Numerical Control Machine Tools Research Articles

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

Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools

Thermal errors are one of the main factors affecting the accuracy of high-precision computer numerical control machine tools. Modeling and compensation are the most common approaches for reducing the influence of thermal error on machine tool accuracy. Accuracy and robustness are the key indicators of machine tool thermal error prediction models, especially under different working conditions. Existing thermal error modeling algorithms provide only point predictions of the thermal error; however, interval predictions of the thermal error are important for understanding the stochastic nature of the thermal error prediction and analysis of reliable risk. To address these challenges, this study proposes a novel thermal error modeling method based on Gaussian process regression (GPR) that provides interval predictions of thermal error and achieves high prediction accuracy and robustness. First, multiple batches of experimental data are used to establish the GPR thermal error model to ensure sufficient modeling information. Second, while existing methods select temperature-sensitive points (TSPs) before modeling, the GPR algorithm can adaptively select TSPs during training of the thermal error GPR prediction model. Third, the proposed model provides interval predictions of thermal errors for evaluating the thermal error prediction reliability. The prediction effects of the GPR model are compared with those of existing thermal error models. The experimental results indicate that the proposed model has the highest prediction accuracy and robustness under different working conditions of the tested compensation models. Furthermore, thermal error compensation experiments are conducted to verify the effectiveness of the proposed model.

Development of low-cost heterodyne interferometer with virtual electronic phasemeter

Commercial laser interferometers are conventionally used to measure the positioning error of a long linear stage in multiaxis computer numerical control machine tools. However, commercial laser interferometers are costly and difficult to use. Therefore, a low-cost photodetector-based heterodyne interferometer combined with an electronic phasemeter module was proposed for precise measurement of the positioning error of a long linear stage. The proposed heterodyne interferometer was combined with a virtual electronic phasemeter that employs a self-developed signal-processing technique. Our core algorithm and proposed photoelectric-signal-processing technique were developed using the LabVIEW human–machine interface. Moreover, to verify the performance of the proposed heterodyne interferometer, a laboratory-built prototype was constructed and used to measure the positioning error of a long linear stage. The experimental results indicated that the positioning accuracy of the proposed interferometer was ±4.5 μm for a linear stage with a displacement of 250 mm; the results obtained were comparable to those obtained with a commercially available laser interferometer. The proposed heterodyne interferometer can thus be used in other applications related to precision engineering.

Theoretical Research on Flow and Heat Transfer Characteristics of Hydrostatic Oil Film in Flat Microfluidic Boundary Layer

The hydrostatic bearing is the core component of ultra-precision computer numerical control (CNC) machine tools. Because the temperature rise in the oil film of hydrostatic bearings seriously affects the working accuracy of the bearings, it is important to study the flow and heat transfer characteristics of the oil film. Based on the physical model of an incompressible viscous fluid flowing in a flat microfluidic boundary layer, velocity, temperature and heat flux distribution equations of oil film are constructed by theories of heat transfer and hydrodynamics. Then, the effects of several parameters on velocity distribution, temperature distribution and heat flux distribution are analyzed, such as the upper plate velocity, the channel length, and so on. The results show that the dimensionless velocity of the oil film decreases with the increase in the upper plate velocity and the channel length. The oil film temperature distribution can be divided into three zones: the increasing zone, stabilizing zone and decreasing zone. The heat flux decreases linearly with the increase in the plate thickness, and increases linearly with the increase in the temperature difference.

Research on Machining Error Analysis and Traceability Method of Globoidal Indexing Cam Profile

The profile of the globoidal indexing cam is a spatially undevelopable surface. It needs a special computer numerical control (CNC) machine tool to finish batch production, and its machining quality will be affected by the motion error of each part of the machine tool and the clamped positioning error of the workpiece. Firstly, the mathematical model of the error of the machine tool for machining the globoidal cam surface is derived, and the influence of the error of the machine tool for machining the globoidal cam surface is given. Secondly, an error tracking method for globoidal cam profile machining error based on error sensitivity coefficient grouping is proposed, which improves the data processing speed and the accuracy of the tracking results. Finally, the error analysis and traceability method of the globoidal cam is verified by experiments, and the error traceability results are fed back to the processing link. The machining quality of globoidal cam is improved by the error compensation, which provides the key technology for the integration of the design, manufacture, and measurement of the globoidal cam.

