Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment

The use of ontologies for knowledge sharing and distributed collaboration has been widely recognised in the knowledge modelling community, but the lack of a systematic and constructive methodology for developing manufacturing ontologies has impeded their wide usage for knowledge reuse in distributed manufacturing environments. This paper presents a constructive, two-level knowledge modelling approach to systematically develop manufacturing ontologies using both software engineering and Semantic Web paradigms. The UML/OCL (Unified Modeling Language/Object Constraint Language)-based object modelling is used first to serve as a graphical and structured basis for conceptual communication between domain experts and knowledge engineers. The OWL/SWRL (Web Ontology Language/Semantic Web Rule Language)-based ontology modelling then extends the UML/OCL-based object models with added semantics using a progressive, semantics-oriented knowledge acquisition method. An illustrative example for manufacturing ontology development in the manufacturing industry for producing electronic connectors is used to demonstrate the practicality of the proposed approach.     

A framework for developing engineering design ontologies within the aerospace industry

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts’ opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as role-based computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies.     

Manufacturing knowledge verification in design support systems

This paper identifies the need for a verification methodology for manufacturing knowledge in design support systems; and proposes a suitable methodology based on the concept of ontological commitment and the PSL ontology (ISO/CD18629). The use of the verification procedures within an overall system development methodology is examined, and an understanding of how various categories of manufacturing knowledge (typical to design support systems) map onto the PSL ontology is developed. This work is also supported by case study material from industrial situations, including the casting and machining of metallic components. The PSL ontology was found to support the verification of most categories of manufacturing knowledge, and was shown to be particularly suited to process planning representations. Additional concepts and verification procedures were however needed to verify relationships between products and manufacturing processes. Suitable representational concepts and verification procedures were therefore developed, and integrated into the proposed knowledge verification methodology.     

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.     

Knowledge integration and sharing for complex product development

Product development is a highly creative and knowledge-intensive process that involves extensive information and knowledge exchange and sharing among geographically distributed teams and developers. How to best integrate such heterogeneous product knowledge has become an extremely important knowledge management (KM) subject associated with product development. Product development knowledge integration and sharing is becoming a key issue in the enterprise KM. This paper addresses the challenges of product development knowledge integration and sharing during product development. The aim of this research work effort is to develop a method to enhance the integration and sharing of product knowledge during the development phase. This study presents a systematic approach to developing knowledge integration and sharing for product development. The proposed approach includes the steps for designing a framework of product development knowledge sharing, developing representation model for product development knowledge, designing product development knowledge sharing process, designing product development knowledge integration ontology, developing knowledge integration and sharing method for product development based on ontology, and implementation of product development knowledge integration and sharing method. The proposed method could bring out an efficient and proactive way for knowledge integration and sharing among product developers in the product development process.     

Towards the derivation of an integrated process cost-modelling technique for complex manufacturing systems

Cost modelling is used to support business decisions, especially, when the objective is to remain competitive on price and be able to realise outputs at low cost. Many researchers and industrialists have proposed and experimented with different cost-modelling techniques with a view to influencing design and production decisions at an early stage of the development process. This has led to cost-modelling methods which have been broadly classified in this paper as qualitative and quantitative. The paper identifies current best practice cost-modelling techniques and their performance in complex and dynamic manufacturing environments. The review served as a platform to support the recommendation for an integrated cost-modelling methodology. The integrated methodology is based on the strengths of cost engineering, enterprise modelling, system dynamics and discrete event simulation modelling techniques. The method can help in the redesign and re-engineering of products and processes for better cost and value indications; support investment decision analysis; help determine appropriate business and manufacturing paradigms; influence ‘make, buy or outsourcing’ decisions and serve as a key process improvement tool.     

Motion generation and virtual simulation in a digital environment

The growing complexity in product design and manufacturing processes has made virtual prototyping an important new approach that enhances products and process development. Considering the ergonomic issues evident in product lifecycles, digital human modelling (DHM) is adopted for virtual simulation and proactive evaluation. A motion generation from semantics (MGS) system is proposed in this research. The MGS system features include virtual prototyping, natural language instruction, a method time measurement (MTM) translator and motion generator. The system was implemented using product lifecycle management (PLM) software and validated in an automotive manufacturing company. The practice of intuitively generating manual operations and conducting virtual simulations enables the system planners to quickly respond to manufacturing process changes and recursively improve the tooling and process design flexibility and efficiency.     

基于本体的机械产品知识仓库研究与应用

为解决产品开发全生命周期中知识管理与知识重用问题,提出了基于本体的机械产品知识仓库系统构架。从语法、属性、结构、实例四个方面进行相似性的计算,实现将源本体的实例映射到目标本体的过程。采用OWL本体表示语言对产品知识进行本体建模。并对基于本体的案例推理进行了深入的研究。最后将研究成果应用于制造业知识管理系统的机械产品知识仓库子系统中。     

基于Web的工艺知识资源服务与管理系统

在分析了当今制造企业对工艺知识资源服务与管理的迫切需求的基础上，提出了基于Web的工艺知识资源服务与管理系统，该系统面向企业内部网（Intranet），包括前台工艺知识资源服务系统和后台工艺知识资源管理系统两部分，前台系统为制造企业内部人员提供知识检索服务，后台系统作为工艺知识管理工具和前台的动态维护工具，文中介绍了系统的结构和功能以及关键实现技术。     

Weaving an agent-based Semantic Grid for distributed collaborative manufacturing

The development and maintenance of distributed collaborative manufacturing systems is faced with increasing challenges caused by the technical difficulties and ontological issues in distributed computing, resource integration and knowledge sharing over heterogeneous computing platforms. This paper presents an agent-based Semantic Grid for distributed manufacturing collaboration across ubiquitous virtual enterprises. A hierarchy of autonomous, adaptive agents forming a ‘super peer’ based peer-to-peer network is laid out in the Semantic Grid to provide, in an open, dynamic, loosely coupled and scalable manner, the service publication, discovery and reuse for management of applications that need to utilise Grid resources. The operation and experimental evaluation of the system are presented to validate the implementation of the proposed approach.     

