CNC Machine Tools Research Articles

Robotic milling stability optimization based on robot functional redundancy

PurposeCompared with the high stiffness of traditional CNC machine tools, the structural stiffness of industrial robots is usually less than 1 N/µm. Chatter not only affects the quality of robotic milling but also reduces the accuracy of the milling process. The purpose of this paper is to reduce chatter in the robotic machining process.Design/methodology/approachFirst, the mode coupling chatter mechanism is analyzed. Then the milling force model and the principal stiffness model are established. Finally, the robot milling stability optimization method is proposed. The method considered functional redundancies, and a new robot milling stability index is proposed to improve the quality of milling operations.FindingsThe experimental results prove a significant reduction in force fluctuations and surface roughness after using the proposed robotic milling stability optimization method.Originality/valueIn this paper, a new robot milling stability index and a new robot milling stability optimization method are proposed. This method can significantly increase the milling stability and improve the milling quality, which can be widely used in the industry.

Steady state prediction model and spectrum characteristics analysis of cutting force for CNC lathe considering variable machining parameters

This paper presents a steady state cutting force prediction model for CNC lathe considering feed displacement. The frequency spectrum characteristics of the cutting force in principal direction are analyzed. The turning test for 12Cr18Ni9 stainless steel workpiece is carried out by CNC machine tool ETC1625P firstly, and the real-time displacement data of worktable in the turning process are obtained. The cutting force data varying with the displacement of the worktable under different cutting parameters are obtained by cubic spline interpolation. The mechanical force model including metal shearing force and plowing force is established, so as to predict the steady state cutting force accurately corresponding to different displacement of the worktable. The influence of different cutting parameters on the amplitude frequency characteristics for the low frequency part of the cutting force in principal direction is studied by using wavelet packet decomposition and fast Fourier transform. The results show that the method can predict the steady cutting force accurately and reflect the amplitude frequency characteristics of the low frequency part for the cutting force in principal direction under different cutting parameters.

Spindle Thermal Evaluation Thermal on Realiability Test of Numerical Controlled Lathe

In the process of designing and manufacturing of CNC machine tools, reliability testing is needed to determine the reliability of the main component design and sub assembly. The precision of machine tools is greatly influenced by the temperature generated when the machine tool operates, both which arise from friction and overload.
 Evaluation of spindle axis errors caused by thermal distortion of the spindle bearing is based on ISO 230-3: 2007, ISO 10791-10: 2001 and ISO 13041-8: 2004. Temperature measurement at several measuring points on the spindle head to determine the increase in temperature on the main spindle bearing when getting more load or cutting process.
 The evaluation results showed that spindle erroneous error with a temperature increase in the spindle head was 16.3 oC by 13 um with no loading, while with loading 71.9 um with a temperature increase of 18.0 oC.

Compilation of load spectrum of CNC machine tools reliability test program based on Weibull kernel function

The load spectrum is the foundational data for reliability tests that simulate actual working conditions. Determining typical working conditions and accurate load extrapolation are the key to the load spectrum. This paper proposes a method for compiling the load spectrum of a reliability test program of CNC machine tools based on Weibull kernel function. The aforementioned method aims to address the issues commonly encountered in the load spectrum, such as complex factors that affect typical working conditions, heterogeneous load data of working conditions, and reliance on assumption distribution during load extrapolation. The method involves a determination process for typical working conditions using the Weibull kernel function and a non-parametric load extrapolation method based on kernel function. Firstly, this paper proposes a kernel density estimation method based on Weibull kernel function to address the issues of probability density contribution value deviation caused by the inconsistency between the conventional kernel function symmetry and the off-peak characteristic of load. The approach is based on the physical meaning and generalization of the Weibull distribution parameters and is used to determine the typical working conditions of CNC machine tools. Secondly, this paper proposes a nonparametric rainflow extrapolation method for cutting loads of CNC machine tools based on two-dimensional kernel density estimation. The approach takes advantage of the nonparametric rainflow extrapolation method and considers the characteristics of the cutting load. And the load spectrum of the three-dimensional cutting force of the CNC machine tool is constructed using the mean-range probability density distribution of the cutting load. Finally, the reliability test program loading spectrum of CNC machine tools is determined using the Conover ratio coefficient. Through case analysis, it can be known that the nuclear density estimation method proposed in this paper can improve the compilation accuracy of the load spectrum and has practical application significance.

