Fault diagnosis in an open world refers to the diagnosis tasks that need to cope with previously unknown faults in the online stage. It faces a great challenge yet to be addressed—that is, the online data of unknown faults may be classified as normal samples with a high probability. In this article, we develop an effective solution for this challenge by using supervised contrastive learning to learn a discriminative and compact embedding for the known normal situation and fault situations. Specifically, in addition to contrasting a given sample with other instances as is the case in conventional contrastive learning methods, our training scheme contrasts the normal samples with negative augmentations of themselves. The negative out-of-distribution data is generated by the Soft Brownian Offset sampling method to simulate the previously unknown faults. Computational experiments are conducted on the Tennessee Eastman Process benchmark dataset and a practical plasma etching process dataset. The proposed method achieves significant improvement compared with four existing methods under three open-set fault diagnosis circumstances, i.e., balanced open-set fault diagnosis, imbalanced fault diagnosis, and few-shot fault diagnosis. This demonstrates its great potentials in real world fault diagnosis applications.

Intelligent fault diagnosis is essential to guarantee the safe operation of industrial processes. And an important issue is how to develop a method to tackle the dilemma where we can only collect limited fault samples. In this paper, we propose a two-stage method based on supervised contrastive learning for imbalanced fault diagnosis tasks. We utilize the supervised contrastive learning technique as it has shown a powerful representation learning ability in previous works. The computational experiments on the Tennessee Eastman dataset show that our proposed two-stage method can achieve improved performance when compared to existing methods.

Imbalanced fault diagnosis becomes increasingly im-portant as the number of fault samples is relatively small in practical situations. Sparse auto-encoder(SAE) has been well addressed in fault diagnosis while it is not suitable for imbalanced fault diagnosis. Cost sensitive learning can be utilized to extend the sparse auto-encoder to cost sensitive sparse auto-encoder(CS-SAE). However, the class weights assigned to different classes are usually unknown in practice. So we propose to use particle swarm optimization(PSO) to optimize the class weights for cost sensitive sparse auto-encoder(PSO-CSSAE). The experiments have shown that the proposed approach consistently outperforms the state of the art on Tennesse Eastman(TE) dataset.

Tacit knowledge is considered as an important information in many kinds of fields, essentially in manufacturing. The tacit knowledge has the ability to improve production efficiency and ensure production quality for production process. However, it is difficulty to represent and apply tacit knowledge. In this paper, a novel knowledge representation model based on statistical data is proposed to visually describe tacit knowledge in production process, which consists of three elements, including attribute, input and output element. Moreover, based on the proposed representation model, a knowledge classification method is developed, which considers the collaboration of statistical data. For solution to classification, a collaborative multi-objective optimization algorithm, the Collaborative Non-dominated Sorting Genetic Algorithm II (CNSGA-II), is designed to optimize classification of tacit knowledge. Furthermore, an industrial case study is reported to show details of the proposed model and method. Finally, an experimental comparison demonstrates that the algorithm can achieve efficient optimized classification with collaborative objectives.

Classification with an imbalanced dataset has been a hard task for a long time. As generative models become more and more attractive, many researchers try to use these methods to address the imbalance problem. In this paper, we employ Bidirectional Generative Adversarial Networks (BIGAN) with improved training architecture as an oversampling method for minority class. We introduce an additional feedback by making use of the encoder$E$and further enhance the training of generator$G$. We conduct the qualitative and quantitative assessment on generated samples, and it shows that under the premise of ensuring diversity, the generated data tend to have less noise. Using it to perform data augmentation in training process, our method has been proved to get a better performance than original GAN-based method on multi-class imbalanced classification task.

