A study on eco-efficiency of a downsized modular manufacturing system by considering flexibility

Mishima, Nozomu; Kondoh, Shinsuke; Ashida, Kiwamu; Nakano, Shizuka

doi:10.1109/icma.2010.5589435

Cited by 1 publication

(2 citation statements)

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“…Objectives of digital factor; based on the information provided in Refs. (Bicocchi et al , 2019; Bloch et al , 2016; Chandra Sekaran et al , 2021; Horbach et al , 2011; Mishima et al , 2010)…”

Section: Figurementioning

confidence: 99%

“…Although PLCM systems are simple to use, they are not linked to other systems such as data acquisition or controls (Cheng et al, 2018). As most of the PLCMs are developed in this (Bicocchi et al, 2019;Bloch et al, 2016;Chandra Sekaran et al, 2021;Horbach et al, 2011;Mishima et al, 2010) manner, they fail to operate in a heterogeneous industrial environment where data comes from a variety of devices, sectors, views and phases with varying levels of granularity (Shariatzadeh et al, 2016). Their lack of compatibility limits integration and data transfer between them and other devices.…”

Section: Digital Factory Challengesmentioning

confidence: 99%

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A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

Tariq

Joy

et al. 2023

RPJ

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Purpose This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations. Design/methodology/approach This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques. Findings A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions. Originality/value Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

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