Design and verification of discrete event controllers for Smart Factory

2018 ◽  
Author(s):  
Armand Toguyeni
Keyword(s):  
Control System ◽  
Manufacturing Industry ◽  
Discrete Event ◽  
Smart Factory ◽  
Systematic Method

"This paper presents a study on the development of smart factory in the context of the manufacturing industry. We show that such systems must be agile, i.e. able to adapt quickly to changing contexts such as a particular production or material faults. In this study we model the control system of these systems by a layered and generic approach. We distinguish several classes of models to manage different categories of flexibility. The main objective of the study is to provide a rigorous and systematic method for modeling and verifying systematically, rigorously and effectively this type of system."

Industrie 4.0 in der Textilindustrie*/Industry 4.0 - Integrated Industry in the textile industry: Smart factory approaches with a special focus on textile products and processes

2015 ◽  
Vol 105 (04) ◽  
pp. 195-199
Author(s):  
R. Riedel ◽  
N. Göhlert ◽  
E. Müller
Keyword(s):  
Textile Industry ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Industrie 4.0 ◽  
Smart Factory ◽  
Special Focus ◽  
Considerable Potential ◽  
Textile Products

Industrie 4.0 bietet für die produzierende Industrie in Deutschland erhebliche Potentiale zur Steigerung der Wettbewerbsfähigkeit. Die Anwendung und volle Ausnutzung der Möglichkeiten entsprechender Technologien sind jedoch an bestimmte Voraussetzungen gebunden. Der Fachbeitrag reflektiert vor diesem Hintergrund die Umsetzungspotentiale von Industrie 4.0 in der Textilindustrie.   Industry 4.0, also called Integrated Industry, provides considerable potential for the manufacturing industry in Germany to increase its competitiveness. However, the application and the full exploitation of the potential of those technologies depend on certain conditions. Against this background, the article reflects on the implementation potential of Industrie 4.0 in the textile industry.

scholarly journals A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulatio

2019 ◽  
Vol 8 (6) ◽  
pp. 4998-5003
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Discrete Event ◽  
The State ◽  
State Variables ◽  
Event Simulation ◽  
Potential Benefits ◽  
Short Hand

Discrete-Event Simulation (DES) is concerned with system and modeling of that system, where the state of the system is transformed at different discrete points from time to time, and several event occurs from time to time and the changes in state variables will transform then activities/attributes connected to these state variables changes according to the event. It is a robust methodology in the manufacturing industry for strategic, tactical, and operational applications for an organization, and yet organizations ignore to use simulation and do not rely on it. Moreover, companies that are using DES are not using the potential benefits but merely used as a short-hand basis for problems like bottlenecks, optimization, and in later stages of production like PLM, this paper aims to apply and analyze Discrete-Event Simulation through a Manufacturing System. The work describes here is to understand the concept of simulation for a system and to practice Discrete Event methodology

Business process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory

2018 ◽  
Vol 60 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Jana-Rebecca Rehse ◽  
Sharam Dadashnia ◽  
Peter Fettke
Keyword(s):  
Business Process ◽  
Industry 4.0 ◽  
Business Process Management ◽  
Manufacturing Industry ◽  
Process Management ◽  
Process Mining ◽  
Smart Factory ◽  
Manufacturing Processes ◽  
Planning And Control ◽  
And Control

Abstract The advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.

The prioritization of technologies and public R&D roles between the manufacturing and service industries in the fourth industrial revolution

foresight ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 680-694 ◽  
Author(s):  
Jinwon Kang ◽  
Jong-Seok Kim ◽  
Seonmi Seol
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Market Failure ◽  
Industrial Revolution ◽  
Service Industry ◽  
Service Industries ◽  
Delphi Survey ◽  
Smart Factory ◽  
Content Type ◽  
Fourth Industrial Revolution

