Reliability Of Machine Tools Research Articles

A novel FMECA method for CNC machine tools based on D-GRA and data envelopment analysis

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement.

A Comprehensive Evaluation Method for Generalized Reliability of CNC Machine Tools Based on Improved Entropy-Weighted Extensible Matter-Element Method

A Comprehensive Evaluation Method for Generalized Reliability of CNC Machine Tools Based on Improved Entropy-Weighted Extensible Matter-Element Method

Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools.

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.

Research on Reliability of CNC Machine Tools Based on Sectional Model Involving Two Weibull Distributions

To study the failure distribution rule of CNC machine tools and accurately fit the distribution model of failure time, the field test failure data from a series of CNC machine tools are analyzed in this paper. A sectional model involving two Weibull distributions is used to fit the failure time of CNC machine tools. The D-test method is used to examine the fit of the model. By comparing the goodness of fit with the ordinary Weibull distribution model, it is finally verified that the sectional model involving two Weibull distributions is more consistent with the failure data distribution rule of the CNC machine tools.

Accuracy reliability analysis of CNC machine tools considering manufacturing errors degrees

To evaluate the accuracy reliability of machine tools, an evaluation method of accuracy reliability considering the machining error out of tolerance is proposed, and an accurate mathematical description is given to realize the quantitative evaluation of machining accuracy under different machining loads. On this basis, the basic connotation and mathematical model of accuracy retention are analyzed, and the influence of different machining loads on the accuracy retention is studied. Finally, experiments are carried out to detect the variations of machining errors of machine tools under different loads and the quantitative evaluations of accuracy reliability and retention of machine tools are completed, which verify the validity of the proposed methods.

A novel method of key meta-action unit integrated identification for CNC machine tool reliability

There are many parts in CNC (computer numerical control) machine tool. Considering the time and economy, it is impossible to analyse them one by one. In order to reduce the blindness of researchers in choosing analysis objects, it is necessary to identify the key parts of CNC machine tool. MAU (meta-action unit) is the smallest structure that can reflect the function and performance of CNC machine tool, so it is taken as the research object. A new method for key MAU identification of CNC machine tool is proposed, as follows. Firstly, all the meta-action units were obtained by the FMA (function-motion-action) decomposition method, and the harmfulness was selected to analyze the influence of MAU on the reliability of CNC machine tool. Secondly, fuzzy theory is integrated into RPN (risk priority number) to obtain the harmfulness of MAU. Thirdly, the subjective weight of the harmfulness about the MAU was obtained by the combination of MAHP (modified analytic hierarchy process) and optimal sequence diagram method, and the objective weight was obtained by CRITIC (criteria importance though intercrieria correlation) method. Fourthly, the subjective and objective weights were integrated by a linear weighting method, and the result were taken as the comprehensive weight of the harmfulness of MAU. Finally, the harmfulness of the MAU could be obtained by its evaluation value and weight, and the MAU with greater impact on the reliability of CNC machine tool could be obtained by Pareto principle, which is the key MAU. A CNC machine tool made in China is analysised, and the effectiveness of the proposed method is verified by the comparison of various methods and experimental analysis.

THERMODYNAMIC MODELING OF OBTAINING WEAR-RESISTANT COATINGS USING COMPOSITE POWDER CHARGES

Abstract. The article provides an overview and analysis of obtaining wear-resistant coatings for parts working in aggressive environments of metallurgical production. The solution of these problems is associated with the hardening of the surface layers of products. Their role in the durability of machines and mechanisms has especially increased at the present time, since the development of most industries is associated with an increase in loads, temperatures, and the aggressiveness of the environments in which the part operates. The titanizing process is an effective method of increasing the reliability and durability of machine parts, tools and technological equipment due to the creation of titanized layers on the surface of the machined parts, which have a unique set of physical and chemical properties. The results of thermodynamic modeling for the production of wear-resistant titanium-based coatings for the determination of rational composite powder charges are presented. The use of thermodynamic modeling makes it possible to quantitatively model and predict the composition and properties of complex heterogeneous, multi-element, multi-phase systems in a wide range of temperatures and pressures, considering chemical and phase transformations. The results of research on wear resistance revealed that, when tested under friction-sliding conditions, the best wear resistance among the considered alloyed titanium coatings was achieved by coatings alloyed with chromium and silicon. Their wear resistance is 1.7-1.9 times higher than that of coatings obtained under isothermal conditions.

Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing

• We solved the fault prediction problem with the combination of CNN and LSTM . • We solved the maintenance decision problem with the deep reinforcement learning. • The enhanced visual guidance of the maintenance process was realized by AR , which could integrate the invisible maintenance information into the machine tools. • The proposed predictive maintenance approach outperforms others in terms of accuracy and effectiveness. In the Industry 4.0 era, the number and complexity of machine tools are both increased, which is prone to cause malfunctions and downtime in the manufacturing process. Predictive Maintenance (PdM), as a pivotal part of Prognostics and Health Management (PHM), plays a vital role in enhancing the reliability of machine tools in the Internet of Things (IoT)-enabled manufacturing. In order to realize a highly reliable maintenance plan integrated with the fault prediction, the maintenance decision-making, and the Augmented Reality (AR)-enabled auxiliary maintenance, an intelligent predictive maintenance approach for machine tools is proposed in this paper via multiple services cooperating within a single framework. The fault prediction service is supported by the combination of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). Specified features from massive production data acquired by IoT can be comprehensively extracted using CNN, and their nonlinear relationship can be fitted by LSTM. Based on the fault prediction result, deep reinforcement learning is adopted to achieve the production control and schedule maintenance personnel if a fault code appears. On top of this, the guidance information from the maintenance experience database can be integrated into the faulty machine tools in the form of visibility through AR, which can guide the maintenance personnel to complete maintenance tasks more efficiently. Moreover, the remote expert service is also integrated in the AR-supported auxiliary maintenance, which is activated to solve unexpected faults that are not stored in the maintenance experience database. Comparative experiments are conducted in the IoT-enabled manufacturing workshop with real-world case studies, and the results demonstrate that the proposed predictive maintenance approach is both effective and practical.

Optimal design of loading device based on a redundantly actuated parallel manipulator

Reliability of machine tools has attracted much attention both in academia and industry. The loading device is very important for the reliability study of CNC machine tools as it can apply multi-axis dynamic forces to the spindle to replace cutting tests. A loading device based on the redundantly actuated parallel manipulator (R5LD) with strong loading capability is proposed in this paper. Firstly, to evaluate the loading capability, this paper proposes new transmission indices for redundantly actuated parallel manipulators (PMs). Besides, the prescribed flexible orientation workspace and regular workspace are defined for the loading device. Then, the corresponding performance atlases are represented graphically in the design space. The structure parameters of the R5LD are achieved in the optimum region of performance atlases. Finally, the R5LD with the optimum parameters is compared with the loading device based on the corresponding non-redundant PM. The R5LD has a larger regular workspace and better transmission performance. The additional UPS chain of the R5LD makes the configuration more symmetrical, balances forces distribution among chains, and strengthens the loading capability of the loading device.

Time-varying comprehensive evaluation technology of CNC machine tool RMS based on improved ADC model

The quality of CNC (computer numerical control) machine tool is the key to the survival of manufacturing enterprises. How to comprehensively and accurately evaluate its quality is very important for the timely discovery and improvement of machine tool defects. The current research either only evaluates a certain quality characteristic of CNC machine tool, or ignores the dynamic characteristics of CNC machine tool quality, which reduces the accuracy of quality analysis results. To address these problems, firstly, RMS (reliability, maintainability, supportability) was taken as the comprehensive evaluation indexes of CNC machine tool quality characteristics to improve the comprehensiveness of the analysis. Secondly, considering the dynamic characteristics of machine tool reliability, the ADC (availability, dependability, capability) model was improved to obtain a comprehensive quality evaluation index function ERMS with time-varying characteristics. Finally, the characteristics of ERMS were analyzed, and its evaluation criteria were also given. Several CNC machine tools made in China were tracked, and the collected fault data were analyzed by the proposed method. The ERMS of the machine tools was obtained, and its characteristics were analyzed. The results verify the correctness of the proposed method, which provides theoretical basis and technical support for the improvement of the quality of CNC machine tool and the market competitiveness of enterprises.