Dynamic Modeling of Motorized Spindle System with Unbalanced Mass and Spindle Inclination

The high-speed motorized spindle has been wildly used in the field of aerospace processing, due to its advantages such as high speed, high precision, and high efficiency. CNC machine tools used for processing aerospace products require high machining accuracy, and once the spindle fails, it will seriously affect the quality of product processing. Thus, it is important to study the faults of the spindle, especially the faults caused by subtle errors. In this work, a dynamic model of a spindle with unbalanced mass fault and spindle inclination fault is established, and the natural frequencies and mode shapes of the motorized spindle are calculated by using the whole transfer matrix method (WTMM). The deflections of the spindle initial end in the different situations are discussed when the two faults happen independently. The results show that the spindle end deflection of the same fault has different sensitivity at different speeds. At the third order of natural frequencies, the deflection of a motorized spindle is greatest regardless of the fault that occurs. Although the motorized spindle rotates at the same speed, different faults could cause different mode shapes. At the lower speed, when the unbalanced mass fault happens, the mode shape is in an arched shape, and while the spindle inclination fault happens, the mode shape is in a concave shape.

A support vector regression-based method for modeling geometric errors in CNC machine tools

A support vector regression-based method for modeling geometric errors in CNC machine tools

Adaptive Control of CNC Machine Tool Cutting Process Based on Artificial Brain-Computer Interface Technology

Adaptive Control of CNC Machine Tool Cutting Process Based on Artificial Brain-Computer Interface Technology

Research on Machining Error Control Method Driven by Digital-twin Model of Dynamic Characteristics of Machining System

The position of each component structure in the machining space of the machining system directly affects the machining quality of the workpiece. In this paper, RBF neural network is used to study the spatial dynamic characteristics of the translational axes of five-axis CNC machine tools. It is the basis for building an evolvable knowledge base. Process evaluation, information feedback, and iterative optimization of thin-wall parts were carried out using digital-twin technology. Firstly, the model simulation of the translational machining space of a three-dimensional five-axis CNC machine tool is carried out by using the finite element method. The RBF neural network predicts the natural frequency of a translational machining space dynamic characteristics. It is further used to construct the dynamic characteristic spectrum of the translational space of CNC machine tools. Secondly, the machine tool flush space cutting process is built with a digital-twin system to optimize the iterative mechanism for machining process optimization. Finally, the method’s effectiveness was verified by experiments on milling thin-walled parts on a dual-turntable five-axis CNC machine and by contouring error measurement experiments. The results show that the average error of the optimized thin-wall contour is reduced by 26.9%. The iterative mechanism can continuously optimize the machining error and improve the machining accuracy. The iterative mechanism can continuously optimize the machining error and improve the machining accuracy.

Operations-aware novelty detection framework for CNC machine tools: proposal and application

Digitisation offers manufacturing companies new opportunities to improve their operations and competitiveness in the market by unleashing potentialities related to real-time monitoring and control of operating machines. Through condition-based and predictive maintenance, the knowledge about the health state and probability of failure of the machines is improved for better decision-making. Amongst them, CNC machine tools do represent a complex case from a maintenance viewpoint as their operations are ever-changing and their high reliability brings to a lack, or limited set, of run-to-failure data. To address the problem, the research work proposes an operations-aware novelty detection framework for CNC machine tools based on already-in-place controllers. The framework is based on statistical modelling of the behaviour of the machine tools, namely through gradient boosting regression and Gaussian mixture models, to identify the health state considering varying operations through time. The proposed solution is verified on sixteen multi-axis CNC machine tools in a large manufacturing company. The results show that the proposed solution can effectively support maintenance decisions by informing on the health states while discerning between varying operations and abnormal/faulty states of interest. This solution represents a brick in a cloud-edge-based industrial information system stack that can be further developed for shop floor-integrated decision-making.

