Control of linear motor machine tool feed drives for end milling: Robust MIMO approach

Linear motors are getting promising for use as high speed, high accuracy machine tool feed drives. The cutting force in the machining process are directly reflected to the linear motor due to no gearing mechanism. To achieve high accuracy machining, the controller for the linear motor system should be designed to compensate for the cutting force.

In this work, a MIMO H_∞ controller for the linear motor machine tool feed drives has been designed to reduce tracking errors induced by cutting forces for end milling. The controller is designed using normalized coprime factorization method for the dynamic model of the linear motor system. The model includes constant in-line and cross coupling force gain, since the feedback cutting force can be considered as the product of the constant gain and the moving velocity of each axis.

Analysis of the structured singular value shows that the designed controller has good robust performance despite wide variations of the cutting force and physical parameters. It is directly implemented on a linear motor X–Y table which is mounted on a milling machine to have cutting experiments via a DSP board. Experimental results verified effectiveness of the proposed scheme to suppress the effects of the cutting force in the high feed rate.     

Recording of real cutting forces along the milling of complex parts

In this paper, a data acquisition system that simultaneously allows the recording of cutting forces and cutting tool position (coordinates X, Y and Z) is presented. Thus, the geometry of the surface being machined and the values of the cutting forces generated during the surface milling can be correlated. In this manner, two usual problems in milling experimentation are solved. First, those derive from the continuous changes in the feed rate value due to the special look ahead functions (being on-line applied by the numerical control in all high-speed milling centres), which affects the correlation between part geometry and forces. Second, those originate by the presence of unexpected stocks coming from previous semi-finishing operations, which changes the value of cutting forces respect to those calculated taking into account the theoretical tool engagement into the part.The objective of this work is the development of a diagnostics tool for allowing the detection of potential milling problems by researchers, making more profitable the performance of machining test on real complex parts. This tool can be used in the optimization of the milling of test-parts, machining real geometries that incorporate problems different from those observed in the linear tests at constant linear feeds (in end-milling conditions).The final goal of the system is the generation of cutting forces maps as a function of the geometry of real workpieces, which is an advance respect the usual forces recording in function of time or tool rotation typical of linear machining tests. In order to do that, the system incorporates a dynamometric plate for the measurement of the three components of the cutting force, as well as an acquisition card connected to the analog output of the position control loops. After machining, the file containing position and force data is post-processed and chromatic and vector maps are generated.The force analysis utility has been applied to three cases, in order to assess its feasibility in machining research projects. The first example shows a reduction of the number of tests and therefore machining time (even by 18 times) in the validation of a mechanistic cutting force model. Second example is focused on the detection of unexpected tool engagement conditions in complex parts. And the last one addresses to the milling of thin walls, for investigation of static and dynamic milling problems.     

Adaptive learning control of milling operations

An adaptive learning control of milling operations in order to improve productivity is presented. Basically, the proposed control system consists of two parts. A feedforward neural network is first developed to acquire the inverse-dynamics model of the controlled plant. Then, a fuzzy feedback mechanism is designed to perform an adaptive modification of connection weights for the feedforward neural network. Based on this control system, an on-line adjustment of feedrate to achieve a constant milling force under a variety of cutting conditions is shown.     

X62W铣床PLC系统改造

X62W77能铣床是一种高效率的加工机械,在机械加工和机械修理中得到广泛的应用．万能铣床的操作是通过机械手柄N时操作电气与机械,已达到机电紧密配合完成预定的操作．是机械与电气结构联合动作的典型控制,是自动化程度较高的组合机床。继电器接触式控制系统,由于电气控制线路触点多、线路复杂、故障率高、检修周期长,给生产与维护带来诸多不便。将X62W万能铣床电气控制线路改造为可编程控制器控制,既可以提高整个电气控制系统的工作性能．又减少维护、维修的工作量。PLC控制系统无论是硬件还是软件,控制稳定可靠,具有极高的可靠性与灵活性。     

