Condition monitoring of CNC machining using adaptive control

In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.     

Design of an optimal damper to minimize the vibration of machine tool structures subject to random excitation

The vibration of machine tools during machining adversely affects machining accuracy and tool life, and therefore must be minimized. The cutting forces for stable turning are generally known to be random, and hence excite all the resonance modes. Of all these modes, those that generate relative motions between a cutting tool and a workpiece are of concern.This paper presents a new approach for designing an optimal damper to minimize the relative vibration between the cutting tool and workpiece during stable machining. An approximate normal mode method is employed to calculate the response of a machine tool system with nonproportional damping subject to random excitation. The major advantage of this method is that it reduces the amount of computation greatly for higher-order systems when responses have to be calculated repeatedly in the process of optimization. An optimal design procedure is presented based on a representative lumped parameter model that can be constructed by using existing experimental or analytical techniques. The two-step optimization procedure based on the modified pattern search and univariate search effectively leads the numerical solution to the global minimun irrespectively of initial values even under the existence of many local minima.     

Active regenerative chatter suppression during boring manufacturing process

Machining performance such as that of the boring process is often limited by chatter vibration at the tool–workpiece interface. Among various sources of chatter, regenerative chatter in cutting systems is found to be the most detrimental. It limits cutting depth (as a result, productivity), adversely affects surface finish and causes premature tool failure. Though the machining system is a distributed system, all current active controllers have been designed based upon a simplified lumped single degree of freedom cutting process model. This is because it was found that in the majority of cutting processes, there exists only one dominating mode. However, such simplification does have some potential problems. First, since the system itself is a distributed system, theoretically it consists of infinite number of vibration modes. When the controller is designed to control the dominating mode(s) only, the energy designed to suppress the particular mode(s) may excite the rest of the structural modes, which unavoidably causes the so-called spillover problem. Second, the success of the control design of such simplified single degree of freedom system relies on the availability of accurate model parameters (such as the effective mass, stiffness and damping), which is unfortunately very hard to acquire. This is because the global properties are varying with the metal removal process and the movable components of machine tool. In this paper, an active controller designed from wave point of view is used to absorb chatter vibration energy in a broad frequency band to improve machining performance of a non-rotating boring bar. In contrast to most of the current active chatter control design, the wave controller is designed based on the real distributed cutting system model. The main advantage of such a control scheme to chatter suppression is its robustness to model uncertainties. The control scheme also eliminates the control spillover problem.     

在微机上实现数控铣床加工仿真

给出了在微机上实现数控铣床加工仿真的两种有效途径 .视向离散方法的主要思路是将毛坯和刀具进行离散 ,通过对离散后的数据结构进行一定操作来达到加工仿真的目的 ;三角片离散方法可以在仿真过程中不断地变换观察方式 ,而且仿真的结果能直接用于多种误差测量     

Investigation and FEA-based simulation of tool wear geometry and metal oxide effect on cutting process variables

In machining, it is clearly noticed that the cutting tool wear influences the cutting process. However, it is difficult with experimental methods to study the effects of tool wear on several machining variables. Thus, in the literature, some earlier studies are performed separately on the effect of tool flank wear and crater wear on cutting process variables (such as cutting forces and temperature). Furthermore when the workpiece material adheres in cutting tool, it affects considerably the heat transfer phenomena. Accordingly, in this work the finite element analysis (FEA) is performed to investigate the influence of combination of tool flank and crater wear on the local or global variables such as cutting forces, tool temperature, chip formation on the one hand and the effects of the oxidized adhesion layer considered as oxide (Fe₂O₃/Fe₃O₄/FeO) on the heat transfer in cutting insert on the other hand. In this investigation, an uncoated cutting insert WC–6Co and medium carbon steel grade AISI 1045 are used. The factorial experimental design technique with three parameters (cutting speed Vc, flank wear land VB, crater wear depth KT) is used for the first investigation without adhesion layer. Then, only linear investigation is performed. The analysis has shown the influence of the different configurations of the tool wear geometry on the local or global cutting process variables, mainly on temperature and cutting. The simulation’s results show also, the highly influence of the oxidized adhesion layer (oxide Fe₂O₃/Fe₃O₄/FeO) on the heat transfer.     

