CNC programming system for complex components based on KBE within integrated environment of CAD/CAPP/CAM

To solve the problem of advanced digital manufacturing technology in the practical application, a knowledge engineering technology was introduced into the computer numerical control (CNC) programming. The knowledge acquisition, knowledge representation and reasoning used in CNC programming were researched. The CNC programming system functional architecture of impeller parts based on knowledge based engineering (KBE) was constructed. The structural model of the general knowledge-based system (KBS) was also constructed. The KBS of CNC programming system was established through synthesizing database technology and knowledge base theory. And in the context of corporate needs, based on the knowledge-driven manufacturing platform (i.e. UG CAD/CAM), VC++6.0 and UG/Open, the KBS and UG CAD/CAM were integrated seamlessly and the intelligent CNC programming KBE system for the impeller parts was developed by integrating KBE and UG CAD/CAM system. A method to establish standard process templates was proposed, so as to develop the intelligent CNC programming system in which CNC machining process and process parameters were standardized by using this KBE system. For the impeller parts processing, the method applied in the development of the prototype system is proven to be viable, feasible and practical.

<div class="htmlview paragraph">The introduction of a Computer Numerical Control (CNC) wing fastening facility in BAE SYSTEMS Airbus UK for the Airbus A340-600 wing led to substantial efforts to reduce Numerical Control (NC) programming time. Since specific values had to be determined for each of 110,000 fastener locations, manual calculations and input had to be eliminated. The decision was made to generate an intelligent CAD model, with “attributes” attached to each fastener location. These attributes would hold three-dimensional information regarding the cross-sectional dimensions of the assembled components at each fastener location, calculated using Knowledge Based Engineering (KBE) software, prior to the NC programming process. The NC software was then configured to read this as background data to programming instructions and to generate output accordingly. This process reduced data preparation by 90% giving significant savings in NC programming time compared to existing methods, with a marked improvement in machine cycle time and quality of first article components.</div> <div class="htmlview paragraph">This paper will explain the development of a knowledge-based NC programming system, and will identify the need for improvement, the existing methods of data preparation, the development of an efficient system and the inherent benefits associated with its implementation.</div>

For the deployment of both Product Lifecycle Management (PLM) and Knowledge-Based Engineering (KBE) approaches, product and process engineering knowledge needs to be identified, acquired, formalized, processed and reused. While knowledge acquisition is still a bottleneck process, the formalized engineering knowledge is still too often encapsulated in CAD models and in KBE systems developed in vendor-specific environments. To address this issue, this paper introduces a possible solution enabling the enrichment of a CAD-KBE-PLM integration schema that provides a standardized and neutral representation of engineering knowledge for further reuse across heterogeneous CAD, KBE and PLM systems. To enrich this schema, the proposed solution combines the use of a Multi-CAD API library – which allows platform-independent and automatic extraction of engineering knowledge from CAD models into an XML-based representation – and a Knowledge Acquisition and Formalization Assistant (KAFA) which assist domain experts to formalize their procedural knowledge.

The incorporation of high‐precision intelligent manufacturing technologies into patterned metal halide perovskite production has attracted scientific and commercial interest because of advantages in terms of designable and controllable processes, multiscale and multimaterial construction strategies, and improved energy‐conversion performance. Here, the current developments in intelligent manufacturing technologies are reviewed for patterned and tailored perovskite structures. Six high‐precision approaches, namely, nanoimprint lithography, space‐confined growth, ink‐based direct printing, laser‐induced nucleation, modification using a laser, and high‐energy etching, are investigated and summarized. The focus of the review is on the basic principles of intelligent manufacturing methods, process optimization, and controllable perovskite microstructures. Additionally, the performance improvements of patterned and tailored perovskites and their devices, including photodetectors, solar cells, lasers, information storage and encryption devices, anticounterfeiting devices, and full‐color display applications, are discussed. The potential future applications of shaped perovskites fabricated via intelligent manufacturing technologies are promising.

Modeling of Intelligent System Thinking in Complex Adaptive Systems

The adjective cognitive especially in conjunction with the word computing seems to be a trendy buzzword in the artificial intelligence community and beyond nowadays. However, the term is often used without explicit definition. Therefore we start with a brief review of the notion and define what we mean by cognitive reasoning. It shall refer to modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge involving among others the representation of knowledge where all processes from the acquisition and update of knowledge to the derivation of conclusions must be implementable and executable on appropriate hardware. We briefly introduce relevant approaches and methods from cognitive modeling, commonsense reasoning, and subsymbolic approaches. Furthermore, challenges and important research questions are stated, e.g., developing a computational model that can compete with a (human) reasoner on problems that require common sense.

Manufacturing firms have been compelled to invest heavily in digitizing and optimizing their technical and manufacturing operations as a result of mass customization. When developing and introducing new goods, not only must manufacturing procedures be computerized, but also information of how the products must be developed and manufactured based on client needs must be applied. One major academic issue is to assist the industry in ensuring that stakeholders understand the background information of automated engineering all through the production process. The goal of the study described in this article is to provide a foundation for a connectivity perspective of Knowledge-Based Engineering (KBE). The use of graph theory in conjunction with content-based filtering methods is used to handle network creation and contextualization, which are fundamental ideas in connectivism. To enable a connectivity management culture, the article demonstrates how engineering information in spreadsheet, knowledge representation, and Computer Aided Design (CAD) models may be infiltrated and displayed as filtering graphs.

