Simulation of cutting forces in ball-end milling

The paper presents a simulation system (SCP) that determines the cutting forces in the ball-end milling process. The system is based on numerical methods, computer programme, theoretical knowledge of technological processes, machining and tests performed. The system for simulation of the cutting process combines the technological data base, the analytical and experimental model and the data base SCP. The experimental model contains a collection of variables of the cutting process by means of sensors and transformation of those data into numerical values, which are a starting point for data calculation of characteristic coefficients of materials. The analytical model is used to estimate the tangential, radial and axial cutting forces, along with a material data base obtained from cutting experiments. Ball-end milling test has been conducted to verify simulation results. The simulation results, are the basis for the development of the tool-designing model and for the model of optimization of the machining process cutting parameters.     

A knowledge base model for complex forging die machining

Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometrical features. Technological data and topological relations are aggregated to a geometrical feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate machining sequences. A machining process model is proposed to formalize the links between information imbedded in the machining features and the parameters of cutting tools and machining strategies. At last machining sequences are grouped and ordered to generate the complete die machining process. In this paper the identification of geometrical features is detailed. Geometrical features identification is based on machining knowledge formalization which is translated in the generation of maps from STL models. A map based on the contact area between cutting tools and die shape gives basic geometrical features which are connected or not according to the continuity maps. The proposed approach is illustrated by an application on an industrial study case which was accomplished as part of collaboration.     

Automatic feedrate adjustment for pocket machining

As high-speed machining and unmanned machining become common, the demand for cutting-load regularization increases, so NC machining can be more efficient. To be presented is a simple cutting-load regularization method for pocket machining. As the conventional off-line approaches where cutting-load is predicted and cutting parameters are adjusted before actual cutting, the proposed method requires a cutting force model, which is quite simplified with the function of two independent variables. One is the geometric measure so called 2D chip-load (cutter-engagement angle or effective cutting depth), and the other is the feedrate. Based on the 2D chip-load analysis for the concave line-line segment of the NC tool path, the adjusted feedrate is calculated by using the simplified-cutting force model (SCFM) obtained by the cutting experiment with a tool dynamometer. The concept of the automatic feedrate adjustment (AFA) method to be proposed is very simple, and the implementation requires little effort. Furthermore, the proposed method does not need much calculation time because there are no complex calculations or cutting simulation.     

基于神经网络的机械加工工序能耗预测

针对工序级能耗难以用数学方法精确估算的问题,提出了一个基于神经网络的机械加工工序能耗预测方法。给出了输入变量及输出变量的选取及其归一化处理方法,进行了隐含层节点数和传递函数的选取。以各切削用量组合及其对应能源消耗的历史数据作为神经网络训练的样本集,建立切削用量组合方案输入和能源消耗输出间的非线性关系,从而对新的切削用量参数组合进行能耗值的预测。以某企业导叶片的粗铣加工为例,验证了该能耗预测方法的有效性。     

三维工序模型切削载荷动态变化的工艺优化策略

综合零件的加工特征及约束条件并引入布尔差运算,可以分解出零件典型面组并 生成对应的工艺路线谱系,达到零件加工工艺快速优化设计的目的。通过分析三维工序模型典 型面组加工时的动态特性,采用B 样条曲线插值参数化的方法构建刀具-工件接触区域动态边界 的统一数学模型,预测切削载荷动态变化状况,并进行切削参数优化达到有效控制切削载荷的 目的。最后以某零件的车削加工为例进行分析,验证该工艺优化策略的可行性。     

Digital twin-driven surface roughness prediction and process parameter adaptive optimization

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.     

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.     