Performance Optimization of CNC Machine Tool System Based on Sensor Data

CNC machine tools have been popularized in the development of the manufacturing industry because of their high precision, high speed, high efficiency, and safe and reliable processing. The numerical control system and the measurement system of the machine tool are the key components of the modern numerical control machine tool, especially the measurement system of the machine tool, which is a prerequisite to ensure the high precision of the machine tool. The sensor is an important part of the measurement system. Sensors are devices that can sense the measurement objects within the measurement range and convert them into output signals and information according to certain rules. They are mainly used to detect the operating objects, system status, and operating environment status of the machine tool system and effectively control the normal operation of the machine tool system. Accurate and reliable information is provided. This study takes the CNC machine tool control system as the research object and studies the optimization measures of the sensor data to its performance. First, a model of the CNC machine tool control system was built based on MATLAB/Simulink, and then, based on this model, an open-loop (without sensor) and closed-loop (with speed and position sensors) control systems were built and were finally verified by simulation. The research results show that when there are sensors, the CNC machine tool control system has better stability and robustness.

Asymmetrical transition-based corner rounding method driven by overlap elimination for CNC machining of short-segmented tool path

For realizing the continuous and stable motions of computer numerical control (CNC) machine tools, the linear motion commands (G01) have to be smoothed at the corners. Most of methods, which employ the symmetrical spline curves to round the corners, avoid the potential overlaps between the adjacent spline transition curves by imposing excessive constraints on the transition lengths or by reducing proportionally their approximation error to the corners. However, these methods will force the transition curves to over-approach the corners, thus failing to increase fully the feedrate at the corners. For solving this problem, an asymmetrical transition-based corner rounding method driven by overlap elimination is proposed to achieve the smoothness of short-segmented tool path. In this method, the effect of geometry of tool path on transition overlaps is first analyzed by means of an unconstrained pre-cornering transition model. Then, the overlaps are identified and classified according to the transition lengths and their positions on the linear segment, and the eliminating strategy for each type of overlap, which adjusts independently the transition lengths on both sides of the corner, is discussed in detail. On the basis of this, an asymmetrical spline transition curve is constructed, which can round the corners with curvature as small as possible within the specified approximation error, thereby increasing fully the feedrate at the corners. Finally, the computer simulations and machining experiment are both conducted to validate the proposed method in increasing the feedrate at the corners and shortening the machining time.

Modeling and Simulation of the NC Feed System based on MATLAB / Simulink

Feed servo system often adopts the CNC system with the digital controller as the core. In such a computer control system, it involves both continuous signal processing and digital signal processing.The numerical control machine tool feed sampling control system is a more complex electromechanical control system, and has nonlinear characteristics, the mathematical model established is a higher-order model, through the simplified transfer to the second-order system model, to facilitate computer simulation operation, so that the numerical control machine tool static performance and dynamic performance parameters to meet the design requirements.

Quality characteristic extraction for complex products with multi-granular fuzzy language based on the triple bottom lines of sustainability

Advanced product optimization design methods can greatly improve the market competitiveness of enterprises in manufacturing industry and promote the sustainable development of economy, society and environment. Quality characteristic is the carrier of product quality and closely related to sustainable product design. To manage reasonably the environmental, economic and social goals in actual product design, this paper proposes a quality characteristic extraction method for complex products with multi-granular fuzzy language based on the triple bottom lines of sustainability. Satisfaction evaluation of a customer group expressed in multi-granular fuzzy language is given full consideration with the adoption of dominant granular structures. Interval rough number (IRN) is employed for product quality function development (QFD) to fully retain the calculation simplicity of rough sets and expression flexibility of interval numbers in the correlation transformation between customer requirements and quality characteristics. A multi-objective optimization model for the quality characteristic extraction of complex products is established according to the triple bottom lines of sustainability, and an improved fruit fly optimization algorithm with the segmented variable step size is designed to solve it efficiently. Lastly, the rationality and effectiveness of the proposed method are demonstrated by the case study of a computer numerical control (CNC) machine tool.

Implementation strategy of physical entity for manufacturing system digital twin

• An implementation strategy design method of physical entities for manufacturing systems DT is proposed to analyze the implementation strategy of the DT-application from the top level, which makes the implementation strategy more scientific and takes full advantages of DT to solve specific application problems of physical entities. • Physical entity for DT deployment scheme design method is explored, which integrates the design domain of axiomatic design with the design domain of physical entity. • The operability and effectiveness of the proposed physical entity for DT deployment scheme design method is validated through CNCMT cutting tool life prediction. Digital Twin (DT) technology, as one of the top ten strategic technology trends for 2020, has received widespread attentions and gradually being used in manufacturing system. However, for the implementation strategy of these applications, most of the current researches are focused on the digital aspect (data processing, algorithm application research, etc.), few researches have been done on the physical entity aspect to guide the design of deployment scheme, which is very important for the DT implementation effect. Motivated by this need, the implementation strategy of physical entity for manufacturing system DT is studied in this paper. This strategy firstly performs the application-oriented requirements analysis of physical entity for manufacturing system DT, and then an optimal requirements deployment scheme with Axiomatic Design (AD) theory is researched. With this effort, the implementation strategy of the application from the top level is clarified, and the implementation scheme can be more scientific, giving full play to the advantages of DT technology to solve the specific application problems of physical entities. A case study for DT based Computer Numerical Control Machine Tools (CNCMT) cutting tool life prediction is carried out to show the implementation flow of the proposed strategy, and its operability and effectiveness is verified.

