A New Dynamics Analysis Model for Five-Axis Machining of Curved Surface Based on Dimension Reduction and Mapping

The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces.

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency,so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status.Firstly,the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology,and the features related to the tool wear status are extracted in the time domain,frequency domain and time-frequency domain to form a feature sample matrix;secondly,the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types:slight wear,normal wear and sharp wear to construct a target Finally,the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status.The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%;and compared with BP model,CNN model and SVM model,the accuracy indexes are improved by 9.48%,3.44%and 1.72%respectively,which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

Fault monitoring and diagnosis of motorized spindle in five-axis Machining Center based on CNN-SVM-PSO

A spindle fault diagnosis method based on CNN-SVM optimized by particle swarm algorithm(PSO)is proposed to address the problems of high failure rate of electric spindles of high precision CNC machine tools,while manual fault diagnosis is a tedious task and low efficiency.The model uses a convolutional neural network(CNN)model as a deep feature miner and a support vector machine(SVM)as a fault state classifier.Taking the electric spindle of a five-axis machining centre as the experimental research object,the model classifies and predicts four labelled states:normal state of the electric spindle,loose state of the rotating shaft and coupling,eccentric state of the motor air gap and damaged state of the bearing and rolling body,while introducing a particle swarm algorithm(PSO)is introduced to optimize the hyperparameters in the model to improve the prediction effect.The results show that the proposed hybrid PSO-CNN-SVM model is able to monitor and diagnose the electric spindle failure of a 5-axis machining centre with an accuracy of 99.33%.In comparison with the BP model,SVM model,CNN model and CNN-SVM model,the accuracy of the model increased by 10%,6%,4%and 2%respectively,which shows that the fault diagnosis model proposed in the paper can monitor the operation status of the electric spindle more effectively and diagnose the type of electric spindle fault,so as to improve the maintenance strategy.

Continuity control method of cutter posture vector for efficient five-axis machining

During five-axis machining of impeller, the excessive local interference avoidance leads to inconsistency of cutter posture, low quality of machined surface and increase of processing time. Therefore, in order to improve the efficiency of five-axis machining of impellers, it is necessary to minimize the cutter posture changes and create a continuous tool path while avoiding interference. By using an MC-space algorithm for interference avoidance, an MB-spline algorithm for continuous control was intended to create a five-axis machining tool path with excellent surface quality and economic feasibility. A five-axis cutting experiment was performed to verify the effectiveness of the continuity control. The result shows that the surface shape with continuous method is greatly improved, and the surface roughness is generally favorable. Consequently, the effectiveness of the suggested method is verified by identifying the improvement of efficiency of five-axis machining of an impeller in aspects of surface quality and machining time.

A Web-based machining process monitoring system for E-manufacturing implementation

Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments.

Improved Quality Prediction Model for Multistage Machining Process Based on Geometric Constraint Equation

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process（MMP）. However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

Adaptive control of machining process based on extended entropy square error and wavelet neural network

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

The General Machining Process Simulator GMPS

Automated manufacturing system is characterized by flexibility. It aims at producing a variety of products with virtually no time loses to change over from one part to the next. In this paper, the Machining Process Simulator GMPS is introduced, which can be used as a supported environment for machining process. It can be executed off-line or on-line in manufacturing systems in order to predict the collisions of tool with machined workpieces, fixtures or pallets. First, the functional model of GMPS is described, then adopted critical techniques in the simulator are introduced. Finally, an application of GMPS in CIMS ERC of China is presented.

Machining Process Classification Based on Carbon Footprint Analysis

Despite spending considerable effort on the development of manufacturing technology during the production process,manufacturing companies experience resources waste and worse ecological influences. To overcome the inconsistencies between energy-saving and environmental conservation,a uniform way of reporting the information and classification was presented. Based on the establishment of carbon footprint( CFP) for machine tools operation,carbon footprint per kilogram( CFK) was proposed as the normalized index to evaluate the machining process.Furthermore,a classification approach was developed as a tracking and analyzing system for the machining process. In addition,a case study was also used to illustrate the validity of the methodology. The results show that the approach is reasonable and feasible for machining process evaluation,which provides a reliable reference to the optimization measures for low carbon manufacturing.

Framework of Digital Machining Process Planning Platform for Cylinder Body Part

Digital factory technology is an advanced manufacturing technology served as to establish a bridge between the process of product development and manufacturing.In terms of application for digital factory technology in machining,especially in machining of a complicated part such as a cylinder body part,a concept of digital process planning and its framework are proposed.Its components including machining domain knowledge model,machining knowledge base,machining resource base and process planning system are studied.A machining knowledge model in tree form and an object-driven knowledge reasoning mechanism are used for machining knowledge base.The process planning system is a user interface that leads a planner to finish the planning process.A case about a cylinder head part is given to demonstrate how the platform works.The framework of digital process planning is the foundation of some intelligent CAPP systems and helps to production line planning.

