model

The load extrapolation is to obtain load data that may occur during the whole life cycle of product based on the limited measured load data, especially extreme load. Aiming at the two problems of traditional time domain extrapolation: without considering the interval time between extreme adjacent values and the high sensitivity of the peak over threshold (POT) model to the extreme threshold, a time domain load extrapolation method for computerized numerical control (CNC) machine tools based on Gray relational analysis-peak over threshold model (GRA-POT) is proposed. Firstly, for the first problem, an improved Markov chain Monte Carlo (MCMC) time domain load reconstruction method is proposed and analysis of reconstructed time domain load; secondly, according to the mean excess function (MEF) method, the extreme threshold range of POT model is determined and divided into multiple sets. The generalized Pareto distribution (GPD) fitting is performed for multiple sets of candidate load extreme samples. Finally, the Gray relational degrees (GRD) between each group of GPD and the median rank distribution are calculated by the Gray relational analysis (GRA), and the extreme threshold corresponding to the maximum GRD is selected as the optimal threshold of the POT model. The dynamic cutting force signal of CNC machine tool under constant speed cutting condition was reconstructed and extrapolated using the proposed method, and the dynamic cutting force spectrum of CNC machine tool was compiled. Case analysis results show that the POT extrapolation model with high fitting precision can be obtained using the proposed method. Furthermore, the precision of load spectrum of CNC machine tool is improved.

Investigation of tool wear in single point incremental sheet forming

This article presents a proposal for a new method to evaluate tool wear incremental sheet forming process. Incremental sheet forming is an innovative forming process with a high interest in fields of the industry due to its low preparation cost and high flexibility, allowing the production of small batches at a reduced cost. Among the various types of incremental sheet forming processes, the single point incremental sheet forming is the most cost-effective, and unfortunately, the single point incremental sheet forming process has high dimensional errors. In order to understand the process and its dimensional errors better, this article shows the study of tool wear and the quality of surface finish with the generated data can correlate with the tool life. The study is carried out by means of a sequence of experimental tests of galvanized steel sheet conformation by altering the stamping parameters (vertical step in, feed and rotation) and capturing the values of the surface roughness of the parts, the forming tool wear and processing time. After the completion of the tests, the classical formulation of the Taylor equation was utilized to obtain a mathematical model capable of estimating the lifetime of the single point incremental sheet forming tool associated with a tool wear value and the desired dimensional accuracy in relation to the processing parameters for the part or tool pair analyzed in a computer numerical control machine tool. The results of the study present an original model of prediction of tool wear in relation to the input parameters for the single point incremental sheet forming process; the overall error rate is 33.44% for the wear model of prediction and 35.94% for the lifetime model of prediction.

The selection of key temperature measurement points for thermal error modeling of heavy-duty computer numerical control machine tools with density peaks clustering

Having great impacts on machining precision, thermal error is one of the main error sources for heavy-duty computer numerical control machine tools. Thermal error compensation using prediction mode...

Track-based analysis for profile generation on globoidal cam in automatic tool changer of CNC machining center

PurposeThis paper aims to propose an elementary approach towards the measurement of a globoidal cam profile used in an automatic tool changer (ATC) of computer numerical control (CNC) machines.Design/methodology/approachA simple and unique online method has been designed for the profile metrology of the cam. This simple methodology will replace the traditional methodology of profile metrology of cam by coordinate measuring machine (CMM). A globoidal cam with an indexable turret and roller follower (rotating in an enclosed track) has been evaluated in our analysis. This analysis plays a significant role in the performance determination of the cam as well as the ATC of CNC machines.FindingsA novel model has been designed and implemented to investigate the profile of a globoidal cam. The proposed methodology becomes an enhancement over the old methodology, i.e. measurement through CMM. Theoretical analysis and practical implementation prove the significance of the method.Originality/valueAn enhanced methodology to effectively measure the globoidal cam profile has been proposed. The practical implication of the proposed methodology remains for the CNC machine tool and ATC manufacturers. Finally, analytical explorations have been carried out to prove the validity of the proposal.

Evaluating Safety Systems for Machine Tools with Computer Numerical Control using Analytic Hierarchy Process

Computer numerical control (CNC) machine tools are complex production systems with fully automatic machine parts. Nowadays, high feed rates and machining speeds are used during the machining process. Human operators are still needed to set-up the machine, load/unload workpieces and parts, load the machining code, and supervise the machining process. The operators work in an environment where automated high-speed motions occur, and consequently, CNC machine tools have to be equipped with safety systems. The approach presented in this paper was to evaluate the main safety systems of CNC machine tools based upon the analytic hierarchy process (AHP). The analyzed systems were divided into six main categories and compared pairwise using five criteria proposed by the authors. The approach and the obtained results significantly relied upon the situation found at the industrial company used as a benchmark for the research. The analysis reveals that, among considered safety devices, manually operated controls are the most efficient ones. Finally, a sensitivity analysis was conducted to test the stability of the AHP solution.

Machining Based Geometric Error Estimation Method for 3-Axis CNC Machine

Computer numerical control (CNC) machine tool plays an extremely significant role in any manufacturing industry due to its automation and high accuracy. Keeping the CNC machine tool at its highest performance to meet the demand of high accuracy machining is always significant. To maintain the accuracy of a machine tool over the time, it is important to measure and compensate the geometric error, one of the main error source of machine tool, especially when the machine get old. There are totally 21 geometrical errors in a 3-axis machine tool including three translational errors and three rotational errors for each axis and three perpendicular error (Squareness) within three axes of the machine. This paper presents an economical and simple method for measuring the geometric error of a 3-axis CNC machine tool based on the machining of actual samples. Three samples for each axis will be machined following a design cutting path. The samples will then be measured using a coordinate measuring machine (CMM). The collect data will be used for estimating the geometric errors. The volumetric errors will be then computed and verified through machining of 3D geometries.