Cyber-physical system integrates physical entity and its virtual model, which facilitates intelligent manufacturing a lot. Due to the geometric and non-geometric factors, transmission delay between actual and virtual environment, errors exist between desired position and actual position in real-time movement. Thus the accuracy and efficiency need to be improved. In this paper, a mapping approach of the actual UR10 robot and its virtual model based on long short-term memory neural network is developed to implement the synchronization of the virtual-real UR10 twins' behaviors in cyber-physical system. The virtual model can reflect and control the behaviors of UR10 in real time and vice versa. This method is based on a time recursive structure thus takes the temporal property of trajectory points into account. A prototype system is developed to validate its effectiveness. Experimental validation is conducted to compare the LSTM based calibration method with existing kinematic methods and multilayer perceptron neural net based methods. As demonstrated in the experiment results, the real-time mapping model of the virtual-real UR1O twins' behaviors can be obtained.

The assembly of customized and complex products entails the collaboration of heterogeneous devices and thus raises the need of effectively planning the assembly process according to the dynamic product and environment information. A cloud CPS-based intelligent assembly system is developed in this paper. Under the framework a product assembly model is introduced to describe the hierarchical relationships and mating features between subassemblies. The integrated assembly knowledge model including product structure, spatial position, mating features, and assembly process is presented. Sensory information collected from the real world and product model together form the assembly context. A two-step assembly knowledge reasoning process is then developed, where similar case matching finds the same or similar product structure from the existing assembly instance library, and priority rules guiding completes the final assembly sequence. A prototype system is developed and the gearbox assembly case validates the effectiveness of the proposed models.

The modular design of product platform becomes an indispensable aspect in manufacturing industry with increasingly tailored requirements from customers. Amongst all components in this platform, the modularization mechanism addresses key issue in the design process of manufacturing product. However, the traditional modular methods only solve the modularization with single one or several objectives. In this paper, a novel hierarchical collaborative method based on multi-objective optimization is developed for modularization of product platform, which is integrated various of modular standards, including the product structure, product function, design of products, assembly of products and so on, and trades off the contradiction of these standards to achieve the optimal modularization with multiple hierarchical objectives. Moreover, for solutions to this method, a hierarchical collaborative multi-objective optimization algorithm, the Hierarchical Non-dominated Sorting Genetic Algorithm (H-NSGA), is developed to optimize the modularization with multilevel objectives. Furthermore, an industrial case illustrates the details of the proposed model and algorithm. Finally, the experimental comparison demonstrates that the hierarchical collaborative method and the hierarchical multi-objective optimization algorithm can achieve efficient modular solutions with multiple hierarchical objectives.

Cloud computing technologies have enabled a new paradigm for intelligent manufacturing system which is powered by utilizing distributed resources, such as collaborative robots, simulation engines, advanced algorithms and human resources. As one of the key issues, the mechanism for online kinematics control of serial robotic manipulator presents speed challenge in the cloud-based system. In this research, a kinematics approximation model based on kernel ridge regression is developed for cloud manufacturing environment. To begin with, the model input is generated using trigonometric functions of rotation angles with permutation tricks which significantly reduces statistical error. Then, the approximation model is trained using kernel ridge regression with radial basis function, where both regularization and bandwidth of kernel have been optimized using grid-search. In addition, Universal Robot 10 is adapted as a collaborative robot example in simulation comparison experiments in order to evaluate the performance of the kinematics approximation model. As demonstrated in the experiment results, the proposed modelling approach can effectively support the cloud simulation paradigm and efficiently meet the real-time speed requirement in a distributed manufacturing environment.

Real-time fault detection and fault diagnosis is a key part of building smart factories. Principal component analysis (PCA) and kernel principal component analysis (KPCA) are the two most commonly used methods for process monitoring. Nevertheless, PCA cannot deal with nonlinear data and KPCA cannot be applied for large data sets. To tackle the above two problems, a novel fault detection and fault diagnosis method based on Gaussian-Bernoulli restricted Boltzmann machine (GBRBM) is proposed in this paper. The key idea of our approach is using GBRBM as a nonlinear dimensionality reduction technique and applying the reconstruction error to establish monitoring SPE statistic. The upper limit of the SPE is estimated by kernel density estimation (KDE). If the SPE statistic of an online sample exceeds the control limit, then the fault is detected and the contribution plot is utilized to diagnose the fault. Tennessee Eastman (TE) process is applied to evaluate the fault detection and diagnosis performance of the proposed method. The effectiveness of the proposed GBRBM monitoring method is improved by saving significant memory requirement while obtaining the highest average false detection rate and comparable fault diagnosis reliability, relative to PCA and KPCA techniques.