Purpose The purpose of this study is to reveal the similarities and differences between the manufacturing and service industries in their prioritization of technologies and public research and development (R&D) roles, along with the complementation of properties of technology and public R&D role in the context of Fourth Industrial Revolution. Design/methodology/approach Two rounds of Delphi surveys were designed to meet the purpose of this study, which used rigorous triangulation techniques. The Delphi method was combined with the brainstorming method in the first-round Delphi survey, while the second-round Delphi survey focused on experts’ judgments. Finally, language network analysis was performed on the properties of technology and public R&D roles to complement the data analyses regarding prioritization. Findings This study identifies different prioritizations of five similar key technologies in each industry, so that it can note different technological impacts to the two industries in the Fourth Industrial Revolution. Smart factory technology is the first priority in the manufacturing industry, whereas artificial intelligence is the first priority in the service industry. The properties of the three common technologies: artificial intelligence, big data and Internet of things in both industries are summarized in hyper-intelligence on hyper-connectivity. Moreover, it is found that different technological priorities in the service and manufacturing industries require different approaches to public R&D roles, while public R&D roles cover market failure, system failure and government failure. The highest priority public R&D role for the service industry is the emphasis of non-R&D roles. Public R&D role to solve dy-functions, focus basic technologies and support challenging areas of R&D is prioritized at the highest for the manufacturing industry. Originality/value This study of the different prioritizations of technologies in the manufacturing and service industries offers practical lessons for executive officers, managers and policy-makers. They, by noting the different technological impacts in the manufacturing and service industries, can prepare for current actions and establish the priority of technology for R&D influencing the future paths of their industries in the context of the Fourth Industrial Revolution. While managers in the service industry should pay greater attention to the technological content of hyper-intelligence and hyper-connectivity, managers in the manufacturing industry should consider smart factory and robot technology.

scholarly journals An Entropy-Based Formulation for the Support of Sustainable Mass Customization 4.0

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
César Martínez-Olvera
Keyword(s):  
Energy Consumption ◽  
Mass Customization ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Discrete Event ◽  
Energy Productivity ◽  
Event Simulation ◽  
Managerial Implications ◽  
Optimal Resource ◽  
Information And Communication

Industry 4.0, an information and communication umbrella of terms that includes the Internet of Things (IoT) and cyber-physical systems, aims to ensure the future of the manufacturing industry competing in a proper environment of mass customization: demand for short delivery time, high quality, and small-lot products. Within this context of an Industry 4.0 mass customization environment, success depends on its sustainability, where the latter can only be achieved by the manufacturing efficiency of the smart factory-based Industry 4.0 transforming processes. Even though Industry 4.0 is associated with an optimal resource and energy productivity/efficiency, it becomes necessary to answer if the integration of Industry 4.0 elements (like CPS) has a favorable sustainability payoff. This requires performing energy consumption what-if analyses. The original contribution of this paper is the use of the entropy-based formulation as an alternative way of performing the initial steps of the energy consumption what-if analyses. The usefulness of the proposed approach is demonstrated by comparing the results of a discrete-event simulation model of mass customization 4.0 environment and the values obtained by using the entropy-based formulation. The obtained results suggest that the entropy-based formulation acts as a fairly good trend indicator of the system’s performance parameters increase/decrease. The managerial implications of these findings are presented at the end of this document.

scholarly journals DESAIN SISTEM KENDALI UMPAN BALIK STATE PADA KASUS KONTINYU UNTUK MEJA KERJA CNC

2019 ◽  
Vol 20 (2) ◽  
pp. 32
Author(s):  
Fakhruddin Mangkusasmito ◽  
Tsani Hendro Nugroho
Keyword(s):  
Control System ◽  
Rise Time ◽  
Manufacturing Industry ◽  
Position Control ◽  
Control Method ◽  
Tracking System ◽  
Settling Time ◽  
Numerical Control ◽  
Pole Placement ◽  
Work Table

Fakhruddin Mangkusasmito, Tsani Hendro Nugroho in this paper explain that One of the important control system in the manufacturing industry is the position control. Mainly in the Computer Numerical Control (CNC) machine, work-table motion control system is used to regulate work-table movements when the machine process a workpieces on it. On standard machines, work-table movements are two axes (X-Y), which is driven by a motor and lead-screw. The discussion in this research only focus on one axis assuming that the systems on both axes are the same and independent. In this research, MATLAB is used to describe the behaviour of the system and also to design appropriate control system in continuos system using state feedback linear controller such as pole placement , tracking system, full order compensator and reduced order compensator. The goal is to obtain a fast response with a rapid rise time and settling time to a step command, while not exceeding an overshoot of 5%. The specification are than a percent overshoot equal to1%, 0,05s settling time and 0,03s rise time. The performance of each control methods are simulated and analyzed to decide the best suit control method for the systems with such criteria. And the result verify that using tracking system controller method achieve such specification with 0% overshoot, 0,04s settling time and 0,028s rise time.

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