A Real-Time C3 Continuous Local Corner Smoothing and Interpolation Algorithm for CNC Machine Tools

Linear tool path segments of computer numerical control (CNC) machine tools need to be smoothed and interpolated in order to guarantee continuous and steady machining. However, because of the highly nonlinear relation between arc lengths and spline parameters, it is difficult to develop algorithms to simultaneously achieve real-time corner smoothing and interpolation with high-order continuity, although it is important to guarantee both high calculation efficiency and good dynamic performance of high-speed CNC machining. This paper develops a computationally efficient real-time corner smoothing and interpolation algorithm with C3 continuous feature. The corners at the junction of linear segments are smoothed by inserting Pythagorean-hodograph (PH) splines under the constraints of user-defined tolerance limits. Analytical solutions of the arc length and curvature of the smoothed tool path are obtained by evaluating a polynomial function of the spline parameter. The smoothed tool path is interpolated in real time with continuous and peak-constrained jerk. Simulations and experimental results show that the proposed tool path smoothing and interpolation algorithm can be executed in real time with 0.5 ms control period. Acceleration and jerk continuity of each axis are achieved along the tool path. Comparisons with existing corner smoothing algorithms show that the proposed method has lower jerk than existing C2 algorithms and the real-time interpolation algorithms based on the Taylor series expansion.

A knowledge based machine tool maintenance planning system using case-based reasoning techniques

In advanced manufacturing systems, Computer Numerical Control (CNC) machine tools are important equipment to manufacture product components of high precision, whilst from equipment maintenance point of view, they are regarded as the ‘products’ provided by machine tool manufacturers. Therefore, the reliability of CNC machine tools affects not only the quality of the components they manufacture, but also the reputation and profits of equipment suppliers. This paper presents a novel knowledge-based maintenance planning system to facilitate information and knowledge sharing between all stakeholders including machine tool manufacturers, users (manufacturing systems), maintenance service providers and part suppliers (for machine tools), in the emerging ‘Product-Service’ business model. Case Based Reasoning principles have been implemented to improve the efficiency of maintenance planning. Ontologies were adopted to represent field knowledge using adaptation guided retrievals based on semantic similarity and correlation. The adaption algorithm has been developed based on the Casual Theory and the dependence relationship to generate the solution for required maintenance problems. The proposed system was implemented using Content Management technologies, which proved to have advantages over traditional database systems in managing engineering knowledge, and has been verified using an example CNC machine tool. The results were commented by industrial collaborators as very promising and further exploitation in industry was recommended.

Reliability and Modal Analysis of Key Meta-Action Unit for CNC Machine Tool

Dynamic characteristics have an important impact on the reliability of computer numerical control (CNC) machine tool. As the basis of dynamic characteristics analysis, the modal analysis should be conducted. However, the current modal analysis methods have some shortcomings. For example, the selected research object cannot reflect the dynamic characteristics of the CNC machine tool, the selection of research object lacks a scientific basis, and the influence of coupling factors is also often ignored. To address these problems, the concepts of meta-action failure mode and the latest research results about meta-action were given herein. Meta-action unit is the smallest structural unit to ensure the normal operation of the machine tool, and it can fully reflect the dynamic characteristics of a machine tool, so it is taken as the research object. In order to reduce the blindness of selecting the analytic object when implementing the reliability improvement measures, we put forward the concept of a key meta-action unit and gave the corresponding extraction method. Meanwhile, electromechanical coupling was considered when built the dynamic model of a key meta-action unit. A CNC machine tool made in China was taken as an example, and a key meta-action unit was extracted and analyzed, then the corresponding reliability improvement measures were also given. The results verify the applicability and effectiveness of this method. The proposed method can solve the shortcomings of the current methods, which lays a foundation for further research of mechanical properties and reliability based on machine tool meta-action unit.

Cascading Fault Analysis and Control Strategy for Computer Numerical Control Machine Tools Based on Meta Action

Cascading faults of computer numerical control machine tools will lead to frequent machine faults, but an effective fault analysis and control strategy can significantly reduce the frequency of such cascading faults. The numerous studies have focused on faulty parts using established fault diagnosis models centered on faulty parts and developed measures for controlling machine tool reliability from the parts' perspective. However, the internal parts of machine tools are strongly coupled, and when one part or assembly begins operating abnormally, it can easily initiate the abnormal operation of other parts or the entire assembly, which can lead to failures. Thus, a fault diagnosis method that considers only individual faulty parts ignores the problem of fault propagation between parts, which may lead to failure to locate the root cause. To address this problem, this paper proposes a cascading fault analysis and control strategy based on meta action. First, the functional components are decomposed into meta actions using the “function-motion-action” principle. Second, the directed edges and hierarchical digraph of meta action fault propagation are obtained. Third, the PageRank algorithm is used to calculate the fault propagation impact degree of each directed edge, and the key paths of meta action fault propagation are extracted based on the hierarchical digraph and the impact degree. Finally, the fault tree analysis method is applied to analyze the meta action on key paths, and feasible machine control measures are developed. The feasibility and effectiveness of the method are demonstrated using an example machining center.