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。     

Optimization of cutting conditions during cutting by using neural networks

Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.     

Hardware-in-the-loop simulator for stability study in orthogonal cutting

The self-excited vibrations due to the regenerative effect, commonly known as chatter, are one of the major problems in machining processes. They cause a reduction in the surface quality and in the lifetime of mechanical elements including cutting tools. Furthermore, the experimental investigations of chatter suppression techniques are difficult in a real machining environment, due to repeatability problems of hard to control parameters like tool wear or position dependent dynamic flexibility. In this work, a mechatronic hardware-in-the-loop (HIL) simulator based on a flexible structure is proposed for dimensionless study of chatter in orthogonal cutting. Such system reproduces experimentally, on a simple linear mechanical structure in the laboratory, any stability situation which can be used to test and optimise active control devices. For this purpose, a dimensionless formulation is adopted and the delay related to the phase lag of the actuator and the controller employed on the HIL is compensated.     

A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.     

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.     

A research on the cutting database system based on machining features and TOPSIS

Cutting parameters play a significant role in machining processes. The traditional cutting database usually neither include all information about part machining nor provide the best alternative of cutting parameters automatically when several alternatives meet the requirements for retrieval. The paper presents a cutting database system based on machining features and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for selecting the best alternative of cutting parameters. Following the object-oriented idea, machining features are organized by part feature, geometric information, material information, precision information and manufacturing resources information, which is very convenient for the database to store and manage the necessary machining information. The multiple criteria decision making matrix D is constructed by spindle speed, feed rate, cutting depth and cutting width. And the best alternative of cutting parameters is selected according to the closeness coefficient by TOPSIS. In addition, a prototype system based on Web browsing mode has been developed. Finally, an example is used to validate that the proposed system is feasible and effective.     

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.     

中国微米纳米-三维集成车削力传感器数据采集与分析系统设计

切削力与切屑形成、切削热、刀具磨损和切削振动等现象有着密切联系,是影响加工精度、刀具寿命和切削效率的重要因素。通过实时测量切削力,及时调整切削参数、优化切削工艺,对于保证加工质量、延长刀具寿命、提高切削效率等有着重要意义。切削力的准确测量和处理离不开优良的数据采集与分析系统,本文针对基于MEMS压阻式芯片的三维集成车削力传感器,以微处理器STM32为控制核心研制了一种三维集成车削力传感器数据采集与分析系统,实现了三维车削力的标定、实时采集和数据分析功能。     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Position-oriented process monitoring in milling of thin-walled parts

Improving machining performance of thin-walled parts is of great significance in aviation industry, since most aviation parts are characterized by large size, complex shape, and thin-walled structure. Machining process monitoring is the essential premise to improve the machining performance. In order to improve the machining quality and efficiency, this paper presents a position-oriented process monitoring model based on multiple data during milling process, and corresponding solution is provided. Through obtaining the internal data set of the numerical control (NC) system during machining, it is possible to correlate the cutting position with monitoring signals including cutting force, acceleration, and spindle power. Then, process optimization is realized to improve the machining quality and efficiency based on the monitoring results. Machining tests are conducted on aircraft structural part as well as blade part, and the experimental results show this method provides a significant insight into the machining process of thin-walled part and contributes to the process optimization. By using feedrate optimization, time consumption for the rough milling process of one titanium alloy part reduced from 19.1 h to 14.4 h and the number of cutter consumption dropped from 5 to 3. And according to the result of position-oriented process monitoring, the machining strategies were optimized to reduce vibration and avoid chatter, thereby improving the machining quality.     

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.     

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.     

Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching–Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.     

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.     

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.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

Modeling and adaptive force control of milling by using artificial techniques

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On the basis of the hybrid process modeling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter, etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identifiers of the process dynamics and primary regulator. An overall procedure of hybrid modeling of cutting process used for creating the CNC milling simulator has been prepared. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability, but also the machining efficiency of the milling system with the adaptive controller is 27% higher than for traditional CNC milling system.     

iCoSim-FMS: An intelligent co-simulator for the adaptive control of complex flexible manufacturing systems

We describe an intelligent co-simulator for real time production control of a complex flexible manufacturing system (CFMS) having machine and tool flexibility. The manufacturing processes associated with the CFMS are complicated with each operation being possibly done by several machining centers. The co-simulator design approach is built upon the theory of dynamic meta-model based supervisory control with the cooperation of its own embedded intelligent blocks. The system is implemented by coupling of the centralized simulation controller (CSC) and real-time simulator for enforcing dynamic strategies of shop floor control. The posteriori adaptive co-simulator is equipped with a concurrent bilateral mechanism for simulation optimization based on appropriate control rules enhancing performance criteria simulation efficiency. A working intelligent adaptive controller prototype (iCoSim-FMS) has been developed to validate the proposed approach and compare its performance with well known FMS heuristic methods.     

Cloud-DPP for distributed process planning of mill-turn machining operations

Today, the dynamic market requires manufacturing firms to possess a high degree of adaptability to deal with shop-floor uncertainties. Specifically targeting SMEs active in the metal cutting sector who normally deal with intensive process planning problems, researchers have tried to address the subject. Among proposed solutions, Cloud-DPP elaborates a two-layer distributed adaptive process planning based on function-block technology and cloud concept. One of the challenges of companies is to machine as many part features as possible in a single setup on a single machine. Nowadays, multi-tasking machines are widely used due to their various advantages such as reducing setup times and increasing part accuracy. However, they also possess programming challenges because of their complex configuration and multiple machining functions. This paper reports the latest state of design and implementation of Cloud-DPP methodology to support parts with a combination of milling and turning features, and process planning for multi-tasking machining centers with special functionalities to minimize the number of setups. The contributions of this work are: representation of machining states and part transfer functionality, support of multi-tasking machines in adaptive setup merging, development of special function blocks to handle sub-setups and transitions, and finally generation of function-block network for the merged setups. The developed prototype is validated through a case study.     

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.