Fuzzy Logic-Based Torque Control System for Milling Process Optimization

This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open-architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC's own resources without requiring additional hardware overheads. The control system consists of two inputs (i.e., torque error and change of error), two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, and a self-tuning mechanism, all of which are embedded within the kernel of a standard open control. The self-tuning strategy is based on the measured peaks in the torque error signal of the closed-loop system response. The self-tuning fuzzy controller is applied to the milling process in a production environment in order to demonstrate the improvements in performance and effectiveness. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are the two-input/two-output for the fuzzy controller and a single-output fuzzy controller (i.e., only feed-rate modification), with and without the self-tuning mechanism. The results demonstrate that the proposed control strategy provides better transient performance, accuracy, and machining cycle time than the others, thus, increasing the metal removal rate.     

Cutting force control of milling machine

In metal cutting processes, an effective control system, which depends on a suitably developed scheme or set of algorithms can maintain machine tools in good condition. In this paper, an approach is developed for cutting force control of CNC machine. Several linear models are identified based on different working conditions. A dominant model plus uncertain terms is derived from these model set, to yield the necessary and key information from the system. Subsequently, it is used as a state estimator, and robust control is carried out by using the observed variables and cutting force. The developed approach is applied to a milling machine center. Examples taken from experimental tests shown that the developed approach is effective for the uncertain CNC machine.     

Detection of tool deflection in milling by a sensory axis slide for machine tools

Previous publications regarding the project Gentelligent Components for Machine Tools of the Collaborative Research Center 653 presented the design of a new sensory z-slide for a 5-axis machining center. Equipped with several strain sensors, the new slide is able to feel the machining process by measuring the process forces and vibrations. Here, a challenge is the detection of mechanical strains in the slide without degrading its high global stiffness. The application of micro strain gages in small notches on the slide represents a promising approach for the improvement of the sensitivity as well as the integration of sensors into the slide. This paper presents the utilization of the sensing axis-slide in a manufacturing environment. For this purpose, a first prototype of the slide is build and integrated into a milling center DMG HSC 55 linear. In this test machine, the dynamic characteristics of the integrated slide are identified with frequency response function measurements. Based on force measurements with a dynamometer, force calibration matrices are computed to calculate the forces in the machine coordinate system at the tool center point from the measured strain signals during milling processes. The force sensing with the slide allows furthermore the identification of tool characteristics such as the static tool stiffness. This parameter is estimated from the ratio of the measured contact forces and the set collision distance when moving the tool smoothly into the work piece. The known tool stiffness enables the detection of the static tool deflection from the force signals during a milling process. To compare the detected tool deflection with the real tool deflection, reference measurements on the work piece are performed using a perthometer. For further monitoring applications of the tool deflection in more complex 2.5D milling an approach to transform the measured forces from the stationary machine coordinate system into the moving tool coordinate system is presented.     

A smart boring tool for process control

A mechatronic metal cutting tool has been developed to improve the accuracy and flexibility of line boring machining in the automotive industry. Laser position sensors and piezoelectric actuator were integrated into the rotating body of the boring tool. To compensate the boring bar droop and effects of cutting forces, a fast tool servo utilizing feedback control of the boring tool insert position was designed and embedded in the rotating tool assembly. In addition to position control, a self-monitoring algorithm that utilizes disturbance estimator has been put together in the controller. Experimental results demonstrated that the developed cutting process controller improves the accuracy of the boring tool as well as reliably detects the process failures, such as tool tip breakage, without additional monitoring equipment.     

Current-sensor-based feed cutting force intelligent estimation andtool wear condition monitoring

Tool wear condition monitoring has the potential to play a critical role in ensuring the dimensional accuracy of the workpiece and prevention of damage to cutting equipment. It could also help in automating cutting processes. In this paper, the feed cutting force estimated with the aid of an inexpensive current sensor installed on the AC servomotor of a computerized numerical control tuning center is used to monitor tool wear condition. To achieve this, the feed drive system is modeled, using neuro-fuzzy techniques, to provide the framework for estimating the feed cutting force based on the feed motor current measured. Functional dependence of the feed cutting force on tool wear and cutting parameters are then expressed in the form of a difference equation relating variation in the feed cutting force to tool wear rate. The computerized system automatically compares successive feed cutting force estimates and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that the tool wear condition monitoring is effective and industrially applicable     

