5-axis Machine Tool Research Articles

Research of design an educational modular 5 axis milling machine

Industrial CNC machine tools are supplied as closed architecture systems. Their structure, kinematics and CNC controllers are designed to be used exclusively for their basic purpose, without any possibility of being changed. Reconfiguration is seen as the ability to add or remove modules from the machine, to be equipped with additional degrees of freedom (axes). For the concept to become a reality, it is necessary that the machine tool can be easily assembled and disassembled by an end-user and can adapt to changing projects or machining requirements. The approached of this research was to develop a 5-axis modular machine tool, with technological capabilities similar to industrial ones, at an affordable level of development costs and suitable to be used for teaching CAM techniques for future engineers.

Method for Data-Driven NC-Code Optimization based on Dexel Material Removal Simulation and Tool Holder Vibration Measurements

This paper presents a novel approach for data-driven NC-Code optimization, based on the integration of dexel-based material removal simulation and an instrumented tool holder, capable to measure vibrations during milling close to the cutting zone. Considering measured cutting vibrations, machine tool axis and NC-line data, a model has been developed optimizing cutting parameters to generate a NC-Code right after a first machining trial with mitigated vibration effects. Different modules for human-assisting vibration visualization and automated optimization of cutting parameters are presented, using milling use-cases implemented on a CNC machining center.

Paralleling Collision Detection on Five-Axis Machining

With the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), five-axis machining has played an important role nowadays. Due to the expensive cost of five-axis machining, how to solve the collision detection for five-axis machining in real-time is very critical. In this paper, we present a parallel method to detect collision for five-axis machining. Moreover, we apply the bounding volume hierarchy technique with two-level bounding volume represent the surface or solid of the object to reduce triangle meshes inside each axis of the five-axis machine tool, and then matching the operating range limit of the five-axis machine tool itself, delete the no colliding triangle mesh. Additionally, we also propose some optimization with loop unrolling and prefetching techniques to improve performance of collision detection. Our approach can reduce the execution time significantly by computing six separating axes in plan and eleven separating axis in non-plan between two triangle meshes based on the characteristic of GPUs (Graphics Processing Units) for program acceleration. Our proposed work consists of kinematic analysis and interpolation for axes to save the numerous collision detection for five-axis machining computations. In this experiment, the result shows that using the proposed approach above can achieve approximately 37.1 times speedup than that of CPU.

Review on Thermal Error Compensation for Feed Axes of CNC Machine Tools

Review on Thermal Error Compensation for Feed Axes of CNC Machine Tools

Selection of a workpiece clamping system for computer-aided subtractive manufacturing of geometrically complex medical models

Abstract Physical models of anatomical structures can be made using Additive Manufacturing (AM) or Subtractive Manufacturing (SM). The advantage of subtractive techniques over additive ones is the possibility of maintaining the homogeneity and consistency of the processed material, which is extremely important in the case of medical devices. Currently, a geometrically complex medical model can be made even on a simple, 3-axis CNC machine tool. However, often the semi-finished product must be machined in at least two clamping configurations. The aim of the work is to present the method of fixing a workpiece in the process of subtractive production of geometrically complex medical objects on the example of skull bone prostheses. The paper discusses the use of two clamping systems for machining such models. It presents the process of subtractive production of bone prostheses models fitted to the defect of the skull bone with the use of the proposed methods of fixing the workpiece. The result of the work are two models of the skull bone prosthesis. A more complex model was analysed in terms of the accuracy of geometry reproduction. The research confirmed the usefulness of the proposed clamping systems for the preparation of medical models of geometrically complex anatomical structures.