SWMRD: a Semantic Web-based manufacturing resource discovery system for cross-enterprise collaboration

As supply chains are becoming ever more global and agile in the modern manufacturing era, enterprises are increasingly dependent upon the efficient and effective discovery of shared manufacturing resources provided by their partners, wherever they are. Enterprises are thus faced with increasing challenges caused by the technical difficulties and ontological issues in manufacturing interoperability and integration over heterogeneous computing platforms. This paper presents a prototype intelligent system SWMRD (Semantic Web-based manufacturing resource discovery) for distributed manufacturing collaboration across ubiquitous virtual enterprises. Ontology-based annotation to the distributed manufacturing resources via a new, multidisciplinary manufacturing ontology is proposed on the semantic web to convert resources into machine understandable knowledge, which is a prelude to the meaningful resource discovery for cross-enterprise multidisciplinary collaboration. An ontology-based multi-level knowledge retrieval model is devised to extend the traditional information retrieval approaches based on keyword search, with integrated capabilities of graph search, semantic search, fuzzy search and automated reasoning to realise the intelligent discovery of manufacturing resources, e.g. to facilitate more flexible, meaningful, accurate and automated resource discovery. A case study for intelligent discovery of manufacturing resources is used to demonstrate the practicality of the developed system.     

Knowledge discovery from observational data for process control using causal Bayesian networks

This paper investigates learning causal relationships from the extensive datasets that are becoming increasingly available in manufacturing systems. A causal modeling approach is proposed to improve an existing causal discovery algorithm by integrating manufacturing domain knowledge with the algorithm. The approach is demonstrated by discovering the causal relationships among the product quality and process variables in a rolling process. When allied with engineering interpretations, the results can be used to facilitate rolling process control.     

基于设计奖项获奖案例的设计知识建模研究

为实现设计奖项获奖案例中的优秀设计知识的有效重用,对获奖案例中的设计知识类别和表示方式进行研究。通过分析设计奖项获奖案例中的设计知识内涵;引入本体概念,根据本体的四元组模型构建获奖案例中设计知识的本体结构。以中国设计智造大奖(DIA)中智慧城市服务平台类获奖案例为例,通过语言逻辑学、语义研究等方法对获奖案例的设计知识进行概念、本体属性、概念关系的描述和定义,从而搭建设计奖项知识(DAK)模型,并借助OWL语言和知识图谱等方式实现智慧城市服务平台类获奖案例中设计知识的语义表示。提出一种基于本体的设计奖项获奖案例知识模型,为设计奖项中的优秀设计知识储存、管理、重用提供理论支持和实践参考。     

Event-based modelling of distributed sensor networks in battery manufacturing

Battery manufacturing systems are characterised by their complex dynamics subject to constant changes caused by technology insertion, engineering modifications, as well as disruption events. To support daily operation, distributed sensors are used to provide real-time data describing the status of each process. Despite the big potential in improving productivity, the advantages of distributed sensor networks are not fully realised for overall system efficiency due to a lack of system-level modelling. Motivated by this need, we develop an event-based modelling (EBM) approach to quantify the systematic impacts of stations and supporting activities with an index called permanent production loss. EBM instantaneously captures the system dynamics using distributed sensor information and provides a severity ranking of stations and supporting activities. We also study the system dynamics of serial production lines with multiple slowest stations and quantify the impacts of disruption events to the production system. A case study is conducted to demonstrate the application of EBM in a battery production system and its ability to facilitate decision-making at plant floor on resources and budget allocation.     

amePLM: a platform providing information provision in engineering

The procedure for the engineering of products and production systems in manufacturing companies is often distributed over several organisational units. The different units involved in these procedures use their respective methods and tools, resulting in silo-like and distributed information and data sources for engineering. In this paper, the open platform amePLM is introduced. This platform is based on a semantic data model. The ontology as explicitly formulated data model allows an integrated view on data and information available in the systems used along the product lifecycle, and the automatic provision of suitable information to the user. Furthermore, this open approach allows the linking of the solution to existing engineering software systems in the sense of a continuous flow of information.     

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.     

Modelling robustness for manufacturing processes: a critical review

‘Robustness’ is an important concept used in quality engineering for the improvement of quality in a manufacturing process. A process which is insensitive to noise variation is called a robust process. The robustness is modelled by several researchers and practioners for its design and implementation in a manufacturing process. A review of all these approaches is essential in order to assess their strengths, limitations and applicability under different process conditions and constraints. Over the years, many of these approaches have found widespread application in measuring, assessing and modelling of process robustness in manufacturing and other industries. In this paper, an attempt has been made to review critically the existing approaches as proposed and applied for measuring and evaluating robustness of manufacturing processes. Based on the critical appraisal, the key issues are identified and a generic framework for modelling and measuring of process robustness in single- and multi-stage manufacturing processes is presented.