Search

Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts. Tool wear under the variable working conditions and the above factors show a complex coupling and timing correlation, which makes it challenging to predict tool wear under variable working conditions. This article aims to resolve this issue. First, we establish a unified representation of working condition factors. The stacked autoencoder (SAE) model adaptively extracts tool wear features from the machining signal. The extracted wear features and respective working conditions then combine into a working condition feature sequence for predicting tool wear. Finally, the advantages of the long short-term memory (LSTM) model to solve memory accumulation effects learn the regular wear pattern of the working condition feature sequence to realize the prediction of the tool wear. An experiment illustrates the effectiveness of the proposed method.

To realize the reuse of process design knowledge and improve the efficiency and quality of process design, a method for extracting thinking process rules for process design is proposed. An instance representation model of the process planning reflecting the thinking process of technicians is established to achieve an effective representation of the process documents. The related process attributes are extracted from the model to form the related events. The manifold learning algorithm and clustering analysis are used to preprocess the process instance data. A rule extraction mechanism of process design is introduced, which is based on the related events after dimension reduction and clustering, and uses the association rule mining algorithm to realize the similar process information extraction in the same cluster. Through the vectorization description of the related events, the final process design rules are formed. Finally, an example is given to evaluate the method of process design rules extraction.

TA126 solve the job shop dynamic scheduling problem with disruptions, an integrated dynamic scheduling method based on shared mental models is proposed in this paper. Each manufacturing unit in the workshop is mapped into an intelligent agent with the individual mental model for the specific scheduling scenarios. After disturbance occurs, the Q-learning reinforcement learning method is adopted to achieve conflict resolution and consensus consultation among agents, and produce the globally optimized scheduling scheme.

Cognitive maps represent decision makers’ mental maps and their strategies, which are always uncertain, ambiguous and hard to be formalized. In order to make intelligent design decision-making, a Bayesian approach for constructing cognitive maps is proposed in this paper. The cognitive map is modeled compatible with a Bayesian Network. Then cause-effect mapping rules between design elements embedded in cognitive maps can be made explicit by means of network structure learning. A score-based greedy search algorithm is implemented for network structure learning, in which penalized mutual information is defined as the scoring metric and hill-climbing search algorithm is used to find the highest-scoring network. The eliminating loop operator is introduced into the algorithm according to the restriction of the edge directionality.

Master-slave P2P mapping principle proposed in our previous work [ is a semantic P2P mapping paradigm with modularity and loosely coupled characteristics. The intent of this paper is to define a common case study of this paradigm for the semantic information integration. The domain of the case study is a semantic P2P information integration system called SGII[, i.e., system that help in information coordinating and interoperating by orchestrating the content and formalization expression of master-slave P2P mapping between elements from different peer node models which represent the data exposed (shared) by data sources. Furthermore, an illustrative example of master-slave P2P mapping paradigm is given to explain how the mappings are implemented and to demonstrate the paradigm can hence be applied in semantic information integration scenarios.

The power source subsystem of satellite is the basic guarantee of the normal orbit for the satellite that provides to energy for the entire satellite. The fault knowledge modeling method for power source subsystem of satellite based on ontology was proposed. Firstly, the failure modes and effects analysis (FMEA) of the power source subsystem of satellite was analyzed, and the fault knowledge was got by extracting key words. Secondly, the ontology of fault knowledge was established. Thirdly, the inference engine based on ontology was realized by analyzing the hierarchical structure of the reasoning system. Only the ontology needs to be created for the fault diagnosis system, which greatly reduces the knowledge creation time.

SDDG (semantic desktop data grid) is a novel semantic peer-to-peer architecture built on top of previous work [1][2] to fully leverage the enterprise information resides in both enterprise and personal information sources. In SDDG, the combination of P2P data integration paradigm, semantic integration principle and grid service oriented framework provides both the required level of flexibility and scalability, and the high interoperability on information and semantic level. The semantic P2P model of SDDG is discussed in detail from the aspects of the definition of peer node, the master-slave P2P mapping mechanism, the classified P2P query. A semantic service oriented implementation framework based on OGSADAI is also discussed in this paper.

