Cyber Physical Systems for Life Cycle Continuous Technical Documentation of Manufacturing Facilities

Barthelmey, André; Störkle, Denis Daniel; Kuhlenkoetter, Bernd; Deuse, Jochen

doi:10.1016/j.procir.2014.01.050

Cited by 24 publications

(8 citation statements)

References 5 publications

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“…Although the largest revenue potential lies in the extensive operations phase of industrial products, existing CPSs research primarily focuses on the beginning of life (e.g. engineering and manufacturing of an industrial product) [28,36]. Hence, we are motivated to address the use and operations phase (MOL).…”

Section: Background and Related Literaturementioning

confidence: 99%

The Impact of Cyber-physical Systems on Industrial Services in Manufacturing

2015

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Besides selling tangible products, manufacturing companies have also started to compete by offering product-related services throughout the product lifecycle. In this context, the recent rise of cyber-physical systems (CPSs) and smart, connected equipment paves the way for additional opportunities for the service business among the lifecycle and pivots of traditional maintenance, repair and overhaul (M RO) service business. Based on 11 case studies, we investigate service innovations driven by digitalization and CPSs and their impact on the service ecosystem. We identify affordances that effectively exploit the new technological capabilities in existing and future service scenarios.

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Section: Background and Related Literaturementioning

confidence: 99%

The Impact of Cyber-physical Systems on Industrial Services in Manufacturing

2015

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“…Building upon the idea of cyber physical systems, Barthelmey et al [46] describe a system to use both hardware and software to track changes in a manufacturing facility automatically. The goal of this work is to create an up to date set of documentation detailing the capabilities and status of a facility.…”

Section: Resultsmentioning

confidence: 99%

A survey of the advancing use and development of machine learning in smart manufacturing

Sharp

Hedberg

2018

Journal of Manufacturing Systems

211

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Machine learning (ML) (a subset of artificial intelligence that focuses on autonomous computer knowledge gain) is actively being used across many domains, such as entertainment, commerce, and increasingly in industrial settings. The wide applicability and low barriers for development of these algorithms are allowing for innovations, once thought unattainable, to be realized in an ever more digital world. As these innovations continue across industries, the manufacturing industry has also begun to gain benefits. With the current push for Smart Manufacturing and Industrie 4.0, ML for manufacturing is experiencing unprecedented levels of interest; but how much is industry actually using these highly-publicized techniques? This paper sorts through a decade of manufacturing publications to quantify the amount of effort being put towards advancing ML in manufacturing. This work identifies both prominent areas of ML use, and popular algorithms. This also allows us to highlight any gaps, or areas where ML could play a vital role. To maximize the search space utilization of this investigation, ML based Natural Language Processing (NLP) techniques were employed to rapidly sort through a vast corpus of engineering documents to identify key areas of research and application, as well as uncover documents most pertinent to this survey. The salient outcome of this research is the presentation of current focus areas and gaps in ML applications to the manufacturing industry, with particular emphasis on cross domain knowledge utilization. A full detailing of methods and findings is presented.

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“…Ово резултује потребом за постојањем велике количине техничке документације. Велика количина потребне техничке документације као резултат има велике трошкове, ниску ефикасност и дуго трајање процеса креирања документације, али и низак квалитет документације [1]. Аутоматско генерисање дела или све потребне документације на основу формалног описа производног процеса решило би описане проблеме.…”

Section: уводunclassified

Наменски Језик И Окружење За Моделовање Производних Процеса И Генерисање Документације

Тодоровић¹

2020

Zbornik radova FTN

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У овом раду описан је текстуални наменски језик за моделовање производних процеса. Креирани наменски језик омогућава једноставно и брзо креирање формалних, рачунарима разумљивих модела производних процеса неопходних за одвијање процеса производње у паметним фабрикама. За описани наменски језик имплементирани су генератори техничке документације. Генератори омогућавају аутоматско креирање неопходне техничке документације, чиме се смањује време потребно за израду документације, али и повећава њена конзистентност и квалитет. За развој мета-модела кориштено је окружење Eclipse Modeling Framework, радни оквир Xtext за развој текстуалне синтаксе и језик Xtend за развој генератора.

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Cyber Physical Systems for Life Cycle Continuous Technical Documentation of Manufacturing Facilities

Cited by 24 publications

References 5 publications

The Impact of Cyber-physical Systems on Industrial Services in Manufacturing

The Impact of Cyber-physical Systems on Industrial Services in Manufacturing

A survey of the advancing use and development of machine learning in smart manufacturing

Наменски Језик И Окружење За Моделовање Производних Процеса И Генерисање Документације

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