Self-Learning Production Systems (SLPS) — Energy management application for machine tools

2013 ◽  
Author(s):  
Goncalo Candido ◽  
Giovanni Di Orio ◽  
Jose Barata ◽  
Jose Luiz Bittencourt ◽  
Ralf Bonefeld
Keyword(s):  
Energy Management ◽  
Machine Tools ◽  
Production Systems ◽  
Management Application ◽  
Self Learning

Distributed and Self-learning Approaches for Energy Management

2021 ◽  
pp. 307-327
Author(s):  
Hussein Joumaa ◽  
Khoder Jneid ◽  
Mireille Jacomino
Keyword(s):  
Energy Management ◽  
Learning Approaches ◽  
Self Learning

Making IoT Run

2017 ◽  
Vol 1 (2) ◽  
pp. 26-44
Author(s):  
Peter Schott ◽  
Torben Schaft ◽  
Stefan Thomas ◽  
Freimut Bodendorf
Keyword(s):  
Production Systems ◽  
Smart Manufacturing ◽  
Structural System ◽  
Maturity Level ◽  
Smart Systems ◽  
Industrial Iot ◽  
Increasing Demand ◽  
Manufacturing Environments ◽  
Self Learning

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

Making IoT Run

2020 ◽  
pp. 377-397
Author(s):  
Peter Schott ◽  
Torben Schaft ◽  
Stefan Thomas ◽  
Freimut Bodendorf
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Maturity Level ◽  
Smart Systems ◽  
Industrial Iot ◽  
Increasing Demand ◽  
Manufacturing Environments ◽  
Self Learning

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

scholarly journals Dynamic Residential Energy Management for Real-Time Pricing

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2562
Author(s):  
Leehter Yao ◽  
Fazida Hanim Hashim ◽  
Chien-Chi Lai
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Smart Home ◽  
Solar Power ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Learning Scheme ◽  
Home Energy Management ◽  
Self Learning

A home energy management system (HEMS) was designed in this paper for a smart home that uses integrated energy resources such as power from the grid, solar power generated from photovoltaic (PV) panels, and power from an energy storage system (ESS). A fuzzy controller is proposed for the HEMS to optimally manage the integrated power of the smart home. The fuzzy controller is designed to control the power rectifier for regulating the AC power in response to the variations in the residential electric load, solar power from PV panels, power of the ESS, and the real-time electricity prices. A self-learning scheme is designed for the proposed fuzzy controller to adapt with short-term and seasonal climatic changes and residential load variations. A parsimonious parameterization scheme for both the antecedent and consequent parts of the fuzzy rule base is utilized so that the self-learning scheme of the fuzzy controller is computationally efficient.

An integrated framework for combined designing dematerialised machine tools and production systems enabling flexibility-oriented business models

2014 ◽  
Vol 28 (4) ◽  
pp. 353-363 ◽  
Author(s):  
Giacomo Copani ◽  
Marco Leonesio ◽  
Lorenzo Molinari Tosatti ◽  
Stefania Pellegrinelli ◽  
Marcello Urgo ◽  
...  
Keyword(s):  
Machine Tools ◽  
Business Models ◽  
Production Systems ◽  
Integrated Framework

A Digital Twin of Multi-Axis Machine Tool for Micro Process Planning

2020 ◽  
Author(s):  
Fumiki Tanaka
Keyword(s):  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
High Performance ◽  
Production Systems ◽  
Planning System ◽  
Digital Twin ◽  
Virtual Machining ◽  
Process Planning System ◽  
Case Data

Abstract Achieving high performance of machining production systems requires the use of multi-axis machine tools. In order to maximize the performance of multi-axis machine tools, micro process planning for creating machining data is important. Many researches on micro process planning mainly focused on 3-axis machining. As promising approaches among them, a micro process planning system was proposed that reuses actual machining cases and analyzes case data to derive the necessary rules. However, it is not always effective for multi-axis machining, because enough case data are not collected for micro process planning of a specific multi-axis machine tool. In this study, a digital twin of multi-axis machine tool in cyberspace is proposed to collect real and virtual machining case data for micro process planning.

Optimizing the Reconfiguration of Machining Desktop Micro-Factory Based on Scheduling Simulation

2016 ◽  
pp. 494-517
Author(s):  
Roberto Pérez ◽  
José Eduardo Márquez ◽  
Arturo Molina ◽  
Miguel Ramírez-Cadena ◽  
Ricardo Del Risco ◽  
...  
Keyword(s):  
Machine Tools ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Sustainable Growth ◽  
The Novel ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Scheduling Optimization ◽  
New Findings ◽  
Micro Machine

Today, the micro-factory concept of downsizing production systems is essential to manufacturing small products in sustainable growth. Concerning this, this paper presents the developments accomplished during the recent years at Tecnológico de Monterrey (Mexico) and Holguin University (Cuba) introducing new findings related to the design of reconfigurable micro-factories based-on micro-machine tools. The chapter discusses the proposed framework for the optimizing the development of micro-factories in the context of micro-reconfigurable manufacturing systems based-on micro-reconfigurable machine tools. The novel methodology for optimizing the scheduling of reconfigurable micro-factories were exposed and a scheduling optimization of a reconfigurable micro-factory prototype was designed and tested.

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