Research on Product Data Management Based on Multi-agent System

PDM (product data management) is one kind of techniques based on software and database, which integrates information and process related to products. But it is not enough to perform the complication of PDM in enterprises. Then the mechanism to harmonize all kinds of information and process is needed. The paper introduces a novel approach to implement the intelligent monitor of PDM based on MAS (multi agent system). It carries out the management of information and process by MC (monitor center). The paper first puts forward the architecture of the whole system, then defines the structure of MC and its interoperation mode.

Statecharts for Distributed Product Data Management System Modelling

Product data management (PDM) has been accepted as an important tool for the manufacturing industries. In recent years, more and mor e researches have been conducted in the development of PDM. Their research area s include system design, integration of object-oriented technology, data distri bution, collaborative and distributed manufacturing working environment, secur ity, and web-based integration. However, there are limitations on their rese arches. In particular, they cannot cater for PDM in distributed manufacturing e nvironment. This is especially true in South China, where many Hong Kong (HK) ma nufacturers have moved their production plants to different locations in Pearl R iver Delta for cost reduction. However, they retain their main offices in HK. Development of PDM system is inherently complex. Product related data cover prod uct name, product part number (product identification), drawings, material speci fications, dimension requirement, quality specification, test result, log size, production schedules, product data version and date of release, special tooling (e.g. jig and fixture), mould design, project engineering in charge, cost spread sheets, while process data includes engineering release, engineering change info rmation management, and other workflow related to the process information. Accor ding to Cornelissen et al., the contemporary PDM system should contains manageme nt functions in structure, retrieval, release, change, and workflow. In system design, development and implementation, a formal specification is nece ssary. However, there is no formal representation model for PDM system. Theref ore a graphical representation model is constructed to express the various scena rios of interactions between users and the PDM system. Statechart is then used to model the operations of PDM system, Fig.1. Statechart model bridges the curr ent gap between requirements, scenarios, and the initial design specifications o f PDM system. After properly analyzing the PDM system, a new distributed PDM (DPDM) system is proposed. Both graphical representation and statechart models are constructed f or the new DPDM system, Fig.2. New product data of DPDM and new system function s are then investigated to support product information flow in the new distribut ed environment. It is found that statecharts allow formal representations to capture the informa tion and control flows of both PDM and DPDM. In particular, statechart offers a dditional expressive power, when compared to conventional state transition diagr am, in terms of hierarchy, concurrency, history, and timing for DPDM behavioral modeling.

Establishment of Person and Enterprise Process Model in Product Data Management System

Before the implementation of product data management (PDM) system, person model and enterprise process model (EPM) must be firstly established. For the convenience of project management, all the related users must be allocated to the “Person User Role Group” net. Based on the person model and the direction of information flow, the EPM is established subsequently. The EPM consists of several release levels, in which the access controls are defined. The EPM procedure shows the blueprint of the workflow process structure. The establishment of person model and EPM in an enterprise has been instanced at the end of this paper.

Research on the J2EE-based product database management system

The basic frame and the design idea of J2EE-based Product Data Management (PDM) system are presented. This paper adopts the technology of Object-Oriented to realize the database design and builds the information model of this PDM system. The integration key technology of PDM and CAD systems are discussed, the isomerous interface characteristics between CAD and PDM systems are analyzed, and finally, the integration mode of the PDM and CAD systems is given. Using these technologies, the integration of PDM and CAD systems is realized and the consistence of data in PDM and CAD systems is kept. Finally, the Product Data Management system is developed, which has been tested on development process of the hydraulic generator. The running process is stable and safety.

How to Implement Product Data Management

introduced are the main implementation contents and brought out is a cycle optimal PDMimplementation methodology by phases through studying requirements of enterprises' datamanagement and summarizing the existing method. The implementation contents include documentsmanagement product structure and configuration management, product classification and codingmanagement, workflow management and software encapsulation. It divides the PDM implementationinto preparing, software selechon and training, implementation plan making, concrete implementingand evaluating. By the cycle of the last two pheses, the successful implementation of PDM can beobtained. This methodology has been used in the CIMS project of two companies and proved to bepractical.

System View of Product Data Management and Its Application

A product data management system for a manufacturing enterprise is to make sure that the proper product data can be communicated to the right people at the right time.This paper describes a system analysis paradigm for data analysis in a product data management(PDM)development.Three aspects of the paradigm,i.e.,function,structure and behavior are rep- resented.The use of the paradigm explains why so many kinds of objects are necessary in a commercial database matrix and what models are available for developing a PDM application.As another result,a lot of models are derived from the analysis of product data system paradigm to model product data and PDM database definitions.

An Open Architecture and Implementation for Product Data Management

In this paper, an open architecture and its implementation for product data management are presented. The system architecture, product data definition model, and a set of components of the architecture are discussed in detail. Especially, the principle and some mechanism of one of the components, an object oriented database management system GH-EODB, are discussed in more detail. The architecture is extensible and adaptable.