A general identification method for position-dependent geometric errors of rotary axis with single-axis driven

Position-dependent geometric errors (PDGEs) of rotary axis affect the accuracy of the multi-axis machine tool. However, many PDGE identification methods were not general enough and not applicable when the structure of the machine tool was limited or changed. In this paper, a general PDGE identification method with single-axis driven was proposed. Firstly, the comprehensive length change model of the double-ball bar (DBB) was established. The simplification was conducted through the constraint condition to obtain the identification matrix. Then, the effect of the installation errors of the DBB was analyzed and eliminated. Simulations were carried out to validate the correctness of the proposed method when considering the installation errors. Next 10 measurement patterns were determined according to the structure limitation of the small-size 5-axis machine tool. Totally 364 combinations with full rank identification matrix are available. Finally, the prediction experiments and analysis for standard deviation were conducted to further validate the effectiveness of the proposed method. It turned out that the small condition number of the identification matrix can more likely achieve high accuracy. And an improved combination using 5 patterns was proposed with the same accuracy achieved compared to that using a total of 10 patterns.

Autonomously triggered model updates for self-learning thermal error compensation

The presented method significantly increases the self-optimization ability of thermal error compensation models by triggering on-machine measurements when unknown thermal conditions occur. These conditions, which are not represented by the training data of the compensation models, are identified by a novelty detection approach based on one-class support vector machines. The results show that the autonomously triggered on-machine measurements applied to a 5-axis machine tool overcome the trade-off between precision and productivity for thermal error compensation. The non-productive time to detect an exceedance of the predefined tolerances is reduced by 78% without significantly reducing the precision of the thermal error compensation.

Tool-path control for singularity avoidance of 5-axis machine tool by combining circular arc and cubic spline

Tool-path control for singularity avoidance of 5-axis machine tool by combining circular arc and cubic spline

Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach

During the batch assembly analysis of linear axis of machine tool, assembly quality evaluation is crucial to reduce assembly quality fluctuations and improve efficiency. This study presented a data-driven modeling approach for evaluating assembly quality of linear axis based on normalized mutual information and random sampling with replacement (NMI-RSWR) variable selection method, synthetic minority over-sampling technique (SMOTE), and genetic algorithm (GA)-optimized multi-class support vector machine (SVM). First, a variable selection method named NMI-RSWR was proposed to select key assembly parameters which affected assembly quality of linear axis. Then, a hybrid method based on SMOTE and GA-optimized multi-class SVM was presented to construct assembly quality evaluation model. In this method, Class imbalance problem was solved by using SMOTE, and parameters optimization problem was solved by using GA. Finally, the assembly-related data from the batch assembly of x-axis of a three-axis vertical machining center were collected to validate the proposed method. The results indicate that the proposed NMI-RSWR approach has capacity for selecting the highly related assembly parameters with assembly quality of linear axis, and the proposed data-driven modeling approach is effective for assembly quality evaluation of linear axis.

Modeling the thermo-mechanical deformations of machine tool structures in CFRP material adopting data-driven prediction schemes

The thermo-mechanical effects in machine tools (MTs) are represented by complex models since they may produce non-linear distortions overtime, impacting significantly on the machining accuracy. This paper aims to model the deformation of CFRP (Carbon-Fiber-Reinforced-Polymers) structures using data-driven schemes to predict and compensate the structural thermo-mechanical behavior. A novel study is presented to investigate the thermally-induced distortions of CFPR structural materials, selecting and positioning sensors, simulating and validating models to compensate the error in real-time. Anisotropic materials are becoming an effective solution to reduce structure mass and increase damping of a MT, nevertheless their physical complexity and the different thermal-coefficients at the interface with conventional materials may generate undesired effects, limiting the obtained advantages. The proposed strategy is based on the evaluation of a set of data-driven models simultaneously, identifying the most suitable solution and comparing finite element simulations with machine learning approach. The study is developed on a vertical axis frame made of CFRP material. The experimental validation is executed on a commercial 5-axis machine tool by varying the temperature conditions and evaluating the structural thermo-mechanical deformation effect on the Tool-Tip-Point (TTP) displacement. The thermo-mechanical behavior is measured by fiber Bragg grating (FBG) sensing technology embedded in the CFRP structure. Data-driven lab tests are evaluated in operational conditions during 36 h, considering: i) training-deployment periods (875 min interval), ii) typical machining stresses and iii) environmental perturbations. The final selected data-driven model is able to reduce the detected error lower than 10 μm range. In particular, the achieved results indicate a congruence between the TTP displacement measured and predicted with a residual error lower than 7.0 μm (Y-direction) using the ANN- multilayer perceptron algorithm.

