大型快锻液压机Takagi-Sugeno模糊系统设计

利用ITI-SimulationX软件建立了大型快锻液压机本体和主控系统的精确仿真模型,进行了三维本体与一维系统的集成仿真,结果表明:液压机热态锻造精度达到±lmm,最高锻造频次达85次/min;针对大型快锻液压机工艺范围广、工况复杂的特点,利用MATLAB/Simulink软件设计了Takagi-Sugeno模糊控制器,以主控系统锻造行程、锻透深度、最大工作压力、工作缸数量和主泵数量作为输入变量,高频响比例阀启闭曲线作为输出变量,依据主控系统仿真结果确定模糊推理规则,通过ITI-SimulationX与MATLAB/Simulink软件协同仿真接口模块实现了对主控系统的智能控制,使液压机在各种工艺参数下,均能达到较高锻造精度和较快响应速度,更好地适应了大型快锻液压机的生产工艺需求.     

A knowledge-based approach to design for manufacturability

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. Many reasons can be cited for the inability to implement the DFM approach effectively, including: lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions; lack of manufacturing cost information at the design phase; and absence of integrated engineering effort intended to maximize functional and manufacturability objectives. The purpose of this research is to show how expert systems methodology could be used to provide manufacturability expertise during the design phase of a product. An object- and rule-based expert system has been developed that has the capability: (1) to make process selection decisions based on a set of design and production parameters to achieve cost-effective manufacture; and (2) to estimate manufacturing cost based on the identified processes. The expertise for primary process selection is developed for casting and forging processes. The specialized processes considered are die casting, investment casting, sand casting, precision forging, open die forging and conventional die forging. The processes considered for secondary process selection are end milling and drilling. The cost estimation expertise is developed for the die casting process, the milling and drilling operations, and the manual assembly operations. The results obtained from the application of the expert system suggest that the use of expert systems methodology is a feasible method for implementing the DFM approach.     

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.     

A virtual inspection framework for precision manufacturing of aerofoil components

The finite element method plays an extremely important role in forging process design as it provides a valid means to quantify forging errors and thereby govern die shape modification to improve the dimensional accuracy of the component. However, this dependency on process simulation could raise significant problems and present a major drawback if the finite element simulation results were inaccurate. This paper presents a novel approach to assess the dimensional accuracy and shape quality of aeroengine blades formed from finite element hot-forging simulation. The proposed virtual inspection system uses conventional algorithms adopted by modern coordinate measurement processes as well as the latest free-form surface evaluation techniques to provide a robust framework for virtual forging error assessment. Established techniques for the physical registration of real components have been adapted to localise virtual models in relation to a nominal design model. Blades are then automatically analysed using a series of intelligent routines to generate measurement data and compute dimensional errors. The results of a comparison study indicate that the virtual inspection results and actual coordinate measurement data are highly comparable and the procedures for registration and virtual inspection are computationally efficient, validating the approach as an effective and accurate means to quantify forging error in a virtual environment. Consequently, this provides adequate justification for the implementation of the virtual inspection system in the virtual process design, modelling and validation of forged aeroengine blades in industry.     

端边云协同的复杂工业过程运行控制智能系统

针对难以建立数学模型的复杂工业运行控制过程,利用可获得的过程控制系统设定值和运行指标以及相关变量的工业大数据和运行控制过程特性,将系统辨识与深度学习相结合,建立以实际运行指标以及相关变量为输入,以实际过程控制系统设定值为输出的运行控制过程数字孪生模型,提出云-边协同的过程控制系统设定值智能控制方法.所提出方法由云-运行控制过程数字孪生模型、边-过程控制系统设定值智能控制模型和自校正机制组成.将工业互联网与工业过程控制系统相结合,提出端边云协同的工业运行控制智能系统的架构和功能,采用所提出控制系统设定值智能控制方法,研制工业过程运行控制智能系统,并在选矿关键设备—–高压辊磨成功应用.所提出系统安全、可靠和优化运行,取得了显著的节能减排效果.     

基于UG和MATLAB的连杆模具异形型腔的离散化研究

作为汽车发动机的主要运动件——连杆,其主流加工方式是模锻工艺,其重要加工装备之一是模锻模具,由于连杆外形复杂导致其模具型腔复杂,为提高连杆锻造模具异形型腔的加工精度,本文对连杆锻造模具异形型腔的离散化进行研究.考虑到连杆体锻模的型腔加工以数控加工为主,据此本文提出了基于三次参数样条函数的用于高速铣削加工的异形曲面的离散化方法,并通过仿真对比验证了该方法的可行性,为企业生产优质连杆的工艺制定提供了理论依据.     

