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.

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.

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.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

Identification of Kinematic Errors of Five-axis Machine Tool Trunnion Axis from Finished Test Piece

Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.

Application Research on Powder Mixed EDM in Rough Machining

Powder Mixed Electric Discharge Machining (PMEDM) has different mechanism from conventional EDM, which can improve the surface roughness and surface quality distinctly and to obtain nearly mirror surface effects. It is a useful finish machining method and is researched and applied by many countries. However there are little research on rough machining of PMEDM. Experiments show that PMEDM machining makes discharge breakdown easier, enlarges the discharge gaps and widens discharge passage, and at last forms even distributed and "large and shadow" shaped etched cavities. Because of much loss of discharge energy in the discharge gaps and reduction of ejecting force on the melted material, the machining efficiency gets lower and the surface roughness gets small in PMEDM machining in comparison with conventional EDM machining. This paper performs experimental research on the machining efficiency and surface roughness of PMEDM in rough machining. The machining efficiency of PMEDM can be highly increased by selecting proper discharge parameters (increasing peak current, reducing pulse width) with approximate surface roughness in comparison with conventional EDM machining. Although PMEDM can improve machining efficiency in rough efficiency, but a series of problems like electrode wear, efficiently separation of machined scraps from the powder mixed working fluid, should be solved before PMEDM machining is really applied in rough machining. Experiments result shows that powder mixed EDM machining can obviously improve machining efficiency at the same surface roughness by selecting proper discharging parameters, and can provide reference accordingly for the application of PMEDM machining technology in rough machining.

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.

A Generic Kinematic Model for Three Main Types of Five-axis Machine Tools

Material removal is one of the most used processes in manufacturing. Five-axis CNC machines are believed to be the best tools in sculptured surface machining. In this study, a generic and unified kinematic model was developed as a viable alternative to the particular solutions that are only applicable to individual machine configurations. This versatile model is then used to verify the feasibility of the two rotational joints within the kinematic chain of three main types of a five-axis machine-tool. This versatile model is very useful applied to the design of five-axis machine tools.

High accuracy NURBS interpolation for five-axis machine of table-rotating/spindle-tilting type

In this paper, the definition of NURBS curve and a speed-controlled interpolation in which the feed rate is automatically adjusted in order to meet the specified chord error limit were discussed. Besides those, a definition of linear interpolation error of post-processed data was proposed, which should be paid more attention to because it will not only reduce quality of the surface but also may cause interference and other unexpected trouble. In order to control the error, a robust algorithm was proposed, which successfully met a desired error limit through interpolating some essential CL data. The excellence of the proposed algorithm, in terms of its reliability and self-adaptiveness, has been proved by simulation results.

Analysis of the Machining Characteristics on Surface Roughness of a Hybrid Aluminium Metal Matrix Composite (Al6061-SiC-Al₂O₃)

Aluminium metal matrix composites are finding increased applications in many areas. Adding of the third element to the metal matrix make the composite hybrid. This paper presents the study on the surface roughness characteristics of a hybrid aluminium metal matrix (Al6061-SiC-Al2O3) composites. The experimental studies were carried out on a lathe. The composites were prepared using the liquid metallurgy technique, in which 3, 6 and 9 wt % of particulates SiC and Al2O3 were dispersed in the base matrix. The obtained cast composites were carefully machined. The characteristics that influence the surface roughness such as feed rate, depth of cut and cutting speed were studied, which made the analysis come to a conclusion that the surface roughness is increases with the increase of feed rate and it reduces the surface roughness with the increase of cutting speed.