Key technology of ship product data exchange based on STEP

In this paper, in order to implement the share and exchange of the ship product data, a new kind of global function model is established. By researching on the development and trend of the application of ship STEP (standard for the exchange of product model data) standards, the AIM (application interpreted model) of AP216 is developed and improved as an example, aiming at the characteristics and practical engineering of ship industry in our country. The data exchange interfaces are formed based on STEP in the CAD/CAM for the ship by all function modules and shared databases under the global function model. The share and exchange of all information and data are solved in the design, manufacture and all life-cycle of ship products among different computer application systems. The research work makes foundation for the ship industry informatization.

A web product data management system based on Simple Object Access Protocol

A new web product data management architecture is presented. The three-tier web architecture and Simple Object Access Protocol (SOAP) are combined to build the web-based product data management (PDM) system which includes three tiers: the user services tier, the business services tier, and the data services tier. The client service component uses the server-side technology, and Extensible Markup Language (XML) web service which uses SOAP as the communication protocol is chosen as the business service component. To illustrate how to build a web-based PDM system using the proposed architecture, a case PDM system which included three logical tires was built. To use the security and central management features of the database, a stored procedure was recommended in the data services tier. The business object was implemented as an XML web service so that client could use standard internet protocols to communicate with the business object from any platform. In order to satisfy users using all sorts of browser, the server-side technology and Microsoft ASP.NET was used to create the dynamic user interface.

Product Data Model for Performance-driven Design

When designing large-sized complex machinery products, the design focus is always on the overall per- formance; however, there exist no design theory and method based on performance driven. In view of the defi- ciency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of ＂Requirement-Function-Structure＂ to construct a new five-domain design theory of ＂Client Requirement-Function-Performance-Structure-Design Parameter＂. To support design practice based on this new theory, a product data model is established by using per- formance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parame- ters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train＇s parameter design.

Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques

In this article, the relationship between the knowledge of competitors and the development of new products in the field of capital medical equipment has been investigated. In order to identify the criteria for measuring competitors’ knowledge and developing new capital medical equipment products, marketing experts were interviewed and then a researcher-made questionnaire was compiled and distributed among the statistical sample of the research. Also, in order to achieve the goals of the research, a questionnaire among 100 members of the statistical community was selected, distributed and collected. To analyze the gathered data, the structural equation modeling (SEM) method was used in the SMART PLS 2 software to estimate the model and then the K-MEAN approach was used to cluster the capital medical equipment market based on the knowledge of actual and potential competitors. The results have shown that the knowledge of potential and actual competitors has a positive and significant effect on the development of new products in the capital medical equipment market. From the point of view of the knowledge of actual competitors, the market of “MRI”, “Ultrasound” and “SPECT” is grouped in the low knowledge cluster;“Pet MRI”, “CT Scan”, “Mammography”, “Radiography, Fluoroscopy and CRM”, “Pet CT”, “SPECT CT” and “Gamma Camera” markets are clustered in the medium knowledge. Finally, “Angiography” and “CBCT” markets are located in the knowledge cluster. From the perspective of knowledge of potential competitors, the market of “angiography”, “mammography”, “SPECT” and “SPECT CT” in the low knowledge cluster, “CT scan”, “radiography, fluoroscopy and CRM”, “pet CT”, “CBCT” markets in the medium knowledge cluster and “MRI”, “pet MRI”, “ultrasound” and “gamma camera” markets in the high knowledge cluster are located.

Kenyan Counties Geospatial Data Knowledge to Monitor Crop Production

Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

An improved data space inversion method to predict reservoir state fields via observed production data

A data-space inversion(DSI)method has been recently proposed and successfully applied to the history matching and production prediction of reservoirs.Based on Bayesian theory,DSI can directly and effectively obtain good posterior flow predictions without inversion of geological parameters of reservoir model.This paper presents an improved DSI method to fast predict reservoir state fields(e.g.saturation and pressure profiles)via observed production data.Firstly,a large number of production curves and state data are generated by reservoir model simulation to expand the data space of original DSI.Then,efficient history matching only on the observed production data is carried out via the original DSI to obtain related parameters which reflects the weight of the real reservoir model relative to prior reservoir models.Finally,those parameters are used to predict the oil saturation and pressure profiles of the real reservoir model by combining large amounts of state data of prior reservoir models.Two examples including conventional heterogeneous and unconventional fractured reservoir are implemented to test the performances of predicting saturation and pressure profiles of this improved DSI method.Besides,this method is also tested in a real field and the obtained results show the high computational efficiency and high accuracy of the practical application of this method.

