scholarly journals SOLIOT—Decentralized Data Control and Interactions for IoT

2020 ◽  
Vol 12 (6) ◽  
pp. 105 ◽  
Author(s):  
Sebastian R. Bader ◽  
Maria Maleshkova
Keyword(s):  
Manufacturing Industry ◽  
Industrial Applications ◽  
Shop Floor ◽  
Test Bed ◽  
Digital Revolution ◽  
Virtual Test ◽  
Data Control ◽  
Recent Approach ◽  
Social Applications ◽  
Manufacturing Environments

The digital revolution affects every aspect of society and economy. In particular, the manufacturing industry faces a new age of production processes and connected collaboration. The underlying ideas and concepts, often also framed as a new “Internet of Things”, transfer IT technologies to the shop floor, entailing major challenges regarding the heterogeneity of the domain. On the other hand, web technologies have already proven their value in distributed settings. SOLID (derived from “social linked data”) is a recent approach to decentralize data control and standardize interactions for social applications in the web. Extending this approach towards industrial applications has the potential to bridge the gap between the World Wide Web and local manufacturing environments. This paper proposes SOLIOT—a combination of lightweight industrial protocols with the integration and data control provided by SOLID. An in-depth requirement analysis examines the potential but also current limitations of the approach. The conceptual capabilities are outlined, compared and extended for the IoT protocols CoAP and MQTT. The feasibility of the approach is illustrated through an open-source implementation, which is evaluated in a virtual test bed and a detailed analysis of the proposed components.

A NeuroEndocrine-Inspired Manufacturing System Using the Potential Field Concept

2011 ◽  
Vol 201-203 ◽  
pp. 1741-1747
Author(s):  
Kun Zheng ◽  
Dun Bing Tang ◽  
Wen Bin Gu
Keyword(s):  
Potential Field ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Environmental Changes ◽  
Point Of View ◽  
Shop Floor ◽  
System Control ◽  
Hormone Regulation ◽  
Test Bed

The manufacturing industry must have manufacturing systems that deal with the agile response to the appearance and changing conditions.As biological organisms are quite capable of adapting to environmental changes and stimulus, bio-inspired concepts have been recognized much suitable for adaptive manufacturing system control. This paper, therefore, proposes a NeuroEndocrine-Inspired Manufacturing System (NEIMS) using the potential field concept. The proposed NEIMS control architecture is inherited from neuro-control and hormone-regulation principles to agilely deal with the frequent occurrence of unexpected disturbances at the shop floor level. Hormone-regulation can impel system to be equilibrium through a potential field approach. From the cybernetics point of view, the control model of NEIMS has been described in detail. And a test bed has been set up to enable the NEIMS simulation.

Evaluating alternative manufacturing control strategies using a benchmark system

2002 ◽  
Vol 216 (6) ◽  
pp. 927-932 ◽  
Author(s):  
R W Brennan ◽  
O W
Keyword(s):  
Control System ◽  
Control Strategies ◽  
Discrete Event ◽  
Shop Floor ◽  
Test Bed ◽  
Agent Based ◽  
Event Simulation ◽  
Controlled Systems ◽  
Job Sequencing ◽  
Manufacturing Environments

This paper describes an investigation of the effects of dynamic job routing and job sequencing decisions on the performance of a distributed control system and its adaptability against disturbances. This experimental work was carried out to compare the performance of alternative control strategies in various manufacturing environments and to investigate the relationship between the ‘control’ and ‘controlled’ systems. The experimental test-bed presented in this paper consists of an agent-based control system (implemented in C++) and a discrete-event simulation model. Using this test-bed, various control strategies were tested on a benchmark manufacturing system by varying production volumes (to model the production system with looser/tighter schedules) and disturbance frequencies. It was found that hybrid strategies that combine reactive agent mechanisms (and allocation strategies such as the contract net) with appropriate job sequencing heuristics provide the best performance, particularly when job congestion increases on a shop-floor.

Towards a Formal Specification of Production Processes Suitable for Automatic Execution

2021 ◽  
Vol 11 (1) ◽  
pp. 161-179
Author(s):  
Marko Vještica ◽  
Vladimir Dimitrieski ◽  
Milan Pisarić ◽  
Slavica Kordić ◽  
Sonja Ristić ◽  
...  
Keyword(s):  
Process Modeling ◽  
Code Generation ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Knowledge Bases ◽  
Smart Devices ◽  
Shop Floor ◽  
Modeling Languages ◽  
Digital Revolution ◽  
Production Processes

Abstract Technological advances and increasing customer need for highly customized products have triggered a fourth industrial revolution. A digital revolution in the manufacturing industry is enforced by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to allow flexibility of smart factories by automating shop floor changes based on the changes in input production processes and ordered products. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. Existing process modeling languages are not usually tailored to model production processes, especially if models are needed for automatic code generation. In this paper we propose a research on Industry 4.0 manufacturing using a Domain-Specific Modeling Language (DSML) within a Model-Driven Software Development (MDSD) approach to model production processes. The models would be used to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution. A pilot comparative analysis of three modeling languages that are commonly used for process modeling is given with the goal of identifying supported modeling concepts, good practices and usage patterns.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 628
Author(s):  
Michail J. Beliatis ◽  
Kasper Jensen ◽  
Lars Ellegaard ◽  
Annabeth Aagaard ◽  
Mirko Presser
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Careful Consideration ◽  
Added Value ◽  
Shop Floor ◽  
Circuit Boards ◽  
Next Generation ◽  
Road Map ◽  
Industrial Iot ◽  
Production Cycles

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

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