Reliability Analysis of Electrical System of Computer Numerical Control Machine Tool Based on Bayesian Networks

The core of computer numerical control(CNC) machine tool is the electrical system which controls and coordinates every part of CNC machine tool to complete processing tasks, so it is of great significance to strengthen the reliability of the electrical system. However, the electrical system is very complex due to many uncertain factors and dynamic stochastic characteristics when failure occurs. Therefore, the traditional fault tree analysis(FTA) method is not applicable. Bayesian network(BN) not only has a unique advantage to analyze nodes with multiply states in reliability analysis for complex systems, but also can solve the state explosion problem properly caused by Markov model when dealing with dynamic fault tree(DFT). In addition, the forward causal reasoning of BN can get the conditional probability distribution of the system under considering the uncertainty;the backward diagnosis reasoning of BN can recognize the weak links in system, so it is valuable for improving the system reliability.

CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

The Innovation of CNC Machine Interface Based on DNC System

This paper describes the innovation schemes of the interface of a CNC machine which cannot communicate with a computer by a Direct Numerical Control(DNC)interface and the functions of a DNC interface system.One architecture of hardware and software of a practi- cal single-chip computer based on DNC interface system developed by the authors is given. Without any change of the original hardware and software,this DNC interface system has been used to innovate the CNC machine's interface to implement the direct communication between a computer and a CNC machine and to achieve no tape transmission of a part program and ma- chine parameters.It has been demonstrated that this DNC interface system has certain practical values in improving the reliability,efficiency and production management of CNC/NC machine.

Design and Fabrication of a Three Dimensional Commemorative Artistic Plaque

In this paper, design and fabrication of a commemorative plaque are described and presented. The plaque was fabricated to honour the memory of the 14 women massacred at L＇Ecole Polytechnique in Montreal. This plaque is the result of a project partnership between the Faculties of Engineering and Fine Arts, and was sponsored by the Office of the Vice-President Academic and Provost. An art design was selected through a contest coordinated by the Visual Arts Departmment. The selected art design was then turned over to the Mechanical Engineering Department to be converted to a 3-dimensional （3D） solid model and then eventually fabricated on a computer numerical control （CNC） milling machine. The fabricated plaque was unveiled during the December 2010 Memorial event at UVic.

Investigation of Cutting Force by End Milling Operation

In this work, the cutting forces by end milling operation are analyzed. Therefore, the main parameters of cutting force as cutting speed, feed rate and depth of cut also are investigated in our case. The cutting force is modelled and analyzed into mathematical Wolfram simulations in order to compare the results and in the same time achieve the best solutions. Theoretical results are carried out by using the regression method that required fulfilling the critter by Fisher. The number of experiment, measurements and results of cutting force are presented in 2D as well as 3D. In order to verify the accuracy of the 2D diagram, the results for our case is used both two way such as experimental and theoretical method as well as results are compared. In other hands, these results indicate directly that the optimized parameters are capable of machining the workpiece. The obtained measurement results are compared with theoretical methods in Wolfram software.

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry.Among all errors,thermal error affects the machining accuracy considerably.Because of the significant impact of Industry 4.0 on machine tools,existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data.A thermal error modeling method is proposed based on bidirectional long short-term memory(BiLSTM)deep learning,which has good learning ability and a strong capability to handle a large group of dynamic data.A four-layer model framework that includes BiLSTM,a feedforward neural network,and the max pooling is constructed.An elaborately designed algorithm is proposed for better and faster model training.The window length of the input sequence is selected based on the phase space reconstruction of the time series.The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting.The average depth variation of the workpiece was reduced from approximately 50μm to less than 2μm after compensation.The reduction in maximum depth variation was more than 85%.The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

Prediction of product roughness,profile,and roundness using machine learning techniques for a hard turning process

High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control(CNC)machining.Quality monitoring and prediction is of great importance to assure high-quality or zero defect production.In this work,we consider roughness parameter Ra,profile deviation Pt and roundness deviation RONt of the machined products by a lathe.Intrinsically,these three parameters are much related to the machine spindle parameters of preload,temperature,and rotations per minute(RPMs),while in this paper,spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters.Power spectral density(PSD)based feature extraction,the method to generate compact and well-correlated features,is proposed in details in this paper.Using the efficient features,neural network based machine learning technique turns out to be able to result in high prediction accuracy with R2 score of 0.92 for roughness,0.86 for profile,and 0.95 for roundness.

Improved time-optimal B-spline feedrate scheduling for NURBS tool paths in CNC machining

Feedrate scheduling in computer numerical control(CNC)machining is of great importance to fully develop the capabilities of machine tools while maintaining the motion stability of each actuator.Smooth and time-optimal feedrate scheduling plays a critical role in improving the machining efficiency and precision of complex surfaces considering the irregular curvature characteristics of tool paths and the limited drive capacities of machine tools.This study develops a general feedrate scheduling method for non-uniform rational B-splines(NURBS)tool paths in CNC machining aiming at minimizing the total machining time without sacrificing the smoothness of feed motion.The feedrate profile is represented by a B-spline curve to flexibly adapt to the frequent acceleration and deceleration requirements of machining along complex tool paths.The time-optimal B-spline feedrate is produced by continuously increasing the control points sequentially from zero positions in the bidirectional scanning and sampling processes.The required number of knots for the time-optimal B-spline feedrate can be determined using a progressive knot insertion method.To improve the computational efficiency,the B-spline feedrate profile is divided into a series of independent segments and the computation in each segment can be performed concurrently.The proposed feedrate scheduling method is capable of dealing with not only the geometry constraints but also high-order drive constraints for any complex tool path with little computational overhead.Simulations and machining experiments are conducted to verify the effectiveness and superiorities of the proposed method.

Binary Decision Diagram Quantitative Analysis Method Based on Fuzzy Set Theory

A binary decision diagram(BDD) is a data structure that is used to represent a Boolean function.Converting fault tree into BDD can effectively simplify counting processes and improve the accuracy and effectiveness of the results. However, due to various types of uncertainties in reliability data, we cannot obtain precise failure probabilities. In order to accurately quantify the certainties and obtain much more reliable results, we use BDD method based on fuzzy set theory for reliability quantitative analysis. In this regard, we take W-axis feeding system of heavy-duty computer numerical control(CNC) machine as a project example and adopt fuzzy BDD quantitative analysis method to analyze its reliability. The analysis results(aided by computer calculation)illustrate the effectiveness of the method proposed in this paper.