scholarly journals Upgrading Strategy of Small and Medium Manufacturing Enterprises (SMMEs) to Smart Manufacturing

2019 ◽  
Vol 627 ◽  
pp. 012011 ◽  
Author(s):  
Baolei Zhang ◽  
Fuquan Zhao ◽  
Zongwei Liu
Keyword(s):  
Smart Manufacturing ◽  
Manufacturing Enterprises

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.

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.

A GEMMA-GRAFCET Generator for the Automation Software of Smart Manufacturing Systems

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 232
Author(s):  
Juan Manuel Castillo ◽  
Giacomo Barbieri ◽  
Alejandro Mejia ◽  
José Daniel Hernandez ◽  
Kelly Garces
Keyword(s):  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Online Tool ◽  
Model Driven ◽  
Manufacturing Enterprises ◽  
Virtual Commissioning ◽  
Industrial Internet ◽  
Smart Manufacturing Systems ◽  
Operational Modes

Within the Industry 4.0 revolution, manufacturing enterprises are transforming to intelligent enterprises constituted by Smart Manufacturing Systems (SMSs). A key capability of SMSs is the ability to connect and communicate with each other through Industrial Internet of Things technologies, and protocols with standard syntax and semantics. In this context, the GEMMA-GRAFCET Methodology (GG-Methodology) provides a standard approach and vocabulary for the management of the Operational Modes (OMs) of SMSs through the automation software, bringing a common understanding of the exchanged data. Considering the lack of tools to implement the methodology, this work introduces an online tool based on Model-Driven Engineering–GEMMA-GRAFCET Generator (GG-Generator)–to specify and generate PLCopen XML code compliant with the GG-Methodology. The proposed GG-Generator is applied to a case study and validated using Virtual Commissioning and Dynamic Software Testing. Due to the consistency obtained between the GG-Methodology and the generated PLC code, the GG-Generator is expected to support the adoption of the methodology, thus contributing to the interoperability of SMSs through the standardization of the automation software for the management of their OMs.

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