An integration of PSO-ANN and ANFIS hybrid models to predict surface quality, cost, and energy (QCE) during milling of alloy 2017A

An alarming pace of increase in worldwide energy consumption is being caused by population expansion and economic development, particularly in emerging market countries. Due to their efficiency and reproducibility in accomplishing high-precision machining, CNC machine tools are widely employed in most metal machining processes. The use of simple machining features to search for the assignment of cutting parameters and the machining process on the output variables is limited since a part, in reality, can contain complex interacting features. Therefore, this study focuses on pocket/groove features by integrating grey relational analysis (GRA) and hybrid PSO-ANN and ANFIS algorithms to optimize and predict surface quality, cost and energy consumption (QCE). Taking into account the population size of the swarm (pop) and the number of neurons (n) in the hidden layer, a parametric study was carried out to find the best prediction using the hybrid algorithm PSO-ANN. This study reported the highest trained correlation values (R2) for all output variables (greater than 0.97%). The study shows that the assignment of machining strategies and sequences on energy consumption can reach 99.25% between the minimum and maximum values. The mean square error (MSE) data demonstrates that the PSO-ANN model is effective. Indeed, an MSE improvement of 99.84%, 99.87%, and 97.62% has been demonstrated in terms of Etot, Ctot, and Ra, respectively, of the PSO-ANN model compared to ANFIS. This study reveals the potential of the PSO-ANN hybrid for multi-criteria prediction (quality, cost, and energy: QCE) by comparing it with the ANFIS model.

Modification of CNC Machine Tool Operations and Structures Using Finite Element Methods: A Review

Modification of CNC Machine Tool Operations and Structures Using Finite Element Methods: A Review

Exploration and Realization about Teaching Experimental of CNC Machine Tool Based on Virtual Simulation Technology

Exploration and Realization about Teaching Experimental of CNC Machine Tool Based on Virtual Simulation Technology

A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm

Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. The Siemens SINUMERIK EDGE (SE) Box system collects signals from the spindle and axes of a CNC machine tool. In this study, features were extracted from signals in time, frequency, and time–frequency domains. The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. The SVM hyperparameters were optimized simultaneously with feature selection, and the model was tested with test data. The proposed model accurately predicted critical machining conditions for unbalanced datasets. The classification model indicates an average recall, precision, and accuracy of 80%, 86%, and 95%, respectively, when predicting workpiece quality and tool breakage.

MINIMIZATION OF DEFLECTION ERROR IN FIVE AXIS MILLING OF IMPELLER BLADES

The 5-axis CNC machine tools are used for manufacturing free form surfaces of sophisticated parts such as turbine blades, airfoils, impellers, and aircraft components. The virtual machining systems can be used in order to analyze and modify the 5-axis CNC machine tools operations. Cutting forces and cutting temperatures induce deflection errors in thin-walled structures such as impeller blades through machining operations. Thin-walled impeller blades' flexibility can result in machining errors such as overcutting or undercutting. So, decreasing the deflection error during machining operations of impeller blades can achieve the desired accuracy in produced parts. Optimized machining parameters can be obtained to minimize the deflection of machined impeller blades. In terms of precision and efficiency enhancement in component production processes, a virtual machining system is developed to predict and minimize deflection errors of 5-axis milling operations of impeller blades. The deflection error in machined impeller blades is calculated by using finite element analysis. The optimization methodology based on the genetic algorithm is applied to minimize the deflection error of impeller blades in machining operations. To validate the integrated virtual machining system in the study, the impeller is milled by using a 5-axis CNC machine tool. The CMM machine is used in order to measure and analyze deflection error in the machined impeller blades. As a result, by using the developed virtual machining system in the study, accuracy and efficiency in 5-axis milling operations of impellers can be increased.

Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

In order to revise the deviation caused by ignoring the dynamic character of fault propagation in traditional fault propagation path identification methods, a method based on the maximum occurrence probability is proposed to identify the key fault propagation path. Occurrence probability of fault propagation path is defined by dynamic importance, dynamic fault propagation probability and fault rate. Taking the fault information of CNC machine tools which subject to Weibull distribution as an example, this method has been proven to be reasonable through comparative analysis. Result shows that the key fault propagation path of CNC machine tools is not unique, but changes with time. Before 1000 hours, key fault propagation path is electrical component (E) to mechanical component (M); after 1000 hours, key fault propagation path is auxiliary component (A) to mechanical component (M). This change should be taken into account when developing maintenance strategies and conducting reliability analysis.