Study on Simulation of 5-axis NC Machining Process

Ray-casting technique used to generate realism graphs is creatively applied to simulate the NC machining process of an integral turbo-wheel, thus the representation of a workpiece is redused from 3D to 1D. As a result, simulation speed is raised greatly and the visualization is kept. The relative problems are the discussed in detail and the 5 - axis NC machining process simulation of integral turbo-wheel is illustrated with Ray-casting representation.

PLANNING METHOD OF TOOL ORIENTATION IN FIVE-AXIS NC MACHINING

The planning method of tool orientation in the five-axis NC machining is studied. The problem of the existing method is analyzed and a new method for generating the global smoothing tool orientation is proposed by introducing the key frame idea in the animation-making. According to the feature of the part, several key tool orientations are set without interference between the tool and the part. Then, these key tool orientations are inter- polated by the spline function. By mapping the surface parameter to the spline parameter, the spline function value is obtained and taken as the tool orientation when generating the CL file. The machining result shows that the proposed method realizes the global smoothing of the tool orientation and the continuity of the rotational speed and the rotational acceleration. It also avoids the shake of the machine tool and improves the machining quality.

Jerk-limited feedrate scheduling and optimization for five-axis machining using new piecewise linear programming approach

In this paper, a new computation method and an optimization algorithm are presented for feedrate scheduling of five-axis machining in compliance with both machine drive limits and process limits. Five-axis machine tool with its ability of controlling tool orientation to follow the sculptured surface contour has been widely used in modern manufacturing industry. Feedrate scheduling serving as a kernel of CNC control system plays a critical role to ensure the required machining accuracy and reliability for five-axis machining. Due to the nonlinear coupling effects of all involved drive axes and the saturation limit of servo motors, the feedrate scheduling for multi-axis machining has long been recognized and remains as a critical challenge for achieving five-axis machine tools’ full capacity and advantage. To solve the nonlinearity nature of the five-axis feedrate scheduling problems, a relaxation mathematical process is presented for relaxing both the drive motors’ physical limitations and the kinematic constraints of five-axis tool motions. Based on the primary optimization variable of feedrate, the presented method analytically linearizes the machining-related constraints, in terms of the machines’ axis velocities, axis accelerations and axis jerks. The nonlinear multi-constrained feedrate scheduling problem is transformed into a manageable linear programming problem. An optimization algorithm is presented to find the optimal feedrate scheduling solution for the five-axis machining problems. Both computer implementation and laboratorial experiment testing by actual machine cutting were conducted and presented in this paper. The experiment results demonstrate that the proposed method can effectively generate efficient feedrate scheduling for five-axis machining with constraints of the machine tool physical constraints and limits. Compared with other existing numerical methods, the proposed method is able to find an accurate analytical solution for the nonlinear constrained five-axis feedrate scheduling problems without compromising the efficiency of the machining processes.

Efficiently constructing collision-free regions of tool orientations for holder in five-axis machining of blisk

Blisks with the integral structure are key parts used in new jet engines to promote the performance of aircrafts,which also increases the complexity of tool orientation planning in the five-axis machining.It is an essential task to find the collision-free tool orientation when the tool holder is pushed deep into the channel of blisk to increase rigidity and reduce vibration.Since the radius of the holder varies with the height,the line-visibility is no longer applicable when constructing collision-free regions of tool orientation.In this paper,a method of constructing collisionfree regions without interference checking is proposed.The work of finding collision-free regions resorts to solving the local contact curves on the checking surfaces of blisk.And it further transforms into searching the locally tangent points(named critical points)between the holder and surface.Then a tracking-based algorithm is proposed to search the sample critical points on these local contact curves.And the corresponding critical vectors are also calculated synchronously.Besides,the safety allowance,discrete precision and acceptable deviation are introduced in the algorithm to ensure accuracy by controlling the angle between two adjacent critical vectors properly.After that,the searched critical vectors are mapped orderly to two-dimensional space and the collisionfree regions are constructed.This method is finally verified and compared with a referenced method.The results show that the proposed method can efficiently construct collision-free regions for holder under the given accuracy.

