Advanced adaptive feed control for CNC machining

In computer numerical control (CNC) machining, the tool feed rate is crucial for determining the machining time. It also affects the degree of tool wear and the final product quality. In a mass production line, the feed rate guides the production cycle. On the other hand, in single-time machining, such as for molds and dies, the tool wear and product quality are influenced by the length of machining time. Accordingly, optimizing the CNC program in terms of the feed rate is critical and should account for various factors, such as the cutting depth, width, spindle speed, and cutting oil. Determining the optimal tool feed rate, however, can be challenging given the various machine tools, machining paths, and cutting conditions involved. It is important to balance the machining load by equalizing the tool's load, reducing the machining time during no-load segments, and controlling the feed rate during high load segments. In this study, an advanced adaptive control method was designed that adjusts the tool feed rate in real time during rough machining. By predicting both the current and future machining load based on the tool position and time stamp, the proposed method combines reference load control curves and cutting characteristics, unlike existing passive adaptive control methods. Four different feed control methods were tested including conventional and proposed adaptive feed control. The results of the comparative analysis was presented with respect to the average machining load and tool wear, the machining time, and the average tool feed speed. When the proposed adaptive control method was used, the production time was reduced up to 12.8% in the test machining while the tool life was increased.     

Cloud-based approach for automatic CNC workpiece origin localization based on image analysis

Computer vision applications in the industry have been a constant field of research in academia. Industrial daily challenges such as quality inspection, object detection, and measurement are examples of situations where some automation could be done by using computer vision techniques. In this paper, a cloud-based approach of an automatic system based on stereo vision and image analysis has been developed to automate a daily routine present in machining companies: workpiece referencing. The proposed architecture uses two cameras mounted in the spindle of a machining center. All images are processed in custom software, running on the cloud, to return the position of the Workpiece Coordinate System (WCS) directly to the Computer Numerical Control (CNC) machine controller. Experimental results validate the application of the proposed architecture in a real machining process machine.     

一种曲线路径剪裁中刀具形变及其误差分析

动态移动切削阻力载荷对高速数控裁床加工过程中刀具形变及其剪裁误差具有的重要影响,提出了一种适用多层布料/皮革曲线剪裁路径的刀具形变及其误差计算方法;建立了动态负载条件下可伸缩刀具的挠度与转角方程,进而推导出高频振动裁刀剪裁误差及其随切削深度变化规律;计算结果表明,数控布料/皮革剪裁刀的动态载荷、高频振动参数、切削深度对剪裁误差具有重要影响,深入剖析高层数控裁床的加工机理,动态参数数据分析,对于提高机床加工效率,降低加工误差,提高刀具使用寿命具有一定的工程应用价值。     

Contouring controller design based on iterative contour error estimation for three-dimensional machining

Recently, there has been a growth of interest in high precision machining in multi-axis feed drive systems, subjected to problems such as friction, cutting force and incompatibility of individual driving axis dynamics. To guarantee high precision machining in modern computer numerical controlled (CNC) machines, CNC's controllers do its control efforts to reduce contour error. One of the common approaches is to design a controller based on the estimation of contour error in real time. However, for complex contours with severe curvatures, there is a lack of effective algorithms to calculate contour errors accurately. To address this problem, this paper proposes an accurate contour error estimation procedure for three-dimensional machining tasks. The proposed method is based on an iterative estimation of the instantaneous curvature of the reference trajectory and coordinates transformation approach, and hence, it is effective for complex reference trajectories with high curvatures. In addition, contour error controller is presented to reduce the estimated contour error. The feasibility and superiority of the proposed model as well as contour error controller are demonstrated through experimental system using a desk-top three-axis CNC machine.     

非接触式三坐标测量短圆弧方法研究

非接触式三坐标测量机是把光学、机械和计算机控制技术融为一体的高精度、高效率、功能强大的测量设备,对于加工中心、数控机床加工的零件检测和制造过程中对形状复杂、公差要求严格的零件检测都特别有效,给数控机床的工装夹具和刀具位置的调整及加工程序补偿提供最有效且精确的数据。实际工作中发现,由于短圆弧工件选取测试采集点比较集中,按照传统的测试方法效果不尽如人意。为此,研究测量短圆弧方法,解决实际测试误差显得至关重要。着重对短圆弧测量误差理论数据进行分析,提出工作中切实可行的测量方法。     

Inspection of the machined surfaces using manufacturing data

Accuracy of complex machined parts is usually estimated using coordinate measurement machines. The inspection results can be used to control or monitor a production system. Traditional inspection process of a machined surface commonly compares the resulting manufactured surface with the CAD-model or the nominal designed surface. Although, the result of this CAD-based inspection can be used to accept or reject the machined part, it does not provide the correct information required to study the machining errors and their behaviour. This paper presents a methodology to inspect a machined surface directly based on its actual CAM geometric model. The presented CAM-based inspection methodology can be used to model and understand the real machining errors, which can be utilized for process control, design for manufacturing or closed-loop machining.     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models – Multiple regression, Random forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply random forest or quantile regression techniques to the machining domain. The performance of these models was compared to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).     

Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review

In Computer Numerical Control (CNC) machining, determining optimum or appropriate cutting parameters can minimize machining errors such as tool breakage, tool deflection and tool wear, thus yielding a high productivity or minimum cost. There have been a number of attempts to determine the machining parameters through off-line adjustment or on-line adaptive control. These attempts use many different kinds of techniques: CAD-based approaches, Operations Research approaches, and Artificial Intelligence (AI) approaches. After describing an overview of these approaches, we will focus on reviewing AI-based techniques for providing a better understanding of these techniques in machining control. AI-based methods fall into three categories: knowledge-based expert systems approach, neural networks approach and probabilistic inference approach. In particular, recent research interests mainly tend to develop on-line or real-time expert systems for adapting machining parameters. The use of AI techniques would be valuable for the purpose.     

An integrated intelligent process planning system (IIPPS) for machining

A key component of computer integrated manufacturing (CIM) is computer aided process planning (CAPP). Process planning in machining involves the determination of the cutting operations and sequences, the selection of machine tools and cutting tools, the calculation of machining parameters, and the generation of CNC part programs. Industrial structures in Norway are defined as small and medium-sized companies. The important fact is how well these companies use high technologies and resources in order to improve their production efficiency, product quality, and company competition in international markets. The concept of an integrated intelligent system (IIS) is created for this purpose. A prototype system, the integrated intelligent process planning system (IIPPS), is described for machining; it was developed on the basis of an IIS and constructed using three levels of effort: (1) AutoCAD, (2) dBASE III and (3) KnowledgePro. The system may be utilized not only by a process plann ing engineer in a company, but also by students of mechanical or industrial engineering.     

Analysis of unified error model and simulated parameters calibration for robotic machining based on Lie theory

In robotic machining process, the kinematic errors of serial structure and compliance errors caused by external cutter-workpiece interactions can result in considerable deviation of the desired trajectory. Therefore, this paper proposes an efficient calibration methodology by establishing a unified error model about kinematic errors and compliance errors based on Lie theory, which simultaneously calibrates the kinematic parameters and joint compliances of a serial machining robot. In this methodology, the propagation law of kinematic errors is investigated by analysis of the kinematic error model, and the corresponding equivalent kinematic error model is thus obtained, in which the joint offset errors are regarded as one source of twist (joint twist and reference configuration twist) errors. On this basis, with the segmentation and modelling of the joint compliance errors caused by the link self-weight and cutting payloads, the unified error model is developed by linear superposition of configuration errors of the robotic end-cutter, calculated from the kinematic errors and compliance errors respectively. Meanwhile, to improve the accuracy of parameters calibration, the observability index is adopted to optimize the calibration configurations so as to eliminate the twist error constraints. The calibrated kinematic parameters and joint compliances are obtained eventually, and used to compensate the kinematic and compliance errors of the serial machining robot. Finally, to validate the effectiveness of the proposed unified error model, simulation analysis is performed using a 6-DOF serial machining robot, namely KUKA KR500. The comparisons among calibrated parameters show that the unified error model is more computationally efficient with optimal calibration configurations, rendering it suitable for the calibration of kinematic parameters and joint compliances in actual machining applications.     

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.     

Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Spindle thermal error prediction approach based on thermal infrared images: A deep learning method

It is essential to precisely model the spindle thermal error due to its dramatic influence on the machining accuracy. In this paper, the deep learning convolutional neural network (CNN) is used to model the axial and radial thermal errors of horizontal and vertical spindles. Unlike the traditional CNN model that relies entirely on thermal images, this model combines the thermal image with the thermocouple data to fully reflect the temperature field of the spindle. After pre-processing and data enhancement of the thermal images, a multi-classification model based on CNN is built and verified for accuracy and robustness. The experimental results show that the model prediction accuracy is approximately 90 %–93 %, which is higher than the BP model. When the spindle rotation speed changes, the model also shows good robustness. Real cutting tests show that the deep learning model has good applicability to the spindle thermal error prediction and compensation.     

Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system

Virtual machining systems are applying computers and different types of software in manufacturing and production in order to simulate and model errors of real environment in virtual reality systems. Many errors of CNC machine tools have an effect on the accuracy and repeatability of part manufacturing. Some of these errors can be reduced by controlling the machining process and environmental parameters. However geometrical errors which have a big portion of total error need more attention. In this paper a virtual machining system which simulates the dimensional and geometrical errors of real three-axis milling machining operations is described. The system can read the machining codes of parts and enforce 21 errors associated with linear and rotational motion axes in order to generate new codes to represent the actual machining operation. In order to validate the system free form profiles and surfaces of virtual and real machined parts are compared in order to present the reliability and accuracy of the software.     

Free-form surface machining error compensation applying 3D CAD machining pattern model

The article presents a methodology for compensating for systematic influences of computer numerical control (CNC) machining processes on free-form surfaces. The proposed procedure is performed off-line by introducing corrections compensating for these influences to machining programs. The effect of systematic influences of machining are deterministic deviations of surfaces. CAD models of these deviations, averaged for a number of surfaces machined under repeatable conditions, represent a machining pattern model (MPM) which serves as the basis for performing compensation. The basis for developing such models is surface deviations determined during coordinate measurements carried out along a regular grid of points. For estimating surface MPMs, a methodology is proposed in which regression analysis, spatial statistics methods, an iterative procedure, and NURBS modeling are applied. An MPM with the opposite sign was used for compensating systematic influences of the ball-end milling process by modifying the nominal geometry data and correcting the machining program. The results of machining error compensation carried out on the basis of a previously developed MPM were compared to the results of compensation performed on the basis of raw measurement data as well as to the results these after compensation on the basis of a model of deterministic deviations for the surface under study.     

数控加工中加工误差预测模型的研究

提出了一个在球头端铣加工中预测复杂曲面加工误差的理论模型.在理论模型的基础上,计算出了曲面各个部分的由刀具变形引起的加工误差.对影响加工误差的诸如切削模式、铣削位置角、曲面几何形状等各种切削状况进行了研究.最后,使用加工中心,在各种加工状况下.通过一系列实验对理论模型进行了验证.并利用计算机图形学工具对二者进行了建模仿真,结果显示理论值与实验值非常吻合.恰好证明了趣论模型在预测表面加工误差方面的适应性非常好.     

Computer-based estimation and compensation of diametral errors in CNC turning of cantilever bars

This paper aims to introduce a computer-based estimation and compensation method for diametral errors in cantilever bar turning without additional hardware requirements. In the error estimation method, the error characteristics of workpieces are determined experimentally depending on cutting speed, depth of cut, feed rate, workpiece diameter, length from the chuck and the geometric error sum of CNC lathe. An Artificial Neural Network (ANN) model is trained using these experimental error characteristics for estimation of the error. The ANN model estimated the workpiece dimensional errors with a good accuracy. Error correction is realised via turning of workpieces with a CNC part program which modified based on the estimated error profile. The dimensional errors are reduced approximately by 90% with the proposed method.     

A configuration independent error model of machine tools: Hyperpatch model and metrology pallet

A machine tool error model based on a three-dimensional hyperpatch error map was developed to describe the machine tool error field over a workspace. The error map was generated by a set of measurement points without using a detailed relationship among joints and linkages of a machine tool. The modeling is based on the motion that an ideal workspace is distorted to an actual workspace through a prescribed parametric representation. The concept was implemented on a three-axis CNC machining center. A metrology pallet was designed to serve as a reference coordinate frame of the workspace. A touch trigger probe was used to collect the required data. Experimental results show that this error model is capable of modelling the warm machine tool distortion behavior. The proposed model is based on the observed phenomena rather than the structural configuration of machine tools. Therefore, the advantage of this approach is that only a few measurements are required to construct the prediction model for a variety of machine tool configurations and for working environments.     

Compensating for systematic errors in 5-axis NC machining

The errors introduced during 5-axis machining are higher than the intrinsic repeatability of the machine tool. It is possible to identify such systematic errors and compensate for them, thus achieving higher performance. A group of systematic errors can be compensated for directly in the inverse kinematics equations. Other systematic errors can be combined and compensated for through the total differentials of the inverse kinematics relations. A new general approach on how to compensate for the systematic errors based on the closed loop volumetric error relations is presented. The errors due to the 5-axis toolpath generation in current CAD/CAM and CNC are analyzed in detail. A new strategy for measurement and compensation is presented.