According to the technical and tactical characteristics of the project, the concept of dynamic regional grid was put forward and applied to the items of Net knowledge representation and knowledge acquisition. The grid-based dynamic region against the project of Net Knowledge Representation and Knowledge Acquisition was proposed. The project series (table tennis, badminton, tennis, volleyball, ball, boxing, taekwondo, football) multimedia information intelligence gathering and knowledge acquisition system was developed. Eight software copyright certificates were made and applied to the actual competition and training.

Resolution-based Knowledge Representation and Reasoning (KRR) systems, such as Flora-2, Silk or Ergo, can scale to tens or hundreds of millions of facts, while supporting reasoning that includes Hilog, inheritance, defeasibility theories, and equality theories. These systems handle the termination and complexity issues that arise from the use of these features by a heavy use of tabled resolution. In fact, such systems table by default all rules defined by users, unless they are simple facts.Performing dynamic updates within such systems is nearly impossible unless the tables themselves can be made to react to changes. Incremental tabling as first implemented in XSB (Saha 2006) partially addressed this problem, but the implementation was limited in scope and not always easy to use. In this paper, we introducetransparent incremental tablingwhich at the semantic level supports updates in the 3-valued well-founded semantics, while guaranteeing full consistency of all tabled queries. Transparent incremental tabling also has significant performance improvements over previous implementations, including lazy recomputation, and control over the dependency structures used to determine how tables are updated.

The core of fault diagnosis expert system is diagnostic knowledge representation and reasoning. Fuzzy Petri nets (FPN) have been testified to an ideal tool for knowledge representation and reasoning. However, when using FPN for diagnostic knowledge representation and reasoning, fault symptoms will disappear after fault propagated. This is not congruous with factual fault propagation. Another major limitation is the lacking of formal reasoning algorithm of FPN, which leads to it can't be implemented expediently by computer. In view of the limitations of FPN based diagnostic knowledge representation and reasoning, an enhanced model named fuzzy fault Petri nets (FFPN) and its formal reasoning algorithm are presented in the paper. A fault diagnosis instance was used to verify the feasibility and validity of the proposed method. It is demonstrated that this method is quite effective.

Fringe projection profilometry (FPP) and phase measuring deflectometry (PMD) are the currently popular technologies for measuring diffuse and specular reflective surfaces respectively. Both methodologies demonstrate superior performance for single-type reflective surfaces. However, with the rapid development of advanced manufacturing and intelligent manufacturing technologies, products have seen significant improvements in complexity, diversity, and personalization, often exhibiting composite reflective characteristics on their surfaces. The aforementioned single technology cannot meet the measurement requirements for composite reflective surfaces. To address this challenge, this paper presents a composite reflective surfaces measurement method combining FPP with PMD techniques, and an optimized system calibration method based on binocular stereo vision is proposed to enhance the system's measurement accuracy. To improve the measurement efficiency, a strategy in which the LCD screen and projector project different colorful fringe patterns simultaneously is adopted. To mitigate stray light interference from the projector on specular surface profilometry, a partition projection method is presented. Instead of establishing the pixel mapping relationship between the projector and diffused surface aeras, the coordinate mapping relationship between the specular reflective region and the projector pixel is established according to the phase-height relationship to realize the accurate projection of the projector to the diffused surface. Experiments have been performed to validate the proposed method, and the results demonstrate that it can reconstruct the composite surfaces with higher accuracy. In particular, the reconstruction accuracy of specular surface can reach several tens of nanometres.

At present, software development life cycle has many standards involved. However, during the process of creating components in an application, there are many problems that originate in the knowledge gap between stakeholders and developers. The problem of developing is issues such as ambiguity, misunderstanding, miscommunications, different domain skills, and conflicting goals. Metamodel are needed because the methodology contents must be purpose-fit and optimally adjusted for the users/stakeholders/organisation. Metamodel, a model of software model includes endeavor domain, method domain, and metamodel domain. It has contents for user's needs, including organization, project, and product aspect. Three interconnected meta-elements are work products, work units, and producers, as well as relationship between them. ISO/IEC 24744 is the standard that includes metamodels.Application software are now applied knowledge management components aiming to reduce the gap between users/stakeholders and software developer team. The knowledge can be shared and transferred to others. This paper, therefore, focuses on applying knowledge management metamodel for software development process. We will extend the metamodel concept in ISO/IEC 24744 for knowledge acquisition, knowledge representation, and knowledge transfer to WorkUnits and WorkProducts, thereby increasing their explicitness in the software development process. Tacit knowledge will be focused and propose knowledge metamodel will be shown.

A New Approach In Learning For Intelligent Multi Agent Systems

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.

The need to create mechanisms or systems to acquire, represent and convey knowledge is one of the problems that has been given priority in the passing of the years of research, the need for acquisition has generated the need for more compact models that conserve implicit and explicit knowledge generated within their organizations. In this environment, due to the change that has generated knowledge management, companies recognize the importance of the management of these intangible assets, as a requirement to stay in the market and adapt to organizational and technological changes, each organization must find its own way and recognize that intellectual capacity has become an important factor for the success of organizational development. The aim of this paper is to present the results of a systematic review of the literature by which we identify some acquisition techniques and knowledge representation, that can be replicated in organizations to leverage internal and external knowledge to the organization and achieve advantages sustainable competitive . The results present management techniques related to the culture and values organizational as well as the definition of different types of alliances also identified as expert systems technologies and social tools as useful for the acquisition and representation of knowledge.