Machining error prediction is an important advance towards optimum machining. In this paper, we define the prediction problem as a classification task where process characteristics impacting on machining errors are attribute variables and error results are class variables. To deal with non-linearity and invisible correlations among process characteristics, the Bayesian network-based classifier learning from historic information is applied to extract hidden knowledge between process characteristics and resulting machining errors. Given an instance from the set of process characteristics describing, the posterior probability of each machining error result can be calculated using Bayes rules. An experiment for surface roughness (Ra) prediction on Al 7055 high-strength aluminum alloy in high speed cutting was presented, in which three Bayesian network-based classifiers, namely Naive Bayesian Classifier, Tree-Augmented Network Classifier and General Bayesian Network Classifier, learnt from the experimental dataset simultaneity. Up to 84.6 % accuracy was achieved by GBNC which was selected as the surface roughness prediction model. Therefore, the Bayesian network-based classifier is a valid method for machining error prediction.

Manufacturing grid is a new pattern developed to answer unprecedented challenges in manufacturing industry, one of the most important foundations of which is encapsulating or modeling manufacturing resources into grid service. But this kind of grid service is restricted by ambiguous and insufficient service description. This paper presents an ontology model, called M-service profile ontology, which provides the framework for explicitly encoding semantics of domain-specific engineering information. The model is generated by extending OWL-S with two classes. One is ManufacturingProfile class which provides formal engineering indexes needed during collaborative work. Anther is the QoSProfile class which provides a mechanism to represent quality of service. This ontology model enriches semantic descriptions of grid service which will be very conducive for later service discovery and management.

Based on investigating challenges of product knowledge sharing in distributed environment, a context-sensitive product knowledge sharing architecture is proposed. Through a discussion of fundamental model for elucidating and characterizing context in knowledge sharing, and the mechanism of knowledge utilizing, annotating and clustering based on this knowledge context model, we present a context-sensitive query mechanism to spread query over the whole knowledge sharing network by establishing query graph and implementing context-sensitive routing. A prototype system is implemented to prove technical feasibility of the proposed approach.

Building ontology from scratch need identify the basic concepts, and their attributes and relationships. In terms of information integration, draft concepts, attributes and relationships can be directly captured by processing schemas of data sources. In this context, we present a hybrid semantic discovery approach, which is an extension of our previous work[1][2] to capture concepts, attributes and relationships from relative schemas. The approach consists of two stages: column oriented matching phase and schema oriented matching phase. Column oriented matching phase focuses on catching a set of attributes of potential concepts by schema columns similarity computing using a composite matcher, and columns clustering using neural network matcher. Schema oriented matching phase categorizes schemas (regarded as potential draft concepts) into clusters or explicit concepts, and attaches relative attributes to the concept through high-dimensional sparse clustering process, which computes the relationship or semantic distance between potential concepts by comparing the corresponding attributes set of each potential concept. The explicit concepts with attributes discovered by our approach can be used as draft material or seeds for further ontology modeling.

Collaborative product development has become important business strategy. In this paper, a framework for autonomous collaborative product development, in which a set of intelligent agents handle major interact tasks based on ontology, is presented. By wrapping engineering applications using agents, the framework applies generalized knowledge repository to leverages relative resources, knowledge and designer in a collaborative way. The detailed application functionality and structure of agents is studied. Finally, communication process between agents is provided, which identifies distributed participators enable to share heterogeneous data via messages of agents.

Enterprise need integrate all relevant heterogeneous databases in semantic level to react to requirements of their customers, collaborate with partners, identify and exploit new business opportunities quickly and effectively. In this context, a semantic model based Integration platform is developed to cover the whole integration process. The platform includes three modules corresponding to different integration stages: 1) metadata abstraction module is used to get available metadata for attribute correspondence identification. 2) semantic matching module is applied to analyze the metadata and find attributes in different databases that represent the same real-world concept. 3) user checking module adopt semantic model construction principle to describe the matching results as a draft semantic model and provide user editing tool to check and improve the draft model. This document describes the architecture of our platform, walks through its most important two features: machine learning technology based attributes matching and semantic model construction principle.