In Industry 4.0, anomaly detection plays an important role in the process of product quality inspection, where the product data with high dimensions and highly imbalanced distribution give rise to some challenges. To handle these challenges, a novel anomaly detection method based on Gaussian Restricted Boltzmann Machine (GRBM) is proposed. To make it more tractable for training the model, the method performs distinct gradient compensations through integrating the free-energy function into the objective function in two stages of product quality inspection. Extensive experimental studies are respectively carried out on two real-world cases, i.e. wine quality and cigarette product testing. Three state-of art anomaly detection methods and two conventional GRBM methods are used for comparison analysis, and the results demonstrate that our proposed method provides effectiveness and superiority in product quality inspection.

The development of complex products entails the collaborative work of a multidisciplinary team and thus raises the need of effectively supporting knowledge creation and sharing in a collaborative and integrated working environment. A design knowledge model based on hypernetwork is proposed in this paper to facilitate knowledge management and collaborative reasoning in the design and development process. Specifically, the knowledge hypernetwork model is constructed with a designer network, a product network, an issue network and a knowledge unit network. The relationships between various nodes from different networks are identified and defined according to the node properties. On this basis, several topological characteristics of hypernetwork are analyzed and some statistical indicators of the design knowledge hypernetwork are defined. The Bayesian approach is applied to conduct the collaborative reasoning process whereby relevant knowledge units for different design entities are recommended according to the current design tasks and the issues to be resolved. A prototype system is developed and the BIW design case validate the effectiveness of the proposed hypernetwork-based model.

The capability of product family design meets new requirements of customer-driven market nowadays. In order to improve the market competence, enterprises need design a certain product family with better quality, lower cost and less time spending as its best, while these three optimal goals are contradictory in most instances. In this paper, a novel multi-objective collaborative optimization algorithm, Non-dominated Sorting Genetic Algorithm with Hill Climbing algorithm (NSGA-HC), is proposed for product family design, which can trade off local bias of the single-factor algorithm. Secondly, in terms of a better product family designing scheme, a Cycle Subsystem Model with Features is structured, which not only considers product features as an essential factor influencing product performance but also regards each subsystem as individual for product family design. In addition, an industrial field example is demonstrated as a multi-objective optimal problem to validate the proposed model and algorithm. Finally, an experimental comparison shows that this novel algorithm has higher efficiency and avoids local extremum.

Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic alqorithm is proposed based on the elitist archive and the K -means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants' computational requirements in a distributed environment.

The networked manufacturing paradigm is a new and advanced development direction of the manufacturing industry. However, based on SOA (Service-Oriented Architecture), service failures and service errors are inevitable in the collaborative manufacturing service network. Aiming at solving this problem, this paper proposes a reliability optimization method for collaborative manufacturing service network based on the importance of service nodes, including the complex network modeling method of collaborative manufacturing service, the importance ranking algorithm of manufacturing service nodes based on Leaderank algorithm and the reliability optimization process. The simulation results show that this method can mine the key nodes in the collaborative manufacturing service network and improve the reliability of the collaborative manufacturing service network with less fault-tolerant loss.

Collaborative design entails sharing knowledge in an effective way and from a variety of sources, which raises the need of developing technology-enabled systems that can improve decision making by streamlining complex analysis and synthesis processes and facilitating communication and negotiation in the design process. While previous research predominantly focuses on a particular aspect of design knowledge, an integrated method is required to develop such systems especially in the context that the amount of data has become huge. To address such a gap, this work aims to address the two views of design knowledge, namely codification and personalisation. The main idea is to facilitate the collaborative design process with a model-based solution which uses computational methods based on codified knowledge while incorporating rich personal experience of problem-solving for decision-making. This solution requires an effective knowledge representation that facilitates the integration of explicit and tacit knowledge in the computational processes. In addition, it relies on effective system integration during these processes. This paper reports the very first step towards such a solution, describing the system integration issues and the user interaction issues in the development of a prototype system.