Computer modeling and software research of car and engine parts

Problem. The development of automotive technology for various purposes and the improvement of existing car models, requires a fast and flexible design process. Spatial models of details and nodes are the starting points for design, and the necessary design documentation is performed on their basis. When manufacturing parts on CNC machines, it is necessary to quickly obtain a control program that will increase the accuracy of the product by ensuring the optimal trajectory of the mutual movement of the part and the tool. Therefore, the creation of a comprehensive methodology for the design of car parts, their basic inspection and obtaining design documentation is an important technical task. Goal. The main goal of the work is to determine the rational sequence and basic principles of computer-aided design, research and manufacturing of car and engine parts, and the development of mathematical spatial models of surfaces in order to optimize control programs for CNC machines. Methodology. The approaches adopted in the work for solving the set goal are based on general design principles. The methods of conducting static and dynamic calculations, as well as spatial mathematical modeling of the processes of manufacturing parts were also used. Results. A comprehensive methodology for designing parts of engines and cars has been developed. The main principles of creating spatial models have been determined in order to achieve their flexibility and simple editing. Methods of automating the development of drawings and design documentation are described. The software options for the primary verification of static strength and frequency analysis are considered. A calculation program has been developed for constructing transient and amplitude-phase-frequency characteristics. The mathematical spatial modeling of typical surfaces of the camshaft is provided with the aim of further research and optimization of the development of a control program for processing on a CNC machine tool. Originality. The defined basic principles of designing parts of engines and cars provide an opportunity to create a flexible spatial model and more fully automate the process of drawing up technical documentation. The developed mathematical spatial models of the supporting and working surfaces of the camshaft make it possible to write a control program with the determination of the optimal trajectory of the mutual movement of the part and the tool. Practical value. The obtained results can be recommended when designing car parts and assemblies.

Machine tool fault classification diagnosis based on audio parameters

With the increasing precision, complexity, and diversification of CNC machine tools, the requirements for fault detection technology of CNC machine tools are getting essential.If problems such as tool wear and spindle locking can not be found in time, it will lead to the waste of a large number of raw materials and production time. Aiming at the fault problem of high-precision NC machine tools, this paper uses the method based on DWT and statistical domain to extract features from audio signals to form data sets, and proposes a lightweight convolution neural network to identify the fault types of machine tools.Firstly, features are extracted from MIMII public data sets, and evaluates the data sets by using three machine learning models (SVM, KNN, and GNB).The results show that when facing different materials and structures, the model trained by extracting features from mechanical audio signals based on wavelet transform and statistical domain has good prediction accuracy.Then, this method is used for audio data of CNC machine tools in a real factory environment.The data set includes six common machine tool faults, including normal operation, tool wear, spindle locking, tool idling, abnormal impact, and back cutting amount.A lightweight convolution neural network was used to identify fault types in machine tools.The experimental results show that the recognition accuracy of the trained model is 97.85%, AUC is 0.9984, and F1 is 0.9769. Compared with the DNN proposed by Jong-Yi (Kuo et al., 2022) [1] , it has been improved in all aspects. The accuracy of fault recognition is improved from 96.1% to 97.85% compared with the method of sound fusion feature and OCSVM (Ding et al., 2022) [2].

Analysis of the configuration characteristics of the chaotic attractor for machine tool dynamic error under the singular spectrum

To explore the chaotic characteristics of the motion error of CNC machine tools, this paper proposes a chaotic attractor feature extraction method for the early motion error of CNC machine tools. Firstly, the chaotic verification of each time series feature of machine tool motion error proves that the change of CNC machine tool motion error is a nonlinear change process with chaotic characteristics. It then establishes the multivariate error phase space and performs a singular spectrum analysis. Because the motion error is strongly disturbed by noise, it is difficult to determine the number of singular values by the traditional singular spectrum analysis method, and this paper proposes to calculate the phase space index of the reconstructed sequence by traversing the number of singular values, to determine the main feature components; Finally, under different noise environments and different signal lengths, three feature extraction methods are introduced for experimental comparison with this paper's method, which shows that the chaotic attractor reconstructed after determining the number of singular values has a more apparent geometric structure. The method proposed in this paper has advantages in machine tool motion error.

Evaluation of Measurement Uncertainty Obtained with a Tool Probe on a CNC Machine Tool

Evaluation of Measurement Uncertainty Obtained with a Tool Probe on a CNC Machine Tool

Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm

The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.