Development and Application of a Production Data Analysis Model for a Shale Gas Production Well

This paper presents the development and application of a production data analysis software that can analyze and forecast the production performance and reservoir properties of shale gas wells.The theories used in the study were based on the analytical and empirical approaches.Its reliability has been confirmed through comparisons with a commercial software.Using transient data relating to multi-stage hydraulic fractured horizontal wells,it was confirmed that the accuracy of the modified hyperbolic method showed an error of approximately 4%compared to the actual estimated ultimate recovery(EUR).On the basis of the developed model,reliable productivity forecasts have been obtained by analyzing field production data relating to wells in Canada.The EUR was computed as 9.6 Bcf using the modified hyperbolic method.Employing the Pow Law Exponential method,the EUR would be 9.4 Bcf.The models developed in this study will allow in the future integration of new analytical and empirical theories in a relatively readily than commercial models.

A new product family mining method based on PLM database

Product family(PF) is the most important part of product platform. A new method is proposed to mine PF based on multi-space product data in PLM database. Product structure tree(PST) and bill of material(BOM) are used as the data source. A PF can be obtained by mining physics space, logic space and attribute space of product data. In this work, firstly, a PLM database is described, consisting of data organization form, data structure, and data characteristics. Then the PF mining method introduces the sequence alignment techniques used in bio-informatics, which mainly includes data pre-processing, regularization, mining algorithm and cluster analysis. Finally, the feasibility and effectiveness of the proposed method are verified by a case study of high and middle pressure valve, demonstrating a feasible method to obtain PF from PLM database.

Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.

Estimations of Net Primary Productivity and Evapotranspiration Based on HJ-1A/B Data in Jinggangshan City, China

Net primary productivity(NPP) and evapotranspiration(ET) are two key variables in the carbon and water cycles of terrestrial ecosystems.In this study,to test a newly developed NPP algorithm designed for HJ-1 A/B data and to evaluate the usage of HJ-1 A/B data in the quantitative assessment of environments,NPP and ET in Jinggangshan city,Jiangxi province,are calculated using HJ-1 A/B data.The results illustrate the following:(1) The NPP and ET in Jinggangshan city in 2010 both show obvious seasonal variation,with the highest values in summer and the lowest values in winter,and relatively higher values were observed in autumn than in spring.(2) The spatial pattern indicates that the annual NPP is high in the southern area in Jinggangshan city and low in the northern area.Additionally,high NPP is distributed in forests located in areas with high elevation,and low NPP is found in croplands at low elevations.ET has no significant north-south difference,with high values in the southeast and northwest and low values in the southwest,and high ET is distributed in forests at low elevations in contrast to low ET in forests in high-elevation areas and in cropland and shrub grassland in low-elevation areas.(3) Compared to the MODIS product,the range of HJ-1 NPP is larger,and the spatial pattern is more coincident with the topography.The range of HJ-1 ET is smaller than that of the MODIS product,and ET is underestimated to some extent but can reflect the effect of topography.This study suggests that the algorithm can be used to estimate NPP and ET in a subtropical monsoon climate if remotely sensed images with high spatial resolution are available.

Comparison of multiple salinity datasets:upper ocean salinity and stratification in the tropical Pacific during the Argo period

Ocean salinity is an important variable that affects the ocean stratification.We compared the salinity and ocean stratification in the tropical Pacific derived from the Argo(Array for Real-time Geostrophic Oceanography data),EN4(Ensemble 4 analysis),SODA(the Simple Ocean Data Assimilation reanalysis),IAP(Institute of Atmospheric Physics data),and ORAS4(Ocean Reanalysis System 4)over 2005–2017.Results show that the spatial distribution of climatological mean of sea surface salinity(SSS)in all the products is consistent,and the low salinity region showed large deviation and strong dispersion.The Argo has the smallest RMSE and the highest correlation with the ensemble mean,while the IAP shows a high-salinity deviations relative to other datasets.All the products show high positive correlations between the sea surface density(SSD)and SSS with respect to the deviations of climatological mean from ensemble mean,suggesting that the SSD deviation may be mainly influenced by the SSS deviation.In the aspect of the ocean stratification,the mixed layer depth(MLD)climatological mean in the Argo shows the highest correlation with the ensemble mean,followed by EN4,IAP,ORAS4,and SODA.The Argo and EN4 show thicker barrier layer(BL)relative to the ensemble mean while the SODA displays the largest negative deviation in the tropical western Pacific.Furthermore,the EN4,ORAS4,and IAP underestimate the stability in the upper ocean at the depths of 20–140 m,while Argo overestimates ocean stability.The salinity fronts in the western-central equatorial Pacific from Argo,EN4,and ORAS4 are consistent,while those from SODA and IAP show large deviations with a westward position in amplitude of 0°–6°and 0°–10°,respectively.The SSS trend patterns from all the products are consistent in having ensemble mean with high spatial correlations of 0.95–0.97.