Reliability allocation method of comprehensive weight computer numerical control machine tool based on failure correlation and factor correlation

AbstractAccording to the shortage of independent hypothesis allocation models between influence factors and subsystems, a reliability allocation method considering influence factor correlation and subsystem failure correlation is proposed. The CNC (Computer Numerical Control) machine tool is taken as the research object. Using Copula function to analyze failure correlation analysis between subsystems, establish subsystem complete reliability model and integrated importance measure model under failure correlation. The four factors of influencing reliability allocation are subsystem complexity, integrated importance measure of subsystem based on failure correlation, failure frequency, and specific gravity ratio of failure downtime. The correlation analysis among the influencing factors was carried out by using gray correlation theory, and the subjective weight of the subsystem was determined. The IEW‐VIKOR (Information Entropy Weight‐Vlsekriterijumska Optimizacija I Kompromisno Resenje) method is used to determine the objective weights of subsystems. The comprehensive weight of the subsystem is also obtained through combinatorial empowerment. The reliability allocation of the CNC machine tool subsystem was carried out according to the failure correlation reliability model, and the results of failure independence and failure correlation hypothesis allocation were compared and analyzed. When the reliability index is the same, the reliability allocation method proposed in this paper can assign lower reliability to each subsystem, which is of great significance to improve the reliability of CNC machine tools and reduce the difficulty of reliability design

Cutting-state identification of machine tools based on improved Dempster-Shafer evidence theory

The reliability of machine tools is highly influenced by the cutting state. The traditional recognition method of cutting state is emphasized on a single classifier, which has the weakness of low identification accuracy and strong randomness. This paper proposes a cutting-state identification method based on improved Dempster-Shafer (DS) evidence theory. This method is divided into multi-classifier preliminary-diagnosis layer and improved DS information-fusion layer. The wavelet packet analysis method is extracted as the input of multi-classifier (Back Propagation (BP) neural network, genetic algorithm (GA) optimized BP neural network and thinking evolution (mind evolutionary algorithms) MEA optimized BP neural network). After the preliminary judgment, the improved DS information-fusion method is integrated as the final judgment, and finally, the effectiveness and feasibility of the method are verified.

FEATURES OF CHEMICAL AND THERMAL TREATMENT OF MICRO-OBJECTS OF DIFFERENT STRUCTURAL CLASSES

One of the ways to increase the reliability and durability of machine parts, tools and technological equipment is chemical heat treatment. For hardening steel parts for various purposes, thermal diffusion alloying with boron is widely used. Traditional objects of chemical and thermal treatment are macro-parts, for which the thickness of the diffusion layer is several orders of magnitude less than the linear dimensions of the part. Recently, a new direction of chemical treatment is actively developing - diffusion alloying of micro-objects. The transition from the macrolevel to the microlevel fundamentally changes the diffusion alloying technology. In this work, we investigated the processes of diffusion alloying with boron of micro-objects, in particular, products with a circular cross-section (wires) of various structural classes. According to the research results, the factors influencing the thickness of boride layers were determined. The intensity of borating of micro-objects of various structural classes has been determined.

Reliability assignment of a heavy-duty CNC machine tool spindle system based on fault tree analysis

Research on the distribution of machine tool reliability indexes is of great significance to improve the reliability of machine tools in the design and manufacture stages. Owing to the complex structure of heavy-duty CNC machine tools, multi-source and heterogeneous failure causes, etc., the traditional single method cannot get accurate results. In this paper, based on the FTA method, a three-level machine tool fault tree model is established to carry out research on reliability index assignment. Then, as the levels progress, the distribution of reliability indicators is based on probability importance analysis, reliability redistribution method, and AHP respectively. Finally, the top-down reliability assignment results are obtained. After analysis, the structure obtained by combining multiple reliability allocation methods is more in line with the actual situation. According to the results, the weak parts of the machine tool are designed and improved, thereby improving the reliability of the machine tool.

Tool wear prediction based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network in manufacturing

Computer numerical control (CNC) machine tool is the foundation of the equipment manufacturing industry, and its technical level is an important indicator to measure the development level of a country’s equipment manufacturing industry. Tool wear during machining has a great impact on the important performance indicators of CNC machine tools, such as machining accuracy, machining efficiency and reliability. Tool wear monitoring is of great significance to improve the machining efficiency, machining accuracy and reliability of CNC machine tools. Multidomain features (time domain, frequency domain and time–frequency domain) can accurately characterise the degree of tool wear. However, manual feature fusion is time consuming and prevents the improvement of monitoring accuracy. A new tool wear prediction method based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network is proposed to solve these problems. In this method, multidomain features of cutting force and vibration signals are extracted and recombined into feature tensors. The proposed hypercomplex position encoding and high-dimensional self-attention mechanism are used to calculate the new representation of input feature tensor, which emphasizes the tool wear sensitive information and suppresses large area background noise. The designed depth-wise separable convolutional neural network is used to adaptively extract high-level features that can characterise tool wear from the new representation, and the tool wear is predicted automatically. The proposed method is verified on three sets of tool run-to-failure data sets of three-flute ball nose cemented carbide tool in machining centre. Experimental results show that the prediction accuracy of the proposed method is remarkably higher than other state-of-art methods. Therefore, the proposed tool wear prediction method is beneficial to improve the prediction accuracy and provide effective guidance for decision making in processing.

