Digital Twin concept for smart injection molding

Liau, Yee Yeng; Lee, H; Ryu, Kwang Ryel

doi:10.1088/1757-899x/324/1/012077

Cited by 38 publications

(12 citation statements)

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“…A digital twin for hydraulic supports (Xie et al, 2019) is built to simulate the actual hydraulic and to support diagnosis and degradation analysis. The digital twin finds application also in CNC machine tool (Luo et al, 2018) and in smart injection process (Liau et al, 2018) to control the behaviours of the physical system in real-time. The papers {40 Papers} in C8 treat the application of <{Digital Twin in the production phase} for {Performance optimization and validation}>.…”

Section: Digital Twin Paradigmmentioning

confidence: 99%

Digital twin paradigm: A systematic literature review

Semeraro

Lezoche

Panetto

et al. 2021

Computers in Industry

416

127

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Section: Digital Twin Paradigmmentioning

confidence: 99%

Digital twin paradigm: A systematic literature review

Semeraro

Lezoche

Panetto

et al. 2021

Computers in Industry

416

127

View full text Add to dashboard Cite

show abstract

“…A digital twin method was proposed for the rapid personalization of automated flow shop manufacturing systems, combining physical system modeling and semi-physical simulation to generate authoritative digital designs of the system during the pre-production phase [54]. Liau et al [55] applied a digital twin to the injection molding industry, modeling all stages of injection molding as virtual models to achieve a two-way control of physical processes.…”

Section: Digital Production Linementioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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“…With this system, non‐expert engineers can set up the machine in a short time. [ 23 ] The remainder of this article is composed as follows. Section 2 describes the method of data acquisition for simulation, experiment, and geometry.…”

Section: Introductionmentioning

confidence: 99%

Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries

Lee

Park³

et al. 2020

Advanced Intelligent Systems

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Injection molding is a widely used manufacturing process for the mass production of plastic materials. The method consists of a mold, an injection machine, and molten polymer. First, solid polymer pellets are melted via pressure and heating, as it passes through a screw. Then, the molten polymer is injected into a mold and cooled until it solidifies. This process is appropriate for complex-shaped products that require short cycle times. More than 1/3 of all thermoplastic materials are created via injection molding to meet the mass throughput of various product industries, including those of electronics, medical devices, and automobile parts. [1,2] Apart from its limitations (e.g., material selection for high fluidity polymer, surface-quality problems, high costs of machine and mold, and the need for expert process condition control), injection molding still enables high competitiveness because of its overwhelming throughput compared with additive manufacturing and machining. Product quality is greatly influenced by process conditions (e.g., time, pressure, velocity, and temperature), which are set by engineers. [3] In most cases, the process conditions are controlled by field experts based on experience. However, in some cases, an engineer uses computer-aided engineering (CAE) software to optimize the injection molding. [4,5] Simulation-based process optimization can be divided into two methods: direct discrete and metamodel-based methods. [6]

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Digital Twin concept for smart injection molding

Cited by 38 publications

References 1 publication

Digital twin paradigm: A systematic literature review

Digital twin paradigm: A systematic literature review

Digital twin-based sustainable intelligent manufacturing: a review

Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries

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