RESEARCH ON TECHNOLOGY AND SYSTEM FOR ON－LINE MEASUREMENT OF SURFACE ROUGHNESS IN ULTRA＝PRECISION MACHINING

ＲＥＳＥＡＲＣＨＯＮＴＥＣＨＮＯＬＯＧＹＡＮＤＳＹＳＴＥＭＦＯＲＯＮ－ＬＩＮＥＭＥＡＳＵＲＥＭＥＮＴＯＦＳＵＲＦＡＣＥＲＯＵＧＨＮＥＳＳＩＮＵＬＴＲＡ－ＰＲＥＣＩＳＩＯＮＭＡＣＨＩＮＩＮＧＬｕＨａｉｂａｏ；ＷａｎｇＹａｏｋｅ；ＺｈｕＪｉｇｕｉ；Ｚｈｏ...

Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM’training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.

Dissolution Characteristics of New Titanium Alloys in Electrochemical Machining

We focus on the electrochemical dissolution characteristics of new titanium alloys such as near-αtitanium alloy Ti60,α+βtitanium alloy TC4andβtitanium alloy Ti40 which are often used for aerospace industry.The experiments are carried out by electrochemical machining tool,and the surface morphology of the specimens is observed by the scanning electron microscope(SEM)and three-dimensional video microscope(DVM).The appropriate electrolyte is selected and the relationships between surface roughness and current density are achieved.The results show that the single-phase titanium alloy Ti40 has a better surface roughness after ECM compared with theα+βtitanium alloy TC4 and the near-αtitanium alloy Ti60.The best surface roughness is Ra 0.28μm when the current density is 75A/cm2.Furthermore,the surface roughness of the near-αtitanium alloy Ti60 is the most sensitive with the current density because of the different electrochemical equivalents of substitutional elements and larger grains than TC4.Finally,the suitable current density for each titanium alloy is achieved.

Machining performance on hybrid process of abrasive jet machining and electrical discharge machining

To develop a hybrid process of abrasive jet machining (AJM) and electrical discharge machining (EDM),the effects of the hybrid process parameters on machining performance were comprehensively investigated to confirm the benefits of this hybrid process.The appropriate abrasives delivered by high speed gas media were incorporated with an EDM in gas system to construct the hybrid process of AJM and EDM,and then the high speed abrasives could impinge on the machined surface to remove the recast layer caused by EDM process to increase the efficiency of material removal and reduce the surface roughness.In this study,the benefits of the hybrid process were determined as the machining performance of hybrid process was compared with that of the EDM in gas system.The main process parameters were varied to explore their effects on material removal rate,surface roughness and surface integrities.The experimental results show that the hybrid process of AJM and EDM can enhance the machining efficiency and improve the surface quality.Consequently,the developed hybrid process can fit the requirements of modern manufacturing applications.

Approach for Polishing Diamond Coated Complicated Cutting Tool: Abrasive Flow Machining(AFM)

Lower surface roughness and sharper cutting edge are beneficial for improving the machining quality of the cut?ting tool, while coatings often deteriorate them. Focusing on the diamond coated WC?Co milling cutter, the abrasive flow machining(AFM) is selected for reducing the surface roughness and sharpening the cutting edge. Comparative cutting tests are conducted on di erent types of coated cutters before and after AFM, as well as uncoated WC?Co one, demonstrating that the boron?doped microcrystalline and undoped fine?grained composite diamond coated cutter after the AFM(AFM?BDM?UFGCD) is a good choice for the finish milling of the 6063 Al alloy in the present case, because it shows favorable machining quality close to the uncoated one, but much prolonged tool lifetime. Besides, compared with the micro?sized diamond films, it is much more convenient and e cient to finish the BDM?UFGCD coated cutter covered by nano?sized diamond grains, and resharpen its cutting edge by the AFM, owing to the lower initial surface roughness and hardness. Moreover, the boron incorporation and micro?sized grains in the underly?ing layer can enhance the film?substrate adhesion, avoid the rapid film removal in the machining process, and thus maximize the tool life(1040 m, four times more than the uncoated one). In general, the AFM is firstly proposed and discussed for post?processing the diamond coated complicated cutting tools, which is proved to be feasible for improving the cutting performance