3D printing is a kind of important method in the field of rapid prototyping and customized production. In order to achieve the purpose of production integration and product customization of 3D printing, this paper propose a 3D printing cloud integrated platform architecture based on cloud manufacturing. Moreover, how to evaluate and select the services of different distributed 3D printing terminals in cloud platform also requires further in-depth study. To address this issue, a service evaluation model of distributed 3D printing based on cloud manufacturing is designed and proposed in this paper. This model can be used to characterization the evaluation information of each dimension by fuzzy theory, and the evaluation results of the service can be accurately quantified by fuzzy number operation. The illustrative example show that the model and the evaluation method can effectively quantify the service quality, improve the quality and accuracy of services selection.

Modern product development becomes increasingly collaborative and parallel, which raises the need for sharing simulation resources in a multi-tenant simulation environment. However, the service requests and scheduling policies of tenants differ since system resources are shared by tenants. In order to deal with this issue, the framework of monitoring, detecting and scheduling system performance is proposed. As SLA is produced as the contract for negotiations between tenants and the service provider, the WS-Agreement specification is applied and improved to represent the service contents and qualities. Based on the SLA template, a dynamic resource distributing mechanism is realized. The Satisfaction Index is introduced into the scheduling of distributed resources, in which the weighted formula is determined by priorities. A case study has been undertaken and preliminary results show that the proposed solution can effectively support the cloud simulation and efficiently meet tenants' personal requirements.

Schedule and cost factors are largely related to the successful performance of a complex product manufacturing or acquisitions processes. Nonetheless, traditional statistics methods cannot effectively meet the requirement of the complex product management. In this paper, the Weibull distribution with modified least squares estimation (LSE) method is utilized to estimate the integration of schedule and cost. Firstly, the explanation of 3-parameter Weibull probability distribution function for estimation the integrated schedule and cost is demonstrated. In addition, the multi-peaks Weibull distribution is proposed for large complex product modeling consisted of several subsystems. Moreover, a modified LSE method for Weibull parametric analysis is proposed reflecting the latest schedule-cost integration status where the old data will be forgotten with the data accumulation, instead of treating all the data equally. Last but not least, the comparison with other statistics methods demonstrates the effectiveness of method in this research. The illustration using complex system samples also shows that the estimation of schedule and cost integrated based on multi-peaks Weibull distribution is viable and efficient.

The development of product becomes increasingly collaborative and parallel, which makes the need for design reuse more critical. This paper proposes a framework to enable effective development of complex product by supporting the management of design knowledge, which refers to metadata, structural, functional and behavioral information. The multi-level knowledge represented by Base Object Model can be seen as special cases of web services, as BOMs are fundamentally XML documents. Surrounding the effective management of knowledge, the method for the retrieval of multi-level knowledge is developed including the process of retrieving, matching and assembling. Since the knowledge is stored in the form of a tree structure, the depth-first search (DFS) and breadth-first search (BFS) are applied to the retrieval algorithm and similarity is used to calculate the match degree. The framework is implemented in the development of an example missile, which demonstrates that its useful for the re-use of design knowledge.

Computer aided engineering analysis is widely applied in industry with the rapid development of computer power and simulation technology. In particular, the design and analysis of complex systems entails multidisciplinary collaborative modeling and thus requires enormous knowledge and experience. However, current work on managing design knowledge is mainly focused on capturing knowledge about the design solutions and processes while little work has been done on investigating the knowledge about the models and processes for engineering analysis. This research is aimed at addressing this gap by developing the methodology for effective modeling and re-using the knowledge generated in the engineering analysis process. Specifically, a system framework is proposed to identify the methodology for managing engineering analysis together with the key issues involved. A knowledge model is developed to describe complex engineering analysis problems as well as their solving processes. A case study has been undertaken and preliminary results show that the proposed solutions can effectively describe the models and processes for engineering analysis whilst this preliminary research opens up further opportunities for doing further work.