线接触加工数控系统的研究

分析了线接触加工的加工方法。此数控系统是基于Windows的全软件数控系统,包括多个软件控制模块,通过控制铣刀的有效走刀姿态,利用铣刀侧刃进行一次走刀完成对整个可展直纹面的加工。此外该系统具有图形自动编程功能,保证了机床加工过程的自动化。     

数控加工仿真系统的研究与应用

介绍数控加工仿真系统的整体设计,提出格栅voxel三维实体建模方法,刀具扫描体的生成算法,实现了刀具切削工件过程的动态仿真,并对碰撞检查算法进行了初步的研究.基于以上方法,建立了蓝天数控系统的加工仿真系统,在加工前对加工程序进行验证,在加工时对刀具轨迹的执行、工件的切削过程等进行实时监控.     

刀具状态在线监测与智能识别方法研究

切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度.选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识.实验结果表明,该方法具有良好的识别效果和工程应用价值.     

Modeling of cutting geometry and forces for 5-axis sculptured surface machining

5-Axis sculptured surface machining is simulated using discrete geometric models of the tool and workpiece to determine the tool contact area, and a discrete mechanistic model to estimate the cutting forces. An extended Z-buffer model represents the workpiece, while a discrete axial slice model represents the cutting tool. Determination of the contact area for a given tool move requires a swept envelope (SWE) of the tool path. The SWE is used to find the intersections of the tool envelope with Z-buffer elements (ZDVs) representing the workpiece. A 3-axis approximation of the 5-axis tool movement is used to simplify the calculations while maintaining a desired level of accuracy. The intersection of the SWE with each ZDV yields segments which are used to find the contact area between the cutter and the workpiece for a given tool path. The contact area is subsequently used with the discrete force model to calculate the vector cutting force acting on the tool.     

Effect of workpiece springback on micromilling forces

The machining forces present in micromilling with tools in the 50–100 m diameter range are dominated by contact pressure and friction between the tool cutting edges and the workpiece. A model of the micromilling process was developed based on the elastic contact between the tool and the workpiece along the side and bottom cutting edges of the tool. Micromilling experiments were conducted on 6061-T6 aluminum to obtain machining forces in the feed and cross-feed directions during slot milling and partial engagement end milling. Comparisons with the experimental data indicate reasonable agreement for full slot milling as well as end milling with radial depths of cut in the range of 2 m to 40 m. It was concluded that this model is adequate for predicting micromilling forces with the precision needed to reduce tool breakage and workpiece clamping forces and for predicting tool deflection that affects wall slope and feature size.This work was supported primarily by the Engineering Research Centers Program of the National Science Foundation under Award Number EEC-9986866. The Engineering Research Center for Wireless Integrated Microsystems is also hereby acknowledged. All machining was performed at the Micromechanical Applications and Processes Laboratory at Michigan Technological University.     

Realization of STEP-NC enabled machining

A STEP-compliant CNC machine tool that demonstrated a G-code free machining scenario is presented. The aim of this research is to showcase the advantages of, and evaluate, STEP-NC—a new NC data model—by implementing it in a legacy CNC system. The work consists of two parts: retrofitting an existing CNC machine and the development of a STEP-compliant NC Converter called STEPcNC. The CompuCam's motion control system is used for retrofitting the machine, which is programmable using its own motion control language—6K Motion Control language and capable of interfacing with other CAPP/CAM programs through languages such as Visual Basic, Visual C++ and Delphi. STEPcNC can understand and process STEP-NC codes, and interface with the CNC controller through a Human Machine Interface. It makes use of STEP-NC information such as “Workplan”, “Workingstep”, machining strategy, machining features and cutting tools that is present in a STEP-NC file. Hence, the system is truly feature-based. The Application Interpreted Model of STEP-NC has been used.     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.     