Analysis of Die Forging Process and FEA of Piston

Die forging process of piston tail and its key technology are analyzed in detail. The key issue is to reduce friction force between punch and billet to facilitate metal flow towards two ends. Three-step forging scheme is used, namely preforming, final forging and cutting the flash. Preforming is critical for the success of the whole process and two schemes are compared. First two steps are simulated using 3D-Deform software. Simulation results show that the process is feasible and its validity is verified.     

Optimization of metal-forming process via a hybrid intelligent optimization technique

In recent years, finite element simulation has been increasingly combined with optimization techniques and applied to optimization of various metal-forming processes. The robustness and efficiency of process optimization are critical factors to obtain ideal results, especially for those complicated metal-forming processes. Gradient-based optimization algorithms are subject to mathematical restrictions of discontinuous searching space, while nongradient optimization algorithms often lead to excessive computation time. This paper presents a novel intelligent optimization approach that integrates machine learning and optimization techniques. An intelligent gradient-based optimization scheme and an intelligent response surface methodology are proposed, respectively. By machine learning based on the rough set algorithm, initial total design space can be reduced to self-continuous hypercubes as effective searching spaces. Then optimization algorithms can be implemented more effectively to find optimal design results. An extrusion forging process and a U channel roll forming process are studied as application samples and the effectiveness of the proposed approach is verified.     

元模型输入参数筛选方法研究

针对传统的灵敏度分析方法应用于元模型输入参数筛选时存在的突出问题,基于信息熵理论提出了元模型输入参数筛选方法。以条件熵为基础,提出了用于元模型输入参数筛选的参数体系,包括不确定度、不确定度浮动、不确定度累积浮动,以及元模型输入参数筛选分析过程;采用贝叶斯网络技术研究了多个子模型构成的组合模型描述问题,以及相应的条件熵计算问题;针对航空兵轰炸对于装备的损伤仿真问题,对单一模型和组合模型的输入参数筛选问题开展了案例研究。     

A self-organizing computing network for decision-making in data sets with a diversity of data types

A self-organizing computing network based on concepts of fuzzy conditions, beliefs, probabilities, and neural networks is proposed for decision-making in intelligent systems which are required to handle data sets with a diversity of data types. A sense-function with a sense-range and fuzzy edges is defined as a transfer function for connections from the input layer to the hidden layer in the network. By generating hidden cells and adjusting the parameters of the sense-functions, the network self-organizes and adapts to a training set. Computing cells in the input layer are designed as data converters so that the network can deal with both symbolic data and numeric data. Hidden computing cells in the network can be explained via fuzzy rules in a similar manner to those in fuzzy neural networks. The values in the output layer can be explained as a belief distribution over a decision space. The final decision is made by means of the winner-take-all rule. The approach was applied to a series of the benchmark data sets with a diversity of data types and comparative results obtained. Based on these results, the suitability of a range of data types for processing by different intelligent techniques was analyzed, and the results show that the proposed approach is better than other approaches for decision-making in information systems with mixed data types.     

Learning task applied to identification of a marine vehicle

This paper describes a learning algorithm to be applied in process control techniques, covering several topics of control applications such as system identification, or process supervision in a simple and useful way which make the method reliable to be applied on industrial process control. Knowledge acquired by means of a proposed learning algorithm is stored into a DAM or FAM (deterministic or fuzzy associative memory) for finally be applied on model parameter mapping. With such mappings, it is possible to know if the process is being disturbed and the intensity of such excitation inputs when no variation exist in system parameters or it is also possible to know the variation of system parameters when all input forces (control force and disturbances) are known.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Modeling,analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches,based on finite element results

Recently, twist extrusion has found extensive applications as a novel method of severe plastic deformation for grain refining of materials. In this paper, two prominent predictive models, response surface method and artificial neural network (ANN) are employed together with results of finite element simulation to model twist extrusion process. Twist angle, friction factor and ram speed are selected as input variables and imposed effective plastic strain, strain homogeneity and maximum punch force are considered as output parameters. Comparison between results shows that ANN outperforms response surface method in modeling twist extrusion process. In addition, statistical analysis of response surface shows that twist extrusion and friction factor have the most and ram speed has the least effect on output parameters at room temperature. Also, optimization of twist extrusion process was carried out by a combination of neural network model and multi-objective meta-heuristic optimization algorithms. For this reason, three prominent multi-objective algorithms, non-dominated sorting genetic algorithm, strength pareto evolutionary algorithm and multi-objective particle swarm optimization (MOPSO) were utilized. Results showed that MOPSO algorithm has relative superiority over other algorithms to find the optimal points.     