In order to improve the efficiency of multi-resolution modeling and simulation, Base Object Model (BOM) based multi-resolution model for federated simulation is proposed in this paper. The whole process includes conceptual modeling design, component design, component assembly and federal simulation. BOM-based conceptual models are divided into two categories: basic BOM and the Resolution Related BOM (RR-BOM). Four-Step Modeling method is applied for those two types of BOMs. The completed BOMs are converted to their corresponding component implementations (BCIs). BOM library and BCI library are set up for model reuse. During the components assembly and federated simulation, Dual Engine Simulation (DES) is designed for multi-resolution simulation. Each federate has an Internal Exchange Service Server (IESS) for the internal component management. IESS provide time management, data management, resolution control and other services. The external supporting environment is Run Time Infrastructure (RTI). This method of modeling and simulation has been applied in industrial projects and greatly improved the efficiency for multi-resolution modeling for federated simulation.

Multidisciplinary collaborative simulation is a new paradigm to enhance the analysis of complex engineering systems more accurately in the systematic level, which enabling the interaction of subsystems models constructed by different CAE software tools in a time-synchronized and high fidelity way. This paper presents a modular method to implement the multidisciplinary collaborative simulation for complex engineering systems in a distributed environment. The modular decomposition is based on the configured subsystems in engineering intuition of corresponding disciplines. The global system is described by inter-connections between subsystems with block representation, thus the different simulation modules are interrelated with the corresponding inputs and outputs. The modular simulation approach allows for independent and parallel modeling of each subsystem and has the advantages of a better modularity. A component-based infrastructure is proposed to integrate the distributed, heterogeneous CAE tools. A modular simulation system which can support the integration of distributed simulation models at run-time is developed, involving the implementation technologies of interrelated modules, disciplinary model transformation and simulation time advancement. Modular modeling and simulation of a rolling stock is demonstrated as a typical case study. Numerical results of the modular simulation demonstrate the effectiveness of this approach to multidisciplinary collaborative simulations for complex engineering systems.

Cloud Simulation Platform is put forward based on the concept of cloud computing. It provides relative services of modeling and simulation as cloud services with the characteristics of good capability in sharing, collaborating, fault-tolerating and migrating multi-granularity resources on demand by multi-user, etc. As cloud's spirits, the pay-as-you-go policy should be the basis of charging policy of the cloud service. As a profitable commercial exploration, this article will discuss the monetization model of the platform. After that a comprehensive pricing and charging strategy based on the feature of resources of the platform will be introduced. Then a useful scheme of adjusting the pricing parameters will be given. Finally through an application example, it's shown that the effective using of the monetization model and strategy ensures the interests of both consumers and service providers.

This article describes two multi-disciplinary modeling and simulation approaches of complex products, points out the language advantage of physical simulation modeling language modelica. By describing the class structure of the language, in particular, the structural characteristics of the equation, It summarizes the modelica language modeling process, and makes analysis for the interpreter and the solver. It graphically details the architecture, hierarchical modeling, high level modeling tools of collaborative simulation platform COSIM. Against the language specific equation expression principle and the characteristics of simulation platform, the integration method of language and platform was creatively proposed, and the key technologies are needed.

Multidisciplinary modeling and simulation of complex engineering systems are generally realized by an integrated approach in which the global system is decomposed into some coupling models of subsystems and then for collaborative computation. However, the coupling relationship of design variables between different disciplinary models usually increases the complexity of collaborative computation. It is a pivotal issue to solve these models in parallel for collaborative simulation. In this paper, numerical computation for multidisciplinary coupling models is analyzed. A method of variable-step collaborative simulation based on the local truncation error estimation is presented. A case study demonstrates the algorithm performance in both efficiency and accuracy.

