Changeable and Reconfigurable Assembly Systems

Lotter, Bruno; Wiendahl, Hans‐Peter

doi:10.1007/978-1-84882-067-8_7

Cited by 34 publications

(18 citation statements)

References 1 publication

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“…Reconfigurable and changeable automation systems, e.g., manufacturing systems [24], assembly systems [33] or machine tools [26], for supporting evolution is an everlasting and intensively studied research topic. Studying co-evolution of product design, production process (workflow) and automation system design is an upcoming, rather novel topic [47,4].…”

Section: Related Workmentioning

confidence: 99%

Delta modeling for variant-rich and evolving manufacturing systems

Kowal

Legat²,

Lorefice

et al. 2014

Proceedings of the 1st International Workshop on Modern Software Engineering Methods for Industrial Automation

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Manufacturing systems exist in many different variants and evolve over time in order to meet changing requirements or environment contexts. This leads to an increased design complexity as well as to increased maintenance effort. In order to appropriately handle this inherent complexity, we propose a multi-perspective modeling approach combining UML activity, component-based and state chart diagrams to separately represent different system aspects. We combine the multi-perspective modeling approach with delta modeling to capture the variability and evolution of these manufacturing systems. Delta modeling allows a flexible, yet concise and understandable representation of variability in a modular manner. We examine our approach by applying it to a manufacturing lab demonstrator system with automated code generation from models obtained by delta application.

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Section: Related Workmentioning

confidence: 99%

Delta modeling for variant-rich and evolving manufacturing systems

Kowal

Legat²,

Lorefice

et al. 2014

Proceedings of the 1st International Workshop on Modern Software Engineering Methods for Industrial Automation

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“…The second stage of the process is a semi-automated, hybrid assembly (Lotter and Wiendahl 2009) segment called backend, including manual and automated workstations. Processes performed at these stations mostly involve the assembly of mechanical components, such as actuators, connectors, springs, etc.…”

Section: Problem Statement and Characteristics Of The Production Envimentioning

confidence: 99%

An online machine learning framework for early detection of product failures in an Industry 4.0 context

Soto

Tavakolizadeh

Gyulai

2019

International Journal of Computer Integrated Manufacturing

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Current paradigms such as the Internet of Things (IoT) and cyber-physical systems are transforming production environments, where related processes are not only faster and with higher standards, but also more flexible and adaptable to changes in the environment. To address the ever-increasing flexibility requirements while keeping current production standards, a new set of technologies is needed. This paper presents an IoT machine learning and orchestration framework, applied to detection of failures of surface mount devices during production. The paper shows how to build a scalable and flexible system for real-time, online machine learning. Furthermore, the approach is evaluated by using a novel and realistic simulation of a production line for electronic devices as a case study. The system evaluation is done in a holistic manner by analyzing various aspects involving the software architecture, computational scalability, model accuracy, production performance, among others.

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“…Coherently with the traditional way of modelling the factory as a number of linked layers, these optimization paths are handled at the Production Management and Manufacturing Execution System (MES) levels by the scientific and industrial communities [25][26][27][28][29][30][31]. In this view, the system logic control is conceived with a very little awareness of the shop-floor and limited options to perform dynamic control adaptations.…”

Section: State Of the Artmentioning

confidence: 99%

Knowledge Based Modules for Adaptive Distributed Control Systems

Ballarino

Brusaferri

Cesta

et al. 2019

Factories of the Future

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Modern automation systems are asked to provide a step change toward flexibility and reconfigurability to cope with increasing demand for fast changing and highly fragmented production-which is more and more characterising the manufacturing sector. This reflects in the transition from traditional hierarchical and centralised control architecture to adaptive distributed control systems, being the latter capable of exploiting also knowledge-based strategies toward collaborating behaviours. The chapter intends to investigate such topics, by outlining major challenges and proposing a possible approach toward their solution, founded on autonomous, self-declaring, knowledge-based and heterarchically collaborating control modules. The benefits of the proposed approach are discussed and demonstrated in the field of re-manufacturing of electronic components, with specific reference to a pilot plant for the integrated End-Of-Life management of mechatronic products. 4.1 Scientific and Industrial Motivations Since several years, the manufacturing industry has been facing a number of technological and production challenges related to the increasing variability of mix and demand of products driven by a short product life cycle. Nevertheless, the growing industrial demand for increased levels of reconfigurability-having impact both in production process automation, supervision and data collection and aggregation systems-is not properly fulfilled, mainly due to the lack of widely accepted solutions

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Changeable and Reconfigurable Assembly Systems

Cited by 34 publications

References 1 publication

Delta modeling for variant-rich and evolving manufacturing systems

Delta modeling for variant-rich and evolving manufacturing systems

An online machine learning framework for early detection of product failures in an Industry 4.0 context

Knowledge Based Modules for Adaptive Distributed Control Systems

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