Sensitivity analysis of material input data influence on machining induced residual stress prediction in Inconel 718

Inconel 718 is commonly used in structural critical components of aircraft engines due to its properties at high temperatures. In order manufacture the final part, these components have to be machined, so the final surface integrity obtained after machining becomes a key issue. Residual stresses, which are included in surface integrity, are an important issue. Although much of the research carried out on machining induced residual stresses has been empirical, finite element modelling appears to be a complementary solution to gain understanding of it. However, some of the major drawbacks still need to be solved before it can become a reliable tool for industry, such us the identification of input parameters and computational cost. This paper deals with the study of machining induced residual stresses. An orthogonal cutting 2D finite element model was used and a sensitivity analysis was conducted to determine the influence of model input data on the predicted residual stresses. The results obtained from the sensitivity analysis showed that material constitutive law was the most relevant input data when predicting residual stress fields. Importantly the material behaviour at a high heating rate in adition to high strain rate must be considered.     

Genetic equation for the prediction of tool–chip contact length in orthogonal cutting

In metal cutting, it has been acknowledged that the tool–chip contact length significantly affects many aspects of machining such as chip formation, cutting forces, cutting temperatures, tool wear and tool life. Important decrease in the tool–chip contact length, decreases the thickness of the secondary shear zone, which leads to a decrease of the cutting temperature and cutting force. As a result, it has a great effect on finish surface and tool life. Several ways have been proposed in different works to find its value, which have given discordant results for the same set of cutting conditions. In this paper, the genetic equation for the tool–chip contact length is developed with the use of the experimentally measured contact length values and genetic programming. The suggested equation has shown to correspond well with experimental data in various machining conditions with associated cutting parameters and this model predicts tool–chip contact length better than other known solutions.     

Optimization of cutting process by GA approach

The paper proposes a new optimization technique based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA. It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.     

An innovative approach for automatic generation,verification and optimization of part programs in turning

Continuous innovation of products and optimization of manufacturing processes are of fundamental importance for preserving competitiveness. In the last decades, several approaches based on analytic models for optimization of basic machining operations such as cylindrical turning and face milling have been developed. However, the analytic approaches may not be adequate for real industrial applications, since they are based on average cutting parameters and thus they are not capable of taking into account the effect of complex geometries and instantaneous cutting conditions. In this paper, an innovative integrated system for automatic generation of optimized part programs in turning based on realistic machining simulation is proposed. The system components are described in detail and the machining simulator is validated by comparison with the results of real cutting tests. Then, the optimization approach is applied to a simple case study. The results show that the behavior of the cost function is rather complex, even for simple workpieces. Moreover, the simulator can detect unfeasible combinations of cutting parameters and thus reduce inline part program refinement and optimization. The optimal combination of cutting parameters determined by the new system was competitive with the solutions derived from tool specifications or proposed by a machining expert.     

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.     

Advanced CNC system with in-process feed-rate optimisation

Tight quality requirements and stringent customer demands are the main thrust behind the development of new generation machine tool controllers that are more universal, adaptable and interoperable. The development of some international standards such as STEP and STEP-NC presents a vision for intelligent CNC machining. Implementation of STEP-NC enabled Machine Condition Monitoring (MCM) is presented in this paper. The system allows optimisation during machining in order to shorten machining time and increase product quality. In the system, an optiSTEP-NC, an AECopt controller and a Knowledge-Based Evaluation (KBE) module have been developed. The aim of the optiSTEP-NC system is to perform initial feed-rate optimisation based on STEP-NC data to assist process planners in assigning appropriate machining parameters. AECopt acts as a connector between the process planner and machining environment with the intention to provide adaptive and automatic in-process machining optimisation. KBE based-MTConnect is responsible for obtaining machining know-how. Optimisation is performed before, during or after machining operations, based on the data collected and monitored such as machining vibration, acceleration and jerk, cutting power and feed-rate.     