Machining fixture for adaptive CNC machining process of near-net-shaped jet engine blade

Low stiffness and positioning problems are difficulties and challenges in the precise machining of near-net-shaped blades.This paper aims to achieve high accuracy in manufacturing by fixture-and deformation-control in the adaptive CNC machining process.Adaptive CNC machining technology is first analyzed,and new fixture-evaluation criteria and methods to evaluate the adaptive CNC machining process fixture design are built.Second,a machining fixture is designed and manufactured after analyzing its positioning scheme,clamping scheme,materials(PEEK-GF30),and structure characteristics.Finally,the designed fixture is analyzed by FEA and experimentally verified by a cutting experiment.The results show that the deformation of the blade is an overall rigid-body displacement,the main deformation of the blade-fixture system occurs on the four clamping heads,and this fixture can effectively protect the blade from local deformation.The proposed clamping-sequence method reliably and effectively controls the local maximum deformation of the blade.The system stiffness is increased by 20 Hz,with each clamping force increased by 200 N.Both high-and low-frequency displacement in roughing milling or finishing milling are acceptable relative to the accuracy demand of blade machining.This fixture and an adaptive CNC machining process can achieve high accuracy in blade manufacturing.

Three-dimensional modeling and reconstructive change of residual stress during machining process of milling, polishing, heat treatment, vibratory finishing, and shot peening of fan blade

Residual stress during the machining process has always been a research hotspot,especially for aero-engine blades.The three-dimensional modeling and reconstructive laws of residual stress among various processes in the machining process of the fan blade is studied in this paper.The fan blades of Ti-6Al-4V are targeted for milling,polishing,heat treatment,vibratory finishing,and shot peening.The surface and subsurface residual stress after each process is measured by the X-ray diffraction method.The distribution of the surface and subsurface residual stress is analyzed.The Rational Taylor surface function and cosine decay function are used to fit the characteristic function of the residual stress distribution,and the empirical formula with high fitting accuracy is obtained.The value and distribution of surface and subsurface residual stress vary greatly due to different processing techniques.The reconstructive change of the surface and subsurface residual stress of the blade in each process intuitively shows the change of the residual stress between the processes,which has a high reference significance for the research on the residual stress of the blade processing and the optimization of the entire blade process.

Machining Process Simulator for Concurrent Engineering

Integration is a key component of CIMS and concurrent engineering is an important step in realizing CIMSprocess integration. Concurrent engineering is itself dependent on an effective machining process simulator that verifies the machining process. A machining process simulator for concurrent engineering CMPS was developed at CIMSERC to meet the need for an effective simulator. This paper introduces the CMPS structure. key techniques. including geometry model construction and kinematics. NC code translation. collision and interference checks, material removal simulation. machining animation. etc. The model uses the solid Ray-representation method and the Voxelsplus B-representation algorithm. The Ray-representation method simplifies the Boolean operation process and improves the material removal simulation speed. The Voxels plus B-representation algorithm quickly detects collisionand interference problems. Finally. CMPS is applied to an actual NC milling process as an example.

Multiconditional machining process quality prediction using deep transfer learning network

The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.

Experimental Research on Effects of Process Parameters on Servo Scanning 3D Micro Electrical Discharge Machining

Servo scanning 3D micro electrical discharge machining (3D SSMEDM) is a novel and effective method in fabricating complex 3D micro structures with high aspect ratio on conducting materials. In 3D SSMEDM process, the axial wear of tool electrode can be compensated automatically by servo-keeping discharge gap, instead of the traditional methods that depend on experiential models or intermittent compensation. However, the effects of process parameters on 3D SSMEDM have not been reported up until now. In this study, the emphasis is laid on the effects of pulse duration, peak current, machining polarity, track style, track overlap, and scanning velocity on the 3D SSMEDM performances of machining efficiency, processing status, and surface accuracy. A series of experiments were carried out by machining a micro-rectangle cavity (900 μm×600 μm) on doped silicon. The experimental results were obtained as follows. Peak current plays a main role in machining efficiency and surface accuracy. Pulse duration affects obviously the stability of discharge state. The material removal rate of cathode processing is about 3/5 of that of anode processing. Compared with direction-parallel path, contour-parallel path is better in counteracting the lateral wear of tool electrode end. Scanning velocity should be selected moderately to avoid electric arc and short. Track overlap should be slightly less than the radius of tool electrode. In addition, a typical 3D micro structure of eye shape was machined based on the optimized process parameters. These results are beneficial to improve machining stability, accuracy, and efficiency in 3D SSMEDM.

An e-quality control model for multistage machining processes of workpieces

To track and control the changes of process quality attributes in multistage machining processes(MMPs),an e-quality control(e-QC) model is proposed.The e-QC model is defined as a quality information service node with e-formalizing technology,whose input/output and intermediate process(that is IPO) are known to other nodes,and its implemention in MMPs is provided.In order to establish the e-QC model,a measuring network is constructed to acquire the original quality data,and the changes of process quality attributes are monitored and diagnosed by the integrated quality analysis tools attached to the e-QC,which can be tracked by information template network in real time.Furthermore,a hierarchical control method is adopted to coordinate e-QCs,in which the quality loss and adjusting cost are used to quantify the opportunities for e-QCs to improve process quality.At last,a prototype is developed to verify the proposed methods.