Research on Rapid Imitation of Human Tibia and Five-axis CNC Machining Based on Computer-aided

The rapid imitation of the human humerus is the computer application of data acquisition, data processing and reconstruction of the mandibular model in reverse engineering. Based on the rapid imitation of the tibia of the NURBS patch in the Geomagic software computer application, the efficiency and quality of the mandibular surface reconstruction is effectively improved. Based on the five-axis NC programming module in the UG computer-aided software application, the five axes required for the mandible humerus processing are compiled. The CNC program uses Vericut computer software to build a virtual machine tool, simulates the actual machining process, detects machine collision and interference, and provides safety for the production of 5-axis machine tools.

Modeling and Simulation of K2x8 Five Axis Machine Tool Based on VERICUT 8.0

Taking the VERICUT 8.0 software as the platform and k2x8 five axis machine as an example, this paper expounds the method and steps of constructing non orthogonal five axis machine simulation machine. The key technologies such as the modeling of machine tool, the construction of machine tool kinematic chain, collision detection and stroke setting, the configuration of control system and simulation are introduced in detail. And the impeller is used as a sample for numerical control simulation processing, which verifies the correctness and effectiveness of the simulation platform, and provides a useful reference for other five axis machine simulation platform configuration.

Rapid calibration method for measuring linear axis optical paths of computer numerical control machine tools with a laser interferometer

The optical path calibration process using a laser interferometer is time-consuming and tedious, requiring repeated adjustment and experienced operators, leading to a significant reduction in measuring efficiency. A novel optical path calibration process of the linear axis of a three-axis machine tool using a Renishaw XL-80 laser interferometer is proposed in this paper. The homogeneous coordinate transformation error model of the measured axis is established. Using the established error model, only the initial distance from the laser transmitter to the beam splitter and the initial distance from the beam splitter to the linear mirror are required to accurately calibrate the laser beam. The relationship between the fixed distance from the center of the light target to the quarter point on the edge of the light target and the movement distance of the measured axis are obtained. Eight deviations of the optical path affected by the laser transmitter, the beam splitter and the linear mirror are solved. Calibrations based on the deviations can be achieved by adjusting the positions and orientations of the laser interferometer components, leading to a rapid and accurate calibration method for measuring the optical paths.

Highly efficient and accurate calibration method for the position-dependent geometric errors of the rotary axes of a five-axis machine tool

This paper proposes a calibration method for continuous measurements with a double ball bar (DBB) used to identify the position-dependent geometric errors (PDGEs) of the rotary axes of five-axis machine tools. The different DBB installation modes for the rotary axes of the spindle and workbench are established, and the same initial DBB installation position is used for multiple tests. This approach minimizes the number of required DBB installations, which increases the measurement efficiency of the PDGEs of the rotary axes and reduces installation errors. PDGEs identification based on the adaptive least absolute shrinkage and selection operator (LASSO) method is proposed. By assigning coefficients to the PDGEs polynomial, the ill-conditioned problem of the identification process can be effectively avoided, thereby improving the identification accuracy. The measurement and identification methods proposed in this paper are verified by experiments on a five-axis machine tool. After compensation, the PDGEs are obviously reduced and the accuracy indexes of the circular trajectory tests performed under multiaxis synchronous control are obviously improved.

