scholarly journals Towards an Integrated Architecture Model of Smart Manufacturing Enterprises

2018 ◽  
pp. 112-133 ◽  
Author(s):  
Thijs Franck ◽  
Maria-Eugenia Iacob ◽  
Marten van Sinderen ◽  
Andreas Wombacher
Keyword(s):  
Smart Manufacturing ◽  
Architecture Model ◽  
Manufacturing Enterprises ◽  
Integrated Architecture

scholarly journals Neuro-inspired Framework for Cognitive Manufacturing Control

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Ioan Dumitrache ◽  
Simona Iuliana Caramihai ◽  
Dragos Constantin Popescu ◽  
Mihnea Alexandru Moisescu ◽  
Ioan Stefan Sacala
Keyword(s):  
Problem Solving ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
Software Modeling ◽  
Manufacturing Enterprises ◽  
Data Flows ◽  
New Vision ◽  
Generation Mechanisms ◽  
And Behavior ◽  
High Degree

There are currently certain categories of manufacturing enterprises whose structure, organization and operating context have an extremely high degree of complexity, especially due to the way in which their various components interact and influence each other. For them, a series of paradigms have been developed, including intelligent manufacturing, smart manufacturing, cognitive manufacturing; which are based equally on information and knowledge management, management and interpretation of data flows and problem solving approaches. This work presents a new vision regarding the evolution of the future enterprise based on concepts and attributes acquired from the field of biology. Our approach addresses in a systemic manner the structural, functional, and behavioral aspects of the enterprise, seen as a complex dynamic system. In this article we are proposing an architecture and management methodology based on the human brain, where the problem solving is achieved by Perception – Memory – Learning and Behavior Generation mechanisms. In order to support the design of such an architecture and to allow a faster learning process, a software modeling and simulation platform was developed and is briefly presented.

scholarly journals Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems

2021 ◽  
Vol 11 (10) ◽  
pp. 4446
Author(s):  
Emilia Brad ◽  
Stelian Brad
Keyword(s):  
Circular Economy ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Optimal Solution ◽  
Smart Manufacturing ◽  
Manufacturing Enterprises ◽  
Design Engineers ◽  
Definition Of ◽  
High Level ◽  
Reconfigurable Machine Tool

In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise’s business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment’s lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm.

scholarly journals Traceable and Intelligent Supply Chain based on Blockchain and Artificial Intelligence

2021 ◽  
Vol 2070 (1) ◽  
pp. 012158
Author(s):  
Sachin Karadgi ◽  
Vadiraj Kulkarni ◽  
Shridhar Doddamani
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Process Data ◽  
Control Data ◽  
Chain Management ◽  
Manufacturing Enterprises ◽  
Development Lifecycle ◽  
Narrow Context

Abstract Smart manufacturing focuses on maximizing the capabilities to increase multiple objectives, like cost, delivery, and quality, in manufacturing enterprises. This requires implementing product development lifecycle, production system lifecycle, and business cycle for supply chain management. In short, a considerable amount of data is generated in a given manufacturing enterprise. Likewise, progress has been made to adopt blockchain in financial industries, but the adoption is slow in non-financial sectors. The article elaborates a methodology for the realization of a traceable and intelligent supply chain. First, the methodology elaborates on the realization of traceability of enterprise entities, which are an integral part of the supply chain. In this case, each participating stakeholder of the supply chain is required internally to realize a smart manufacturing system with an extension to write critical control data to the blockchain (i.e., a subset of process data). Artificial Intelligence (AI) is being adopted in most industries. A supply chain stakeholder has access to its data and can employ AI to derive new insights. The data available with the stakeholder provides a narrow context. With blockchain, all the stakeholders have access to the data from other stakeholders. Subsequently, the insights derived by a stakeholder will be more meaningful. This will assist in realizing an intelligent supply chain.

Design of an Integrated Architecture for Process Enterprise e-Manufacturing

2009 ◽  
Vol 626-627 ◽  
pp. 711-716
Author(s):  
Dong Juan Xue ◽  
W.X. Zhang
Keyword(s):  
System Architecture ◽  
Remote Monitoring ◽  
Steel Industry ◽  
Integrated System ◽  
Production Plan ◽  
Manufacturing Enterprises ◽  
Process Manufacturing ◽  
Dynamic Modification ◽  
Integrated Architecture ◽  
Implementation Method

To solve dynamic modification consistency of production plan and scheduling for process manufacturing enterprises, firstly the integrated architecture and implementation of e-manufacturing in iron & steel industry is presented. Then the integrated system architecture about administration and management, production process, remote monitoring is built and its characteristics, scheme and concrete design & implementation method are elaborated. The integrated structure effectively realizes information sharing among the sub-systems within process enterprise.

scholarly journals A Framework for Industry 4.0 Readiness and Maturity of Smart Manufacturing Enterprises: A Case Study

2021 ◽  
Vol 13 (12) ◽  
pp. 6659
Author(s):  
Zeki Murat Çınar ◽  
Qasim Zeeshan ◽  
Orhan Korhan
Keyword(s):  
Industry 4.0 ◽  
Smart Manufacturing ◽  
Maturity Models ◽  
Four Dimensions ◽  
Manufacturing Enterprises ◽  
Maturity Score ◽  
Parts Manufacturing ◽  
Adoption Level ◽  
Insight Into

Recently, researchers have proposed various maturity models (MMs) for assessing Industry 4.0 (I4.0) adoption; however, few have proposed a readiness framework (F/W) integrated with technology forecasting (TF) to evaluate the growth of I4.0 adoption and consequently provide a roadmap for the implementation of I4.0 for smart manufacturing enterprises. The aims of this study were (1) to review the research related to existing I4.0 MMs and F/Ws; (2) to propose a modular MM with four dimensions, five levels, 60 second-level dimensions, and 246 sub-dimensions, and a generic F/W with four layers and seven hierarchy levels; and (3) to conduct a survey-based case study of an automobile parts manufacturing enterprise by applying the MM and F/W to assess the I4.0 adoption level and TF model to anticipate the growth of I4.0. MM and F/W integrated with TF provides insight into the current situation and growth of the enterprise regarding I4.0 adoption, by identifying the gap areas, and provide a foundation for I4.0 integration. Case study findings show that the enterprise’s overall maturity score is 2.73 out of 5.00, and the forecasted year of full integration of I4.0 is between 2031 and 2034 depending upon the policy decisions.

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