Multidisciplinary models of complex products are developed by heterogeneous CAE tools in a distributed simulation environment. It is a key technology to integrate CAE models together, making them work collaboratively. In this paper, a modular method is proposed. First, a stage-by-stage modeling method is presented to reduce the interaction between modeling and simulation. Then, HLA and web service technology are employed to facilitate the simulation process. To fulfill the HLA based simulation, the development method of a general HLA adaptor is illustrated. A case study demonstrates the effectiveness of this approach.

Collaborative simulation is an important technology for complex engineering systems. The challenges of collaborative simulation are especially in model integration, interaction control and time advancement where a variety of collaborative disciplinary subsystems for time-synchronized running are handled dynamically in a distributed environment. In this paper, the formulized paradigm of multidisciplinary collaborative simulation for complex engineering systems is principally analyzed. A strategy for collaborative simulation in a distributed environment is proposed to implement an integrated framework leveraging HLA and web services technology. A minor-step-based interaction algorithm is presented to improve the precision. The interaction model definition and I/O data processing mechanism of the adaptor is studied. The prototype system and case studies are developed to demonstrate the effectiveness of this approach.

Simulation technology is widely applied in product development to make forecasts on the performance of design solutions for which it is difficult or not economical to develop physical prototypes. There are some circumstances, e.g. the development of mechatronic products, under which a number of simulations need to be used together. Therefore, it is necessary to develop a collaborative simulation platform which enables design engineers in a multidisciplinary team to develop, share, reuse, and integrate computational models. Difficulties in developing such a platform include using multiple simulation tools, the runtime integration of distributed models, the sharing of proprietary information, etc. Among these difficulties, run-time integration of distributed models is the one significantly affecting the accuracy, efficiency and stability of a simulation. This paper describes how multidisciplinary collaborative simulation problem can be solved, discusses different methods for run-time interaction, and presents a novel method that is proposed and implemented in a prototype system for collaborative simulation. Further evaluation on these methods shows that the novel method has better performance in terms of both accuracy and efficiency.

Collaborative simulation has become an important process of complex product development and works as a key technology to improve product dynamic characteristics and synthetic performances. This paper firstly introduces a model description of collaborative simulation, and gives a systematic analysis on all kinds of factors which affect performance of algorithm including speed, precision and convergence, etc. Then a basic algorithm of collaborative simulation and an improved edition of algorithm from the perspective of step size control are proposed, the later of which can increase simulation speed while guaranteeing precision and convergence. An example is given to verify its validity.

Virtual Prototyping widely used in complex product development is concerned with collaboration of multidiscipline and cooperation between several development stages, such as requirement analysis, conceptual design, detail design and engineering design. To reduce the complexity and time span of virtual prototype modeling, an innovative kind of Multi-Granularity Modeling (MGM) Framework which gives solutions of inheritance and implementation problems of multi-phase modeling is proposed. In our method which introduces the concept of Granularity Model (GM) and its decomposition and evolvement specifications, key model information can extend automatically across multiple granularity models in different stages. A high-level software specification that illustrates an implementation in collaborative product design of this framework is described. Finally, the framework is exemplified via an attacking system which adopts multi-granularity modeling methods with UML (Unified Modeling Language) profile and middleware based on XML (eXtensible Markup Language).

This paper proposes a novel combinative algorithm for communication among domain models in multidisciplinary collaborative simulation. To implement this algorithm in collaborative simulation platform effectively, it is required to use a proper model encapsulation method and a matched RTI control strategy. A theoretical analysis is provided to imply its higher precision toward traditional combinative algorithm based on discrete mode offered by commercial simulation interface. Then a case study is presented to show its validity as well as discrete mode. Convergence and convergence order discussion is to be made in further research.