Geometric errors identification considering rigid-body motion constraint for rotary axis of multi-axis machine tool using a tracking interferometer

The measurement and identification of the four position-independent geometric errors (PIGEs) and six position-dependent geometric errors (PDGEs) in the rotary axis are necessary to reduce their contributions to the overall machining errors of a multi-axis machine tool. In this paper, a new geometric error identification method using a tracking interferometer is presented by considering the rigid-body motion constraint in multilateration. The rigid-body motion constraint is introduced to establish a new coordinate calculation model for the measurement points, and an identification process is presented to separately identify the PIGEs and PDGEs by deriving identification models based on established geometric error models. The main novelty of the proposed method lies in the consideration of the rigid-body motion constraint, which makes the identification more robust against random factors. Monte Carlo simulations and verifying experiments were performed for validation. The results show that the PIGEs and PDGEs in the rotary axis can be successfully separated and identified by the new method. Improved identification accuracy compared with the traditional method is achieved. The maximum angular positioning error was reduced by 84% after compensation. A lower uncertainty in the identified errors compared with those obtained by the traditional method and instrument software is achieved. The proposed method is validated by comparing the identification results with those obtained by the instrument software and laser interferometers.

Novel tool offset fly cutting straight-groove-type micro structure arrays

Optical micro structure arrays, with regular geometrical morphology such as micro groove array, pyramid array, triangular pyramid array, etc., are widely used in optical components due to their unique optical properties. The machining of these micro structure arrays needs the cooperation of the multi linear axes of precision machine tools, while the lack of certain linear axis (e.g. Y-axis) limits the machining of such micro structure arrays. In this paper, a novel offset-fly-cutting-servo (OFCS) machining system was developed, which can fabricate the straight-groove-type micro structure arrays under the general configuration (X-, Z-, C-axis) of commercial ultra-precision machine tools. In this paper, a mathematical model for tool arc trajectory linearization compensation was established. Then the machining scheme was designed and cutting experiments of micro groove array, pyramid array and triangular pyramid array were performed. Finally, the machined and designed morphology of the micro structure arrays were compared and analyzed. Theoretical and experimental results show that the machined and designed morphology of the micro structure arrays match well, the root mean square (RMS) value of the form error of micro groove array, pyramid array and triangular pyramid array are below 1 μm. It confirmed that the proposed OFCS is an effective machining method for the straight-groove-type micro structure arrays. It provides a technical solution for machining straight-groove-type micro structure array in the absence of linear axis of ultra-precision machine tools, also provides theoretical reference for other machining of complex micro structure arrays.

Analysis and improvement of the positioning accuracy of rotary axes of compound machine tools based on pitch error compensation

A method for pitch error compensation analysis and parameter identification for a rotary axis with special structures is proposed to improve the positioning accuracy of the rotary axes of compound machine tools. The characteristics of the planar linkage mechanism of a rotary axis are analyzed. Then, the error compensation trend is obtained according to the corresponding relationship between the angle and the displacement. An angular positioning error piecewise function is used to represent the ideal effect and actual performance of the pitch error compensation of the rotary axis. The rotation positioning accuracy test of the rotary axis is completed with a laser interferometer and rotary calibration device. By the measured angular positioning error of the non-compensated points, the Levenberg–Marquardt algorithm is used to identify the parameter of the piecewise function, and the optimized linkage parameters of the rotary axis are obtained. By the measured angular positioning error of the integer points of the rotary axis, the pitch error compensation value is modified. The distribution of the calculated and measured angular positioning error of the rotary axis under the pitch error compensation is consistent. The compensation results show that the positioning accuracy of the rotary axis can be greatly improved and that it meets the work requirement of the compound machine. The piecewise function of pitch error compensation, linkage parameter identification and improved method of pitch error compensation proposed in this article offer certain reference and application value. The results can improve the rotation positioning accuracy of the rotary axes of machine tools with planar linkages.

Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece

Achieving high workpiece accuracy is the long-term goal of machine tool designers. There are many causes for workpiece inaccuracy, with thermal errors being the most common. Indirect compensation (using prediction models for thermal errors) is a promising strategy to reduce thermal errors without increasing machine tool costs. The modelling approach uses transfer functions to deal with this issue; it is an established dynamic method with a physical basis, and its modelling and calculation speed are suitable for real-time applications. This research presents compensation for the main internal and external heat sources affecting the 5-axis machine tool structure including spindle rotation, three linear axes movements, rotary C axis and time-varying environmental temperature influence, save for the cutting process. A mathematical model using transfer functions is implemented directly into the control system of a milling centre to compensate for thermal errors in real time using Python programming language. The inputs of the compensation algorithm are indigenous temperature sensors used primarily for diagnostic purposes in the machine. Therefore, no additional temperature sensors are necessary. This achieved a significant reduction in thermal errors in three machine directions X, Y and Z during verification testing lasting over 60 h. Moreover, a thermal test piece was machined to verify the industrial applicability of the introduced approach. The results of the transfer function model compared with the machine tool's multiple linear regression compensation model are discussed.

A high-precision laser method for directly and quickly measuring 21 geometric motion errors of three linear axes of computer numerical control machine tools

Error compensation has become an important means of improving the manufacturing and processing accuracy of computer numerical control (CNC) machine tools. Quick and precise measurement of the various geometric motion errors (GMEs) of CNC machine tools is crucial. We propose a novel laser method for the efficient and direct high-precision measurement of the 21 GMEs of a three-axis CNC machine tool, or three linear axes of a five-axis tool. A corresponding system was developed, comprising a fiber-coupled laser unit, sensor head, target unit, and beam-steering unit. The beam-steering unit was designed to perform high-accuracy 90° rotation of the measuring beam, and the target unit was designed to be sensitive to 18 GMEs of the three linear axes. Stability, repeatability, and comparison experiments were conducted to verify the performance of the proposed system. The results showed that the stability of the position error measurement is ± 6.3 nm. For straightness error measurement, the stability, repeatability error, and residual are within ± 60.3 nm, ± 0.5 μm, and ± 0.7 μm, respectively. These are within ± 0.12 arcsec, ± 0.5 arcsec, and ± 0.5 arcsec for the pitch and yaw measurements, and within ± 0.37 arcsec, ± 1.5 arcsec, and ± 1.0 arcsec for the roll measurements, respectively. For squareness error measurement, the repeatability error and residual are within ± 0.6 arcsec and ± 1.6 arcsec, respectively. Compared with a laser interferometer, the proposed system can measure the 21 GMEs of a three-axis machine tool with one-step installation. Without accuracy loss, the measurement efficiency is approximately 45 times higher than that of a laser interferometer, thus providing a new quick and accurate measurement method of GMEs and error compensation of CNC machine tools.

Geometric- and force-induced errors compensation and uncertainty analysis of rotary axis in 5-axis ultra-precision machine tool

In machining of a microstructure on a free form surface, a five-axis ultra-precision machine tool has a great advantage in its flexibility and efficiency compared to a conventional machine tool. However, rotary axes in the five-axis machine tool are sensitive to position errors due to low rigidity. In addition, micro-tools, which are utilized in micro-machining, have low stiffness. The low rigidity and stiffness of the rotary axes and tool introduce geometric errors in machining. Therefore, the position and orientation errors of the rotary axes and the micro-tool must be identified and compensated. Many components contribute to uncertainty of the measurements which will inevitably reduce the reliability of the results. Therefore, it is necessary to analyze the uncertainty of each component. This paper proposed a method to comprehensively identify position-independent geometric errors (PIGEs) and force-induced errors (FIEs) of a system from the rotary axis on the machine to the micro-tool. A simple and practical error model to represent the tool position with respect to the angle of the rotary axis was built. Uncertainty was analyzed to validate the reliability of the proposed method. Cutting experiments were carried out on an AISI 1018 steel and an Al6061 workpiece, and the results were analyzed to verify the proposed model.