液压碳石墨密封环激光辅助车削的加工性能研究

为了实现液压碳石墨密封环的高效车削加工,采用激光辅助加工方法,进行了碳石墨密封环材料的激光辅助车削加工研究.考虑到碳石墨密封环材料具有高强度、高硬度等特点,利用激光束对工件进行局部加热,以提高加工效率、减小切削力和刀具磨损.针对碳石墨M104密封环的车削加工过程,进行了常规切削和激光辅助切削的对比实验研究.设计了激光辅助加工的实验流程,并进行了工艺参量的合理选择,得到了较高的切削效率.结果表明,激光辅助切削的主切削力和径向力分别比常规切削下降了23.5%和19.9%;激光辅助切削的切削区温度分布与常规切削相近;刀具磨损和破损的程度较小,能获得较好的表面加工质量.     

Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring

It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neuro-fuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring applications.     

雷达精密深腔壳体零件数控加工动力学研究

为了确定合理的精密深腔薄壁零件铣削加工工艺参数，借助动力学仿真的数控加工工艺参数优化方法，通过建立铣削过程动力学系统实验模态分析测试平台，测试具体加工机床和不同长径比刀具的动力学特性，再应用“切削过程动力学仿真优化系统”获取特定机床不同刀具特性、不同加工阶段加工系统的切削稳定域和优化的切削参数。从而有效提高零件材料的去除效率，抑制细长刀具的切削颤振现象，较好地解决精密深腔薄壁壳体零件铣削加工时侧壁产生切削振纹的难题。     

Tool breakage monitoring using motor current signals for machine tools with linear motors

In recent years, a number of machining centers have been built using linear motors. These machining centers have great potential for precision and high-speed machining. Nevertheless, a number of problems remain unsolved, such as monitoring and control. This paper presents a new tool breakage monitoring method for this type of machining center using the current signal of the linear motor. First, the relationship between the cutting force and the motor current is analyzed. Then, the new tool breakage method is presented. From a mathematical point of view, the new method uses a nonlinear energy operator to capture the abrupt changes of the motor current signal, which is directly related to the tool breakage. The experiment validation is included.     

Intelligent process supervision for predicting tool wear in machining processes

An intelligent supervisory system supported on a model-based approach is presented herein. The application for predicting tool wear in machining processes is selected as a case study. A model created using artificial neural networks and able to predict the process output is introduced as a means of dealing with the characteristics of such an ill-defined process as machining. This model describes the output’s dynamic response to changes in the process-input command (feed rate) and process parameters (depth of cut). In order to predict tool wear, residual errors are used as the basis for a decision-making algorithm. Based on the model and the weighted sum of squared residuals method, the procedure continuously checks whether a given index (tool condition) exceeds a critical threshold. In the chosen application, an over-the-threshold index is interpreted as indicating unacceptable tool wear necessitating immediate tool replacement. Experimental tests are run in a professional machining centre under different cutting conditions using real-time data and new, half-worn and worn tools. The results show this supervisory system’s suitability and potential for industrial applications.     

A new method based on Fiber Bragg grating sensor for the milling force measurement

Milling force is an important parameter to describe the mechanical processing chip removal process, and it has a direct influence on generation of heat, tool wear or failure, quality of machined surface and accuracy of the work piece. Its accurate measurement is a significant basis for judging process state and improving the reliability of machining system. In this study, through analyzing the variation rule of ring diameter, a new method that using Fiber Bragg grating sensors and variation rule of ring diameter to measure the milling force has been proposed, and the basic structure of annulus has also been designed. A dynamometer has also been constructed, and the preliminary verification test was done. Through the analysis of experimental data, the dynamometer based on annulus elastic body can be used in milling force test, and it owns high sensitivity.     