A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost

NC machining is currently a machining method widely used in mechanical manufacturing systems. Reasonable selection of process parameters can significantly reduce the processing cost and energy consumption. In order to realize the energy-saving and low-cost of CNC machining, the cutting parameters are optimized from the aspects of energy-saving and low-cost, and a process parameter optimization method of CNC machining center that takes into account both energy-saving and low -cost is proposed. The energy flow characteristics of the machining center processing system are analyzed, considering the actual constraints of machine tool performance and tool life in the machining process, a multi-objective optimization model with milling speed, feed per tooth and spindle speed as optimization variables is established, and a weight coefficient is introduced to facilitate the solution to convert it into a single objective optimization model. In order to ensure the accuracy of the model solution, a combinatorial optimization algorithm based on particle swarm optimization and NSGA-II is proposed to solve the model. Finally, take plane milling as an example to verify the feasibility of this method. The experimental results show that the multi-objective optimization model is feasible and effective, and it can effectively help operators to balance the energy consumption and processing cost at the same time, so as to achieve the goal of energy conservation and low-cost. In addition, the combinatorial optimization algorithm is compared with the NSGA-II, the results show that the combinatorial optimization algorithm has better performance in solving speed and optimization accuracy.     

Modelling and implementing circular sawblade stone cutting processes in STEP-NC

The paper presents a STEP-NC compliant implementation of circular sawblade stone cutting machining processes. Although some stone machining processes has been already covered in the STEP-NC research and standardization initiatives (as for instance stone machining through stone milling machines), there have not been yet, however, any detailed model proposal to cover circular sawblade stone cutting operations. Sawblade cutting technology for stone parts have several specific parameters with no clear equivalent technologies as defined in milling, turning, etc. The paper reviews main characteristics of the circular sawblade stone cutting machining operations, and proposes a STEP-NC extended model based on the selection and definition of new features and on the modelling of these stone cutting operations. The resulting model is the base for the development of the STEP-NC stone cutting CAM and CNC machine. The machine architecture is designed to be able to react to changes in the machining conditions, very common in this technology. The system is based on the definition of features to be communicated to the controller. The controller has the objective of machining the features, and it is able to re planning, on real time, the work to get them despite changing conditions in the stone or in the disc.     

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.     

Dealing with feature interactions for prismatic parts in STEP-NC

Determining the precedence of machining features is a critical issue in feature-based process planning. It becomes more complex when geometric interaction occurs between machining features. STEP-NC, the extension of STEP (ISO 10303) standard developed for CNC controllers, is a feature-based data model. It represents all the geometric and topological product data minus feature interactions. In this paper, machining precedence of interactive and non-interactive STEP-NC features is discussed. Local and global precedence of machining features are defined on the basis of geometric constraints, such as geometric interaction of features and feature approach face and technological constraint such as access direction of the cutting tool. A software tool has been developed to visualize the STEP-NC part model and to generate the graphs of feature interaction and feature precedence. The output can be then used to augment the STEP-NC data in order to generate the optimal sequence of operations.     

Design of artificial neural networks for tool wear monitoring

An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing.     

A framework for machining optimisation based on STEP-NC

Inappropriate machining conditions such as cutting forces cause tool failures, poor surface quality and worst of all machine breakdowns. This may be avoided by using optimal machining parameters, e.g. feed-rate, and continuing to monitor it throughout the machining process. To optimize feed-rate, we propose a system that consists of an optimisation module, a process control module and a knowledge based evaluation module. STEP-NC is the underlying data model for optimisation. Given the nominal powers, the cutting force can be estimated based on the higher-level production information such as workpiece properties, tool materials and geometries, and machine capabilities. The main function of the Process Control module is process monitoring and control. The output is the desired actual feed-rate. Finally, the actual feed-rate is recorded and evaluated in the Knowledge Based Evaluation module.     

Dynamic NC simulation of milling operations

To increase productivity in manufacturing, accurate cutting-simulation systems have increasingly been used to study the performance of machining processes. A new dynamic cutting-simulation system that can simulate the dynamic behaviour of the milling cutting force along the programmed NC toolpath is presented in the paper. The radial and axial depths of cut in the cutting process are extracted from a geometric cutting-simulation system on a workstation. Then, the radial and axial depths of cut and the other given cutting parameters are transmitted to a mechanistic model of the milling process from which the dynamic cutting force is obtained. There is good agreement between the simulated and measured cutting forces.