Smart Factory Information Service Bus (SIBUS) for manufacturing application: requirement, architecture and implementation

The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing product information, collaborative information exchange, and increasing productivity. To take full advantage of the potential of the IoT in manufacturing systems, it is necessary that the information service perspective should receive keen attention. This paper proposes a reference architecture for the information service bus or middleware for the Smart Factory that can be used for information acquisition, analysis, and application for the various stakeholders at the levels of Machine, Factory, and Enterprise Resource Planning. To reflect the real voice of the industry, real industrial problems have been identified, transformed into requirements, and incorporated into the information architecture; i.e., Smart Factory Information Service Bus. The implementation process of the reference architecture is also presented and illustrated via case studies.

Zero Defect Manufacturing Strategies and Platform for Smart Factories of Industry 4.0

Gökan May

Within the context of market globalisation, the quality of products has become a key factor for success in manufacturing industry. The growing unpredictability of demand necessitates continuous adjustments in production targets. Addressing customer needs and customer satisfaction are the most important factors for successful businesses. Being consistent in meeting their needs, the existing manufacturing systems have to be adaptable while maximising the quality of their products. Guided by this challenge, in this paper we provide a holistic framework and ad-hoc strategies applicable both to new and existing manufacturing lines to achieve zero-defects in manufacturing via a novel ZDM platform that integrates state of the art ICT technologies, AI models and inspection tools which elevate manufacturing plants to a superior level of competitiveness and sustainability. The proposed approach and results in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z - Fact0r, i.e. Zero-defect manufacturing strategies towards on-line production management for European factories.

SPRINGER-VERLAG BERLIN2019

Factory planning system considering energy-efficient process under cloud manufacturing

Yong Chan Choi, Ian Anthony Stroud, Jumyung Um

Cloud computing sets to make a change in business between enterprises over the Internet based on services providing dynamically reconfigurable and virtualized networks. It is difficult for an aircraft manufacturer to consider the performance and status of each factory (i.e., capacity, inventory, order, technology, etc.) because of the huge amount of suppliers. In legacy environments, the supplier consumed a long time to identify the machine and process required and the energy to be consumed in order to satisfy a new customer demand. If the process occurred with a new order should be optimized, additional time and cost were needed. Cloud computing can enable the relationship between the manufacturer and suppliers of aircraft, which is representative of mass customized products, to be flexible and efficient. In order to enhance the response time during contracting with suppliers, this paper focuses on an architecture, providing a service for a decision with the concerns about the time and cost as well as energy with regard to process planning. For realizing this service the manufacturer provides the process plan described using neutral language for numerical controllers called 'STEP-NC' which is independent from a specific machine tool. On the supplier side, the machinability check, tool path generation and energy use estimation should be identified with machine tool specifications described by STEP-NC. In the Cloud computing environment, the analysis is performed to check the possibility that a supplier can complete a new order from the manufacturer on the due date with the analysis of machine tools and on-going production schedule. This function is developed in a system which can be installed on a virtual platform and provided by a cloud service. (C) 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Elsevier Science Bv2014

Towards semantics-driven modelling and simulation of context-aware manufacturing systems

Damiano Nunzio Arena

Systems modelling and simulation are two important facets for thoroughly and effectively analysing manufacturing processes. The ever-growing complexity of the latter, the increasing amount of knowledge, and the use of Semantic Web techniques adhering meaning to data have led researchers to explore and combine together methodologies by exploiting their best features with the purpose of supporting manufacturing system's modelling and simulation applications. In the past two decades, the use of ontologies has proven to be highly effective for context modelling and knowledge management. Nevertheless, they are not meant for any kind of model simulations. The latter, instead, can be achieved by using a well-known workflow-oriented mathematical modelling language such as Petri Net (PN), which brings in modelling and analytical features suitable for creating a digital copy of an industrial system (also known as "digital twin"). The theoretical framework presented in this dissertation aims to exploit W3C standards, such as Semantic Web Rule Language (SWRL) and Web Ontology Language (OWL), to transform each piece of knowledge regarding a manufacturing system into Petri Net modelling primitives. In so doing, it supports the semantics-driven instantiation, analysis and simulation of what we call semantically-enriched PN-based manufacturing system digital twins. The approach proposed by this exploratory research is therefore based on the exploitation of the best features introduced by state-of-the-art developments in W3C standards for Linked Data, such as OWL and SWRL, together with a multipurpose graphical and mathematical modelling tool known as Petri Net. The former is used for gathering, classifying and properly storing industrial data and therefore enhances our PN-based digital copy of an industrial system with advanced reasoning features. This makes both the system modelling and analysis phases more effective and, above all, paves the way towards a completely new field, where semantically-enriched PN-based manufacturing system digital twins represent one of the drivers of the digital transformation already in place in all companies facing the industrial revolution. As a result, it has been possible to outline a list of indications that will help future efforts in the application of complex digital twin support oriented solutions, which in turn is based on semantically-enriched manufacturing information systems. Through the application cases, five key topics have been tackled, namely: (i) semantic enrichment of industrial data using the most recent ontological models in order to enhance its value and enable new uses; (ii) context-awareness, or context-adaptiveness, aiming to enable the system to capture and use information about the context of operations; (iii) reusability, which is a core concept through which we want to emphasize the importance of reusing existing assets in some form within the industrial modelling process, such as industrial process knowledge, process data, system modelling primitives, and the like; (iv) the ultimate goal of semantic Interoperability, which can be accomplished by adding data about the metadata, linking each data element to a controlled, shared vocabulary; finally, (v) the impact on modelling and simulation applications, which shows how we could automate the translation process of industrial knowledge into a digital manufacturing system and empower it with quantitative and qualitative analytical technics.