Collaborative simulation is proposed as a technique to support design evaluation with higher fidelity by addressing the interacting phenomena during the development of the complex product. To facilitate distributed simulation, high level architecture (HLA) was proposed and evolved as a standard with its specification, interfaces and data model. HLA provides simulation management for distributed component- based simulation and is identified as a candidate standard for collaborative simulation. Nevertheless, bottlenecks caused by the inherent deficiency of HLA restrict the applications of HLA-based collaborative simulation. In our presented research, we try to remedy the deficiency by integrating Web services technology to HLA-based simulation systems. Web services technology is adopted as the infrastructure for heterogeneous resources wrapping while HLA is the backbone for simulation scheduling. The framework and key issues for our solution are discussed in detail and a case study is performed. The prototype system and case study demonstrate that integrating web services with HLA is feasible in supporting collaborative simulation in the internet distributed environment with heterogeneous computing platforms.

This paper presents a Web Service and HLA based multidisciplinary collaborative approach for complex product design in distributed network environment. A HLA and Web Service based framework to support multidisciplinary modeling and collaborative simulation is presented Then Its key modules including session manager and service encapsulation are discussed The framework supports collaborative designing on heterogeneous platforms. A typical prototype system of locomotive development supporting collaborative simulation is implemented to verify the feasibility of our solution. The experimental results shows that Web Service and HLA based approach can effectively support collaborative design under the distributed M&S environment.

Collaborative product development is concerned with various experiences, knowledge, teams, tools, processes, etc. Effective communication and cooperation of these factors are the key issues that challenge the current computational architectures. A service oriented approach is proposed by us to integrate available resources from a repository in open standard. In our solution, functionality of the any computational resource is encapsulated as web services with a variety of granularities. The services are defined, registered, consumed and orchestrated in service oriented architecture (SOA). Key issues of our solution are studied. A prototype system supporting collaborative simulation is implemented to verify the feasibility of the solution. The result shows that service oriented computing is promising for collaborative product development.

The development of complex product is essentially concerned with multidisciplinary knowledge and requires computer supported cooperation among the designers. A multidisciplinary collaborative modeling and simulation (M&S) technology is an effective approach for the design and development of the complex product. The challenge now faced is how to apply the multidisciplinary M&S in the systematic level of complex product development. The key issue is how to integrate the heterogeneous, autonomous software tools in a distributed environment. In this paper, we propose a high level architecture (HLA) and web services based framework for multidisciplinary collaborative design in a distributed environment. The process of multidisciplinary collaborative design project is principally analyzed. A modeling and simulation platform with the focus of multidisciplinary collaborative design is present. Besides its key technologies of HLA and web services based integration are studied. The interoperability issue to this research is also discussed.

This paper presents the design of a collaborative simulation framework based on HLA and Web service to make good use of the benefits of these two infrastructures. The framework leverages the Web-based technology to provide remote communication with user clients while using HLA for simulation traffic. Integration method, theoretical thoughts, technical considerations and up to date development of HLA and Web service are introduced in detail. To extend the framework towards supports for a knowledge-based complex product design system, an open and extensible environment based on the framework is put forward. Future work is discussed and the conclusion is given at the end of the paper

In this paper, the architecture of collaborative modeling and simulation for the virtual prototyping of complex products is presented primarily. An approach of multidiscipline modeling towards HLA-based distributed collaborative design is studied. A HLA and Web services based framework and its key technologies of the distributed network environment to support multi-discipline modeling and collaborative simulation is discussed. A typical application demonstration of multidisciplinary collaboration simulation for the virtual prototyping of locomotive development is introduced

This paper presents a method of matrix application to the multidisciplinary cooperative design for complex products. Based on classifying the interdependency relationship between different design activities and describing the definition & types of DSM (design structure matrix), the design flow diagram is illustrated by the matrix of DSM. Then a new DSM is gained by analyzing and partitioning the original DSM, at the same time, a new information flow and design flow diagram of reducing the coupled activities is proposed. Finally, the discussions and conclusions for the application of DSM in multidisciplinary cooperative design process are given at the end of this paper.