An asynchronous consistency algorithm in smart manufacturing cloud data centers

In the smart manufacturing factory, the fresh real-time datas and multiple replications are important for numerically-controlled machine tools. The production datas are stored in distributed cloud data centers (CDCs), so the communication latencies produced by data synchronization may impair the machining performance. In this paper, an asynchronous consistency algorithm (ACA) which is inspired by the nearest infection is proposed to reduce the communication latencies between nodes. The experimental results demonstrate that the proposed algorithm can support lower client-side latencies and increase data stability, thereby improving the production efficiency.

A generalized model selection framework for multi-state failure data analysis

PurposeThe purpose of this paper is to carry out a reliability analysis of a mechanical system considering the degraded states to get a proper understanding of system behavior and its propagation towards complete failure.Design/methodology/approachThe reliability analysis of computerized numerical control machine tools (CNCMTs) using a multi-state system (MSS) approach that considers various degraded states rather than a binary approach is carried out. The failures of the CNCMT are classified into five states: one fully operational state, three degraded states and one failed state.FindingsThe analysis of failure data collected from the field and tests conducted in the laboratory provided detailed understandings about the quality of the material and its failure behavior used in designing and the capability of the manufacturing system. The present work identified that Class II (major failure) is critical from a maintainability perspective whereas Class III (moderate failure) and Class IV (minor failure) are critical from a reliability perspective.Research limitations/implicationsThis research applies to reliability data analysis of systems that consider various degraded states.Practical implicationsMSS reliability analysis approach will help to identify various degraded states of the system that affect the performance and productivity and also to improve system reliability, availability and performance.Social implicationsIndustrial system designers recognized that reliability and maintainability is a critical design attribute. Reliability studies using the binary state approach are insufficient and incorrect for the systems with degraded failures states, and such analysis can give incorrect results, and increase the cost. The proposed MSS approach is more suitable for complex systems such as CNCMT rather than the binary-state system approach.Originality/valueThis paper presents a generalized framework MSS's failure and repair data analysis has been developed and applied to a CNCMT.

Parallel axis precision grinding of micro-tooth internal thread with the coarse-grains CBN wheels

As an important part of precision transmission system, internal threads with high precision, high hardness and small pitch are widely used in the fields of aerospace, numerical control machine tools, medical equipment and construction machinery. Aiming at micro-tooth internal thread, the research on parallel axis precision grinding of micro-tooth internal thread with the coarse-grains CBN wheels is carried out. Firstly, the grinding wheel section is designed based on the principle of parallel axis grinding of micro-tooth thread, and the theoretical error is systematically analyzed. Secondly, the maximum undeformed chip thickness of parallel axis thread grinding based on coarse-grains is established. Finally, the precision grinding experiment of internal thread is carried out. The optimal parameters can be obtained: the grinding wheel diameter is 17.08 mm, the grinding wheel angle is 90.13°, the lead is 3 mm, the pitch diameter is 21.06 mm. In this case, the maximum axial error is about 0.1 μm, the tooth profile angle error is 0.01° and the rounded radius error is about 3.5 μm. Considering the machining effect and efficiency, the truing depth is 150 μm, the feed rate is 0.2 mm/s, the grinding speed is 17,000 r/min and the grinding depth is 5 μm.

The Optimization Simulation Analysis of Heavy NC Machine Tool Bed

In order to reduce the high manufacturing cost of heavy numerical control (NC) machine tool bed, the paper first introduced the stiffness and strength calculation method of constant cross-section and constant span beam, and deduced the theoretical optimization model with the minimum cross-section area of the bed as the optimization objective. Using the interior point method completed the optimization analysis of the machine bed. Then, the ISIGHT software was used to realize the integration of design software and analysis software, and the optimization simulation analysis of the machine bed was completed under the same interface. Finally, the calculation accuracy of the theoretical model is verified by comparing the optimization results of the theoretical model and the simulation model. The weight can be reduced by more than 10% on the basis of ensuring the stiffness and strength of the machine bed. The method lays a theoretical foundation for the manufacture of the machine bed.