Tool Positioning Algorithm Based on Smooth Tool Paths for 5-axis Machining of Sculptured Surfaces

The current research of the 5-axis tool positioning algorithm mainly focuses on searching the local optimal tool position without gouging and interference at a cutter contact（CC） point,while not considering the smoothness and continuity of a whole tool path.When the surface curvature varies significantly,a local abrupt change of tool paths will happen.The abrupt change has a great influence on surface machining quality.In order to keep generated tool paths smooth and continuous,a five-axis tool positioning algorithm based on smooth tool paths is presented.Firstly,the inclination angle,the tilt angle and offset distance of the tool at a CC point are used as design variables,and the machining strip width is used as an objective function,an optimization model of a local tool positioning algorithm is thus established.Then,a vector equation of tool path is derived by using the above optimization model.By analyzing the equation,the main factors affecting the tool path quality are obtained.Finally,a new tool position optimization model is established,and the detailed process of tool position optimization is also given.An experiment is conducted to machine an aircraft turbine blade by using the proposed algorithm on a 5-axis blade grinding machine,and the machined blade surface is measured with a coordinate measuring machine（CMM）.Experimental and measured results show that the proposed algorithm can ensure tool paths are smooth and continuous,improve the tool path quality,avoid the local abrupt change of tool paths,and enhance machining quality and machining efficiency of sculptured surfaces.

基于iMachining和PM软件的智能高效粗加工方法研究

针对传统数控铣削加工效率低、加工参数主要依靠经验以及机床和刀具加工潜力发挥不足的弊端,利用SolidCAM软件提供的iMachining加工策略,完成数控铣削粗加工的自动编程。同时,针对iMachining在刀具轨迹自动生成时某些部位切削力突变的情况,利用Production Module软件提供的刀具轨迹分析功能,对刀具承受切削力突变的部位进行优化处理。通过以上方式进行数控铣削粗加工编程,有效提高了数控铣削加工效率,充分发挥了机床和刀具的潜力,提高了刀具的使用寿命和机床切削的稳定性。

Uniform surface polished method of complex holes in abrasive flow machining

Abrasive flow machining(AFM) is an effective method that can remove the recasting layer produced by wire electrical discharge machining(WEDM). However, the surface roughness will not be easily uniform when a complex hole is polished by this method. CFD numerical method is aided to design good passageways to find the smooth roughness on the complex hole in AFM. Through the present method, it reveals that the shear forces in the polishing process and the flow properties of the medium in AFM play the roles in controlling the roughness on the entire surface. A power law model was firstly set up by utilizing the effect of shear rates on the medium viscosities, and the coefficients of the power law would be found by solving the algebraic equation from the relations between the shear rates and viscosities. Then the velocities, strain rates and shear forces of the medium acting on the surface would be obtained in the constant pressure by CFD software. Finally, the optimal mold core put into the complex hole could be designed after these simulations. The results show that the shear forces and strain rates change sharply on the entire surface if no mold core is inserted into the complex hole, whereas they hardly make any difference when the core shape is similar to the complex hole. Three experimental types of mold core were used. The results demonstrate that the similar shape of the mold core inserted into the hole could find the uniform roughness on the surface.

Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

Combined method of plastic work piece machining based on a pretreatment mechanical down

An analysis of polymer materials behavior under cutting forces load was presented.The analysis was accomplished taking into account the existence and interaction of micro cracks in material.On the basis of modeling representations of a polymeric material behavior at cutting the method of preliminary mechanical destruction a superficial layer of polymeric material was developed.The essence of the method consists in producing micro-cracks in form of blind holes on the upper layer of blanks before turning.The aim of this method is a plastic deformation zones creation under stresses interaction occurring at the adjacent crack apexes.Results of experimental researches of fabric-based laminate turning processing according to the offered method were submitted.The analysis of the received results confirms expediency of application of the given combined method and the decrease of a roughness on the processed surface of fabric-based laminate is testifying about it.