Milling cutter breakage detection by the discretewavelet transform

The paper describes the use of cutting force to detect milling cutter breakage based onthe discrete wavelet transform. The discrete wavelet transform performs a multi-level signaldecomposition of the cutting force so that the cutter breakage features can be extracted. Millingcutter breakage can then be detected from the cutting force with or without the cutter breakagefeatures. Experimental results have indicated that milling cutter breakage can be successfullydetected even under different cutting conditions.     

Discrete-time model predictive contouring control for biaxial feed drive systems and experimental verification

In this paper we present a novel algorithm to model predictive contouring control for biaxial feed drive systems. model predictive control (MPC) refers to a class of model-based controllers that uses an explicit process model and tracking error dynamics to predict the future behavior of a plant, making it effective for machine tool feed drive systems that must achieve high-precision motion and are severely affected by friction, cutting force and changes in the workpiece mass. To improve contouring performance, we propose a new performance index in which error components orthogonal to the desired contour curve are given more importance than tracking errors with respect to each feed drive axis. Controller parameters are calculated in real time by solving an optimization problem. The parameters depend on the instantaneous slope of the reference trajectory and thus vary with time for curved reference trajectories, resulting in a time-varying controller. Weighting factors for the error components in orthogonal and tangential directions are used to adjust the error importance in each direction. In addition, to consider the required feed drive energy, the control inputs in both directions are included in the performance index. The effectiveness of the proposed control approach is demonstrated with an experimental biaxial feed drive system for circular and non-circular trajectories. The proposed contouring controller allows the feed drive to follow smooth curves and reduces contouring error.     

Active vibration control and real-time cutter path modification in rotary wood planing

Forced structural vibration and cutting tool inaccuracy have been identified to be the primary causes of surface defects in rotary wood planing. This paper presents the development of a control strategy used to compensate for the effects of both vibration and cutting tool inaccuracy on planed wood surface finish. The solution is based on active vibration control and real-time modification of the cutting tool trajectory using an optimal Linear Quadratic Gaussian tracking controller. A small-scale mechatronic wood planing machine, which has an actively controlled spindle unit, has been designed for practical investigation of the proposed technique. Experimental results show that the applied compensation increased the dynamic performance of the machine and the quality of the surface finish produced.     

Fusion of hard and soft computing techniques in indirect, online tool wear monitoring

Indirect, online tool wear monitoring is one of the most difficult tasks in the context of process monitoring for metal-cutting machining processes. Based on a continuous acquisition of certain process parameters (signals such as cutting forces or acoustic emission) with multi-sensor systems, it is possible to estimate or to classify certain wear parameters. However, despite of intensive scientific research during the past decades, the development of reliable and flexible tool wear monitoring systems is an ongoing attempt. This article introduces a new, hybrid technique for tool wear monitoring in turning which fuses a physical process model (hard computing) with a neural network model (soft computing). The physical model describes the influence of cutting conditions (such as tool geometry or work material) on measured force signals and it is used to normalize these force signals. The neural model establishes a relationship between the normalized force signals and the wear state of the tool. The advantages of this approach are demonstrated by means of experimental results. Moreover, it is shown that the consideration of process parameters, cutting conditions, and wear in one model (either physical or neural) is extremely difficult and that existing hybrid approaches are not adequate. The ideas presented in this article can be transferred to many other process monitoring tasks.     

Cooperative teleoperation of a multirobot system with force reflection via Internet

With the rapid development of information technology, the Internet has evolved from a simple data-sharing media to an amazing information world where people can enjoy various services. Recently, the use of the Internet has been expanded to the field of automation, i.e., using the Internet as a tool to control equipment located at remote sites. This work presents a system that enables multiple operators at different sites to cooperatively control multiple robots with real-time force reflecting via the Internet. To overcome instability and reliability problems caused by random time delay of the Internet communication, we adopt an event as the reference for the controller design. To improve real-time efficiency and reduce the complexity of the controller, a distributed approach is proposed for the control of remote robots, so that the time delay in one control loop does not affect performance of the others. A vision-based method is developed to monitor and render interactions between the robots. The usefulness and effectiveness of the developed method and system have been verified by teleoperation experiments on a two-robots cooperative system among Hong Kong, mainland China, and the U.S.