Dynamics analysis and energy consumption modelling based on bond graph: Taking the spindle system as an example

The spindle system of a Computer Numerical Control (CNC) machine tool is essential for improving the machining efficiency and accuracy. Although there are many studies on the energy consumption of machine tool spindle, few comprehensively consider the energy consumption and dynamics characteristics of the mechanical transmission of the spindle system. Therefore, a new bond graph method for modelling the dynamics characteristics and energy consumption of the spindle system of a CNC machine tool is proposed to solve this problem. The bond graph model of the spindle system is established based on the structure and energy flow characteristics of the spindle system. The bond graph model is converted into a dynamic simulation model that can be run in the MATLAB-Simulink environment. The dynamics and energy consumption characteristics of the spindle system are analyzed by observing the response of different input torques in the dynamic simulation model. Finally, the feasibility and practicability of the model are verified by experiments. The predictive accuracy of the proposed model is above 95%, which offers a viable approach for optimizing the design of the spindle system.

Microstructure and Machinability of Laser Cladding Co-WC on High Speed Steel Tools for Numerical Control Machine Tools

Microstructure and Machinability of Laser Cladding Co-WC on High Speed Steel Tools for Numerical Control Machine Tools

Mechanical Properties of a Novel Modular Joint of Single-Layer Aluminium Alloy Lattice Shell

To overcome the disadvantages of the discontinuous webs in the joint area of aluminium-alloy gusset joints and to further improve the efficiency of high-altitude positioning and installation of aluminium-alloy reticulated shell joints, this paper proposed a novel modular joint. Four sets of joint specimens were processed by computerized numerical control machine tools to carry out bearing capacity tests. The mechanical characteristics, failure mechanism and stiffness trends of this novel joint were explored. The results show that the proposed novel modular joints are significantly better than the traditional gusset joints in terms of bearing capacity and ductility. The flexural strength of the novel joints is 17% higher than that of the traditional gusset joints, and the novel joints have good deformability, which can prevent brittle failure. The results of elastoplastic finite element optimization analysis based on multiple contacts show that the theoretical failure mode and the curve of the load versus displacement of the joints are in good agreement with the actual measurements. Although the bearing capacity of the joints increases with increasing thickness of the modular unit and the reinforcement ring, when the thickness of the bottom plate exceeds twice the thickness of the top plate, this increase is very limited. Based on the working mechanism of the novel joints, a simplified calculation formula for their ultimate bearing capacity is proposed, which provides a theoretical basis for the engineering design and specification preparation of this type of joint henceforth.

A study of using back-propagation neural model in automatic lubrication installation for the feeding system of computer numerical control machine tool

Nowadays, the feeding systems of computer numerical control machine tools are lubricated by periodic oil supply or fixed stroke, the lubrication is insufficient in the case of high load and high-speed movement, and the lubrication is excessive during finishing and low feed rate. This study discusses the optimum lubrication timing of the feeding system. When the feeding system is moving, the servomotor torque value and current, accuracy, and oil film thickness are measured by sensors. Moreover, the lubrication characteristic model is validated and built by using the sensor values, and the optimal lubrication state estimation is obtained by using the back-propagation neural model. Then analyzed and feedback to the machine tool controller, to intelligent the lubrication system. According to the test, when the feed rate is increased by 5 m/min, the friction coefficient increases with rate, increasing the output of the frictional value of the work table by 6.90%. When the load is increased by 175 kg, the friction coefficient decreases with load, reducing the frictional value output of table movement by 6.71%. In the relationship between oil film thickness and current, the accuracy difference between the prediction and actual test results is less than 10%; in the case of the same accuracy, the oil supply frequency is reduced by 80%, and environmentally friendly machine tool has been achieved.

Comprehensive measurement model of geometric errors for three linear axes of computer numerical control machine tools

The geometric error measurement of computer numerical control (CNC) machine tools is developing towards automation and high precision. This not only improves the measurement efficiency, but also reduces the error caused by the long measurement time. This paper proposes a high-efficiency, automatic method of measuring 21 geometric errors of the three linear axes of CNC machine tools and establishes a comprehensive measurement model. The measurement system and model are applicable to linear axis geometric error measurement for all CNC machine tools. The measurement model was developed using rigid body kinematics theory and combined with the ray-tracing method. It mainly analyses the error crosstalk, system errors caused by optical components, laser beam drift, and non-parallelism errors between the measuring beams. ZEMAX was used to verify the model accuracy. Further, repeatability and comparison experiments were conducted to confirm the reliability and accuracy of the measurement system and model.

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.