Cyber-physical production system approach for energy and resource efficient planning and operation of plating process chains

Leiden, Alexander; Herrmann, Christoph; Thiede, Sebastian

doi:10.1016/j.jclepro.2020.125160

Cited by 31 publications

(11 citation statements)

References 26 publications

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“…The outcomes of our systematic review develop on empirical research [1][2][3][4][5][6][7][8][9][10][11]39,40,44,[121][122][123][124][136][137][138][139][140][144][145][146][147][148][157][158][159][160] contending that sustainable cyber-physical production systems developed on functional and behavioral patterns can address the inconveniences of disruptive Industry 4.0-related production environments. Wireless sensor technology monitor manufacturing assets and networked production or logistics business operations in real time.…”

Section: Discussionmentioning

confidence: 99%

“…CPPSs represent an elaborate and fluid network [4][5][6][7] of services and shop floor components (e.g., sensors and actuators), can adjust swiftly to new manufactured items or product variants, can optimize networking among smart connected devices in the production environment, and can provide self-governance, self-organization, and interoperability across smart networked factories, thus optimizing the resilience of manufacturing systems. CPPSs are crucial in advancing sustainable manufacturing Internet of Things and smart factories [8][9][10][11], by harnessing wireless sensor networks for controlling objectives, facilitating integration of industrial data, and supervising and coordinating real objects and operations.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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“…Kurle models heat flows in the production system and included electroplating processes into his dynamic simulation approach [23]. In [24], the authors modelled a plating process chain's energy and resource demands part of a cyber-physical production system. The approach from Thiede enables modelling the energy demand in manufacturing systems and the technical building system [25].…”

Section: Modeling and Simulation Of The Energy And Resource Efficiency In Manufacturingmentioning

confidence: 99%

“…Cleaner production regulations in the electroplating industry System dynamics Heilala et al [22] Environmental impact of discrete manufacturing systems Discrete event model with a life cycle impact assessment Kurle [23] Planning heat flows in production systems Dynamic heat flow model Leiden [24] Energy and resource demand of plating process chains Dynamic energy and resource flow model Thiede [25] Energy flows in manufacturing processes and systems Dynamic energy flow model THERM [26,27] Energy flows in manufacturing systems and building Dynamic energy flow model Xu [28] Chemical/metal flows in electroplating and rinsing process Mass balance with stoichiometric equations…”

Section: Dong Et Al[21]mentioning

confidence: 99%

Synergetic Modelling of Energy and Resource Efficiency as well as Occupational Safety and Health Risks of Plating Process Chains

Leiden

Thiede

Herrmann

2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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To meet the sustainable development goals of the United Nations, the energy and resource efficiency of industrial processes have to increase, and workplaces have to become decent for the involved workers. Plating process chains are typically associated with high energy and resource demand and the use of hazardous chemicals. For the analysis and improvement of the energy and resource efficiency as well as for modelling the occupational safety and health risks, a variety of separate approaches are available. Combined approaches are not available yet. An agent-based simulation is used as the basis for integrated energy and resource as well as occupational safety and health risk assessment. In particular, an energy and resource flow model provides the life cycle inventory data for an environmental assessment. The integration of a mechanistic inhalation exposure model through a surrogate model approach enables a combined synergetic consideration of environmental and occupational safety and health effects. A simulation case study shows the impact of chrome acid changes in chrome electroplating processes as well as the effect of different rinsing cascade settings and rinsing control strategies.

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“…• average duration of power outages [Adeyemi et al, 2020;Ahmad et al, 2021;Dileep, 2020] as a reliability criterion for relevant services; • average share of electricity losses [Xiong et al, 2018;Leiden et al, 2021] as an indicator measuring the state of power grid equipment (affects the rate of digital technologies' application);…”

Section: National Models and Stages Of Digital Transformation In The Electric Power Industrymentioning

confidence: 99%

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2021

Foresight and STI Governance

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The growing share on the aerospace market is due to the increased supply of aircraft components, primarily engines (Figure 5).The changes in the global AM markets' "design" among other things have been caused by the implementation of industrial policies aimed at increasing competitiveness and accelerating growth through structural reforms. However, countries' priorities and implementation methods vary significantly. Industry 4.0 radically changes the understanding of industrial policy vector and stakeholders [Reischauer, 2018]. Germany, China, and the US are striving to maintain leadership by increasing value added in the manufacturing industry. France and Japan localize production, increase its sustainability, and reduce the negative effects of high labor costs. France aims to modernize its manufacturing basis and retain AM leadership, provided it can contain labor costs growth and related social factors [Blanchet et al., 2016]. Russia's industrial policy is mostly vertical in nature, providing selective support and "appointing champions" -industries Таble 1. Classification of AM markets Group Number of markets Composition Share in aggregate AM market (%) Industry 3.0 3 Electronics, optoelectronics, ICT 63.2 Industry 4.0 4 Additive manufacturing, biotechnology, life sciences, flexible production (including robotics) 27.2 Other* 4 New materials, aerospace, nuclear technologies, armaments 9.6* Products not directly related to Industry 3.0 and 4.0. COMTRADE data includes only public information on international trade in armaments. Thus the above estimates do not reflect this market's actual size. However, using this approach makes sense for two reasons. Other available data allows to estimate the overall arms market size without breaking it down into product types (e.g. the SIPRI Arms Transfers Database project). For us, it's important to consider structural shifts not only between markets but also within them, at the level of specific product groups. In line with the adopted approach, the arms market comprises not only armaments and their parts, but also prismatic infrared binoculars, optical telescopes, periscopes, navigation logbooks, and depth sounding equipment.. Source: authors. *To analyse the changes in the aggregate AM market situation, we selected countries at different stages of economic development according to the World Bank classification, including: developed, newly industrialised, and emerging ones, and the BRICS group. Sources: authors' calculations based on COMTRADE data and HS 2002 classification.Таble 4. Selected countries' shares in global imports of advanced AM products (%)*

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Cyber-physical production system approach for energy and resource efficient planning and operation of plating process chains

Cited by 31 publications

References 26 publications

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Synergetic Modelling of Energy and Resource Efficiency as well as Occupational Safety and Health Risks of Plating Process Chains

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