PREDICTIVE MODELS OF THE SPRING-BACK IN THE BENDING PROCESS

Spring-back is one of the most sensitive features of sheet metal forming processes because the elastic recovery during unloading leads to some geometric changes in the product. Three parameters that are most influential on spring-back in the V-die bending process are sheet thickness, sheet orientation, and punch tip radius. In this research, radial basis function (RBF) neural network and back propagation (BP) neural network approaches are used to predict the spring-back using the data generated from experimental observations. The performances of the models in training and testing sets are compared with those observations. Furthermore, output obtained through regression analysis is used to compare with the two developed model outputs. The results indicate that both networks can be applied successfully for prediction of spring-back against the input parameters of the V-die bending process. Also, the BP model shows better performance for prediction of spring-back with respect to the RBF model.     

Neural network based modeling and optimization of deep drawing – extrusion combined process

A combined deep drawing–extrusion process is modeled with artificial neural networks (ANN’s). The process is used for manufacturing synchronizer rings and it combines sheet and bulk metal forming processes. Input–output data relevant to the process was collected. The inputs represent geometrical parameters of the synchronizer ring and the outputs are the total equivalent plastic strain (TEPS), contact ratio and forming force. This data is used to train the ANN which approximates the input-output relation well and therefore can be relied on in predicting the process input parameters that will result in desired outputs provided by the designer. The complex method constrained optimization is applied to the ANN model to find the inputs or geometrical parameters that will produce the desired or optimum values of TEPS, contact ratio and forming force. This information will be very hard to obtain by just looking at the available historical input–output data. Therefore, the presented technique is very useful for selection of process design parameters to obtain desired product properties.     

Modelling the software development process using an expert simulation system having fuzzy logic

This paper describes an intelligent computerized tool designed to aid managers of software development projects in planning, managing and controlling the development process of medium- to large-scale software projects. Systems dynamics is used to model and simulate the dynamic process of software development. The software development process is affected by some imprecise and vague variables that are treated as fuzzy variables. The simulation model is integrated with two expert systems. The fuzzy input variables to the system dynamics simulation model are handled by an input expert system having fuzzy logic. This expert system is designed to check the consistency of input variables. The simulation results are analysed by an output expert system having fuzzy logic. This expert system is designed to make recommendations based on experimentation with the simulation model.     

Development of an Intelligent Pneumatic Cylinder for Distributed Physical Human–Machine Interaction

Pneumatic systems are well known for their advantages and simplicity, and have been applied in various applications. This paper presents the development and experimental evaluation of an intelligent pneumatic cylinder and its control system. The cylinder is designed to have an optical encoder, pressure sensor, valve and a Programmable System on a Chip (PSoC) as the central processing unit. The PSoC will handle I²C communication, input and output data from the analogue to digital converter, counter program and pulse width modulation (PWM) duty cycle. An application tool for a distributed physical human–machine interaction is proposed using an intelligent pneumatic cylinder. The system applied 36 links of the actuator to form an Intelligent Chair Tool (ICT). The control methodology presented contains an inner force loop and an outer position loop implemented using a unified control system driven by PWM to an on/off valve. In this research, four control approaches, i.e., position control, force control, compliance control and viscosity control, were constructed and experimented. The physical properties of various objects were also detected by the intelligent cylinder through the detecting function experiment. Finally, an emulation experiment using mass was carried out and the results clearly show the ability of the intelligent cylinder, and the control approaches towards realization of the future ICT application.     

A Framework for Coordinated Control of Multiagent Systems and Its Applications

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.     

Present state and trends in robot technology

Sensory feedback control is utilized in order to make a robot intelligent. This paper describes the present status of intelligent robots by introducing robot systems with force and visual sensors developed in Hitachi, Ltd. The force feedback control named “virtual compliance mechanism” can generate the same functions as any other compliance mechanism such as rcc for inserting operation by setting suitable values to the parameters of the software in the control unit. 3-D visual feedback control method is also described in which the slit light method is used to recognize 3-dimensional position and orientation of the object.