Production data evaluation analysis model: a case study of broaching machine

ABSTRACT To increase machine tool reliability and overall product quality, process quality must be increased for all parts and components. Aside from quality, processing efficiency is also a crucial indicator of machine tools, which has brought about various customized machine tools with high processing efficiency. As broaching machines offer high production efficiency and are simple in structure and operation, we used broaching machines as a case study to construct a model for process capability evaluation, analysis, and improvement. Furthermore, the Six Sigma quality index directly reflects process yield as well as process quality levels. We then derived the relationships between the quality level of the product and those of individual quality characteristics to establish quality assessment standards and created a process quality analysis chart for products with multiple quality characteristics. We used a mathematical programming model to find the corresponding coordinate point of the upper confidence limit for Six Sigma quality indices. Process engineers need only check whether the corresponding coordinate points fall in the acceptable-quality zone or the poor-quality zone to identify critical-to-quality characteristics. Finally, we used a cause-and-effect diagram to determine the causes of poor process quality and formulate suggestions for improvement.

Enhancing machining accuracy reliability of multi-axis CNC Indexed by: machine tools using an advanced importance sampling method

The purpose of this paper is to propose a general precision allocation method to improve machining performance of CNC machine tools based on certain design requirements. A comprehensive error model of machine tools is established by using the differential motion relation of coordinate frames. Based on the comprehensive error model, a reliability model is established by updating the primary reliability with an advanced importance sampling method, which is used to predict the machining accuracy reliability of machine tools. Besides, to identify and optimize geometric error parameters which have a great influence on machining accuracy reliability of machine tools, the sensitivity analysis of machining accuracy is carried out by improved first-order second-moment method. Taking a large CNC gantry guide rail grinder as an example, the optimization results show that the method is effective and can realize reliability optimization of machining accuracy.

Research on Electrical Control System of CNC Machine Tool Based on Vector Control

With the rapid development of science and technology, the society demands more and more diversification of products, which requires the cycle of product renewal to be shorter and shorter, which makes the proportion of multi-variety and small batch production increase obviously. CNC (Computer Numerical Control) machine tool is a very popular production machine in China’s industrial production, which is mainly composed of hardware circuit, mechanical part and lower computer software. CNC machine tools play an important role in solving the problem of precision or complex parts, and the data of CNC machine tools in mechatronics system are typical. Numerical control technology is related to a country’s position in international industry, so accelerating the development of advanced numerical control technology and strengthening the construction of numerical control equipment are important methods to improve a country’s position in international industry. Based on vector control technology, this paper discusses the design of electrical control system of CNC machine tools, and studies the methods to improve the safety, reliability, machining accuracy and productivity of CNC machine tools, in order to provide reference for the further application of vector control in CNC machine tools.

Reliability allocation of rotary ultrasonic vibration-assisted EDM machine tool based on maximum entropy ordered weighted average and constraint under the index of overall cost

In this paper, the reliability allocation and index of overall cost of rotary ultrasonic vibration-assisted EDM machine tool were studied. The failure rate of each module of the rotary ultrasonic vibration-assisted EDM machine tool can be accurately predicted by the maximum entropy ordered weighted average algorithm so that the corrected maintenance cost can be predicted. And effective control of preventive maintenance costs can be achieved by selecting the best maintenance times under the premise of reliability. The cost of corrective maintenance can be reduced by improving modules with a high failure rate, so as to realize the purpose of cost optimization under the premise of determining the maintenance time and reliability. The reliability function of the rotary ultrasonic vibration-assisted EDM machine tool is established according to the series-parallel relationship of each module. The results showed that the reliability of rotary ultrasonic vibration-assisted EDM machine tool is stabler at the non-fixed maintenance period according to the comparison between the non-fixed and the fixed maintenance period.