Autonomy for Smart Manufacturing

Park, Hae‐Sim; Tran, Ngoc-Hien

doi:10.7736/kspe.2014.31.4.287

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…The changes that trigger Industry 4.0 have impacted different domains throughout the value chain. First, an autonomous system—embedding smart components in CPSs equipped with autonomous capability—achieves a specified goal independently without any human intervention (Gamer et al, 2020 ; Park & Tran, 2014 ). However, human intelligence and intervention remain a key role because of the safety, security, social aspects and uncertainties posed by such autonomous systems (Fosch-Villaronga et al, 2020 ; Gil et al, 2019 ; Nahavandi, 2017 ; Santoni de Sio & van den Hoven 2018 ; Weichhart et al, 2019 ).…”

Section: Human-centred Design In Industry 40mentioning

confidence: 99%

“…Product life cycles are getting shorter while production batch sizes are getting smaller with dynamic product variants associated with increasing complexity, which is challenging the traditional production systems (Benabdellah et al, 2019 ; Kuhnle et al, 2021 ; Ma et al, 2017 ; Prinz et al, 2019 ; Windt et al, 2008 ; Zhu et al, 2015 ). To manage these dynamics, the industrial concept of Industry 4.0 has come about and has been accepted in both research and industry, a trend linked to digitalization and smart systems that could enable factories to achieve higher production variety with reduced downtimes while improving yield, quality, safety, and decreasing cost and energy consumption (García-Magro & Soriano-Pinar, 2019 ; Järvenpää et al, 2019 ; Napoleone et al, 2020 ; Oztemel & Gursev, 2020 ; Park & Tran, 2014 ). Although the adoption of Industry 4.0 in manufacturing reveals positive outcomes, the increased complexity as a collateral effect has also brought many challenges (Bednar & Welch, 2020 ; Cohen et al, 2019 ; Fernandez-Carames & Fraga-Lamas, 2018 ; Mourtzis et al, 2018 ; Wittenberg, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Human-centred design in industry 4.0: case study review and opportunities for future research

2021

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The transition to industry 4.0 has impacted factories, but it also affects the entire value chain. In this sense, human-centred factors play a core role in transitioning to sustainable manufacturing processes and consumption. The awareness of human roles in Industry 4.0 is increasing, as evidenced by active work in developing methods, exploring influencing factors, and proving the effectiveness of design oriented to humans. However, numerous studies have been brought into existence but then disconnected from other studies. As a consequence, these studies in industry and research alike are not regularly adopted, and the network of studies is seemingly broad and expands without forming a coherent structure. This study is a unique attempt to bridge the gap through the literature characteristics and lessons learnt derived from a collection of case studies regarding human-centred design (HCD) in the context of Industry 4.0. This objective is achieved by a well-rounded systematic literature review whose special unit of analysis is given to the case studies, delivering contributions in three ways: (1) providing an insight into how the literature has evolved through the cross-disciplinary lens; (2) identifying what research themes associated with design methods are emerging in the field; (3) and setting the research agenda in the context of HCD in Industry 4.0, taking into account the lessons learnt, as uncovered by the in-depth review of case studies.

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Section: Human-centred Design In Industry 40mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human-centred design in industry 4.0: case study review and opportunities for future research

2021

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“…Some themes proposed the lifecycle of SMSs (Barkmeyer and Wallace, 2016) and the related methods of SMSs including adaptive critic methods (Shervais et al, 2011) enable SM research and development methods (Helu and Hedberg, 2015), the components of SMSs including services (Thomas and Trentesaux, 2014), multi-stakeholder (Papazoglou et al, 2015), operations planning and control (Thompson, 2014), and reconfigurable technologies (Sanderson et al, 2016). Others often focus on the SMSs’ benefits and barriers to adoption, available methods, architecture, and autonomy (Kusiak, 2017; Park and Tran, 2014). However, research on how to develop specific SMSs is still scarce in the past.…”

Section: State Of the Artmentioning

confidence: 99%

Integrating fuzzy Kano model and fuzzy analytic hierarchy process to evaluate requirements of smart manufacturing systems

Qu¹,

Ming²,

Qiu³

et al. 2019

Concurrent Engineering

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In this article, an integrated method is proposed for evaluating the requirements of smart manufacturing systems under the era of Industry 4.0 and Industrial Internet of Things. This method uses systematic research on identifying, classifying, and evaluating the requirements of smart manufacturing systems with uncertainty, multi-users, and multi-disciplines. The results of this article provide a procedure to capture the preferential SMSs-R and the framework of SMSs-R. First, this method provides a comprehensive requirement of smart manufacturing identified by questionnaire survey, document reviews, and the affinity diagram method. Second, the classification of SMSs-R considers the multi-user preference by the fuzzy Kano model. Third, the best non-fuzzy performance and fuzzy analytic hierarchy process method is used for evaluating and ranking the SMSs-R factor to solve the uncertain, vague, and preferential questions. Finally, we validated the effectiveness of the method through a real discrete manufacturing enterprise case.

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“…These characteristics are the building blocks for the manufacturing system to transform into smart manufacturing. These design features are expressed in the sub-features by multiple authors like [2][3][4][5][6][7][8][9][10][11][12] are summarized in Figure 1. The similar characteristics are shown by lean production system including flexibility, adaptability, agility and automation.…”

Section: Introductionmentioning

confidence: 99%

Development of Innovative Operational Flexibility Measurement Model for Smart Systems in Industry 4.0 Paradigm

et al. 2022

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The exponential growth of cutting-edge technologies continuously pushing the manufacturing industry into the paradigm of smart manufacturing. Smart manufacturing provides the pace of highly competitive market demand and customized intensive production. The goal of smart manufacturing technologies embedded systems in the Industry 4.0 paradigm and lean production is to enhance the flexibility in all tears of the enterprise. It is a big challenge, to measure the flexibility of smart systems for decisionmaking and adaptation of the new manufacturing technologies. The conceptual architecture of smart manufacturing systems has been proposed to solve the problem. Operational flexibility has been measured using a mathematical model for smart manufacturing in the Open Platform Communication Unified Architecture enabled Cyber-Physical Production System at shop floor level and validated. The results obtained from experimentation depict the operational flexibility, maximum capacity, and breakeven point of the manufacturing system have been improved by using smart manufacturing technologies. The proposed model improves the product manufacturing using Smart Computer Numeric Control Machining, Smart Autonomous Robotic Machining, Smart Additive Manufacturing and Smart Hybrid Additive & Subtractive Manufacturing up to 30.4%, 53.6%, 55% and 65% respectively. It will also help the decision-makers to overcome the challenges of transformation from conventional to smart manufacturing industry 4.0 paradigm.

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Autonomy for Smart Manufacturing

Cited by 20 publications

References 8 publications

Human-centred design in industry 4.0: case study review and opportunities for future research

Human-centred design in industry 4.0: case study review and opportunities for future research

Integrating fuzzy Kano model and fuzzy analytic hierarchy process to evaluate requirements of smart manufacturing systems

Development of Innovative Operational Flexibility Measurement Model for Smart Systems in Industry 4.0 Paradigm

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