An approach to enhancing the intelligence of a three-fingered automated flexible fixturing system by using adaptive control theory

A new approach to clamping workpieces for the machining process has been developed. The new approach is based on the identification of the workpiece stiffness, during the machining process, which enables the fixturing system to determine whether the clamping force should be changed. Using this approach, a three-fingered intelligent automated flexible fixturing system for planar objects has been successfully designed and implemented. The conventional and traditional clamping methods use a hydraulic vise to clamp workpieces with a fixed amount of clamping forces. This often causes the workpieces to deform under such force, thus contributing to machining error. It is therefore desirable to control the clamping force according to the progress of the machining process. The implementation of this approach to the three-fingered automated flexible fixturing system enhances the system’s ability to achieve intelligent clamping force control and deal with abnormal machining, thus making the system intelligent. A thin ring-shaped workpiece was successfully used as an example to demonstrate the feasibility and validity of this approach. It also demonstrates that the developed system can be used in the highly automated manufacturing system of the future.     

Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses

This paper presents the use of artificial neural networks (ANN) to diagnose degraded behaviours in wire electrical discharge machining (WEDM). The detection in advance of the degradation of the cutting process is crucial since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy. Concerning this, previous investigations have identified different types of degraded behaviours in two commonly used workpiece thicknesses (50 and 100 mm). This goal was achieved by monitoring different functions of characteristic discharge variables. However, the thresholds achieved by these functions depended on the thickness of the workpiece. Consequently, the main objective of this work is to detect the degradation of the process when machining workpiece of different thicknesses using one unique empirical model. Since artificial neural network techniques are appropriate for stochastic and non-linear nature processes, its use is investigated here to cope with workpieces of different thicknesses. The results of this work show a satisfactory performance of the presented approach. The satisfactory performance is shown by two ratios: the validation ratio, which ranges between 85% and 100%, and the test ratio, which results between 75% and 100%.     

Iterative learning control for integrated system of robot and machine tool

This study is concerned with the integrated system of a robot and a machine tool. The major task of robot is loading the workpiece to the machine tool for contour cutting. An iterative learning control (ILC) algorithm is proposed to improve the accuracy of the finished product. The proposed ILC is to modify the input command of the next machining cycle for both robot and machine tool to iteratively enhance the output accuracy of the robot and machine tool. The modified command is computed based on the current tracking/contour error. For the ILC of the robot, tracking error is considered as the control objective to reduce the tracking error of motion path, in particular, the error at the endpoint. Meanwhile, for the ILC of the machine tool, contour error is considered as the control objective to improve the contouring accuracy, which determines the quality of machining. In view of the complicated contour error model, the equivalent contour error instead of the actual contour error is taken as the control objective in this study. One challenge for the integrated system is that there exists an initial state error for the machine tool dynamics, violating the basic assumption of ILC. It will be shown in this study that the effects of initial state error can be significantly reduced by the ILC of the robot. The proposed ILC algorithm is verified experimentally on an integrated system of commercial robot and machine tool. The experimental results show that the proposed ILC can achieve more than 90% of reduction on both the RMS tracking error of the robot and the RMS contour error of the machine tool within six learning iterations. The results clearly validate the effectiveness of the proposed ILC for the integrated system.     

An expert system approach for die and mold making operations

In the modern manufacturing of sophisticated parts with 3D sculptured surfaces, die and mold making operations are the most widely used machining processes to remove unwanted material. To manufacture a die or a mold, many different cutting tools are involved, from deep hole drills to the smallest ball nose end mills. Since the specification of each tool is very different from each other, each mold or die is specific with their complicated shapes and many machining rules exist to consider, a great deal of expertise is needed in planning the machining operations. An expert system (DieEX) developed for this purpose is described in the present work. The geometry and the material of the workpiece, tool material, tool condition and operation type are considered as input values and various recommendations about the tool type, tool specifications, work holding method, type of milling operation, direction of feed and offset values are provided.     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.     

Genetic synthesis of a fuzzy pulse discriminator in electrical discharge machining

In this paper, a sensor system, called a fuzzy pulse discriminator, is developed to classify various discharge pulses in electrical discharge machining (EDM). The fuzzy rules of the pulse discriminator are obtained based on the features of the gap voltage and gap current between the tool workpiece. The membership functions of the fuzzy pulse discriminator are automatically synthesized by using genetic algorithms. The effectiveness of this approach is verified under different cutting parameters.