Rapid growth and spread of information technology especially network technology enables people to use computers more naturally and easily and as a result, more design data and information leave in designers' desktops. Recent advances in grid, semantic web, P2P and web services have revolutionized the way we communicate and collaborate and also make it possible to integrate desktop data. Undertaking the intersection fulfillment of these technologies on enterprise desktop enables new integrative and collaborative use of not only organizational data but also distributed, autonomous personal data on the web by fully leveraging potentially useful information sources on many desktops. In this context, we introduce SDDG (Semantic Desktop Data Grid) a P2P-semantic-grid enabled information sharing infrastructure for entire enterprise information, which wraps both organizational and personal information sources as semantic grid services and creates a semantic information interoperability environment following a P2P data coordination way. Through a discussion of fundamental scientific positions in general and approaches to information integration research in particular, a three-tier hybrid framework is presented and discussed in detail. The architecture for SDDG is an extension of our previous work ESD [1] by reflecting principles of P2P data integration in semantic grid.

Nowadays, swirling around most businesses is not structure information but semi-structure information, such as spreadsheets, and documents created by an individual or company. To fully manage and use semi-structure information as structure information does, we propose a technology based on the bi-directional integration between XML and RDB in a three-tiered structure. Through undertaking research at the intersection of XML and RDB, the technology expects to represent and use semi-structure information based on XML, and manage semi-structure information using RDB. Bi-directional means that we transform semi-structure data represented by XML into RDB and oppositely publishing RDB data into corresponding XML format based on schema mapping between XML and RDB, which is the first tier named Static Transformation Tier in the three-tired structure. On the basis of static transformation tier, we transform semi-structure data query, i.e. query on XML from user interface into SQL on the corresponding bottom RDB and return back the result data in XML format, which is the second tier named Dynamic Query Tier. At last, synchronous updating for the virtual XML and bottom RDB is carried out as the third tier named Synchronous Updating Tier. All the three tiers' integration is realized through the middleware, which is also offered in this paper.

To establish a required underlying foundation of enterprise information sharing over a highly complex and dynamic networked environment at both system and knowledge level, an enterprise needs a more generic, standardized, scalable and semantically interoperable infrastructure to fully leverage the information from different data sources, applications, and environments on demand. In this context, we present a system SGII which coordinates enterprise information by combining p2p data integration paradigm and semantic web technology on top of OGSA-DAI infrastructure. Trough a discussion of the fundamental architecture from the P2P and grid perspectives respectively, we express the basic principles of information sharing and integration of SGII. We also present the component peer services of SGII by discussing their instantiation, semantic enrichment and interaction mechanisms based on OGSA-DAI infrastructure.

To overcome the challenges of EII (Enterprise Information integration), we propose SGII which is the first system undertaking research at the intersection of semantic grid and P2P data integration, exploiting their strengths in a common framework, and expanding their applicability in the area of EII We first discuss how the P2P and semantic grid technologies can drive current EII systems to a new decentralized, flexible, scalable system based on a short survey of the state of the art of EII and its current challenges Then, through a discussion of the fundamental formal architecture in general and its components in particular, we depict the basic integration principles from both P2P and semantic grid perspectives The key contributions of this paper are a P2P semantic grid service oriented framework for EII, which mainly consists of three semantic grid services (data peer, semantic peer and application peer services); basic integration principles, which is compatible with OGSA-DAI infrastructure and P2P data integration paradigm; and added value over the state of the art of EII.

Building ontology from scratch need identify the basic concepts of application domain. In terms of database integration, draft concepts can be directly captured by processing schemas of databases. In this context, we present an automatic approach based on matching and clustering of relational schema columns to capture concepts from relative databases. By combining three individual name matchers following a composite way, the matching phase computes the similarity between column names, which will be used as classifiers for clustering. A neural network matcher is proposed in clustering phase to categorize columns of schemas into clusters by using column constraints with the results from matching phase for joint consideration of multiple criteria. Finally, each concept is defined as a cluster of columns representing the same meaning. The concepts discovered by our approach can be used as draft material or seeds for further comprehensive concept capture.

Although a lot of previous work on schema matching has developed many partial automatic matches for specific application domains, combining multiple match techniques enables achieving high accuracy for a large variety of match circumstances. In this context, we present a schema-based element matching approach that concatenates linguistic-based matchers and a constraint-based matcher. We propose a basic processing of our element level match approach in terms of a sequence of linguistic-based match and constraint-based match. We also provide a composite element name matcher to automatically combine linguistic-based match algorithms with a maximum priority strategy, and a neural network matcher to categorize elements of schemas by using element constraints with results from composite name matcher for joint consideration of multiple criteria. The concatenation of composite name matcher and neural network matcher enable our approach to adapt to more complex matching circumstance