A machining digital twin for hybrid manufacturing

Dvorak, Jake; Cornelius, Aaron; Corson, Greg; Zameroski, Ross; Jacobs, Leah A.; Penney, Joshua; Schmitz, Tony L.

doi:10.1016/j.mfglet.2022.07.097

Cited by 18 publications

(5 citation statements)

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“…A general-purpose DT requires a full and "loyal" representation of the physical system regardless of the searched value and usage [30]. This breaks the feasibility of such project at the first modeling constraint (e.g., modeling the physical laws behind a complex industrial machine).…”

Section: The "Specific-purpose Digital Twin" As Key For Fast Adoptionmentioning

confidence: 99%

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

Khdoudi,

Masrour,

El Hassani

et al. 2024

Systems

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In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector.

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Section: The "Specific-purpose Digital Twin" As Key For Fast Adoptionmentioning

confidence: 99%

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

Khdoudi,

Masrour,

El Hassani

et al. 2024

Systems

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“…This work explores the implementation of hybrid manufacturing for BJAM SiC samples and builds on initial work reported by Dvorak et al [19]. Related works that collectively discuss this scenario are summarized in the following paragraphs.…”

Section: Introductionmentioning

confidence: 98%

“…Cornelius et al demonstrated the hybrid manufacturing method for the five-axis computer numerical control (CNC) machining of a wire arc additive manufacturing (WAAM) component [25]. Dvorak et al used the same hybrid manufacturing method for the threeaxis machining of a WAAM preform with a focus on how the methodology provides essential components of a machining digital twin for hybrid manufacturing [26]. Mali et al completed a comprehensive review of freeform surface milling [27].…”

Section: Introductionmentioning

confidence: 99%

Freeform Hybrid Manufacturing: Binderjet, Structured Light Scanning, Confocal Microscopy, and CNC Machining

Dvorak

Gilmer

Zameroski

et al. 2023

JMMP

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This paper describes a hybrid manufacturing approach for silicon carbide (SiC) freeform surfaces using binder jet additive manufacturing (BJAM) to print the preform and machining to obtain the design geometry. Although additive manufacturing (AM) techniques such as BJAM allow for the fabrication of complex geometries, additional machining or grinding is often required to achieve the desired surface finish and shape. Hybrid manufacturing has been shown to provide an effective solution. However, hybrid manufacturing also has its own challenges, depending on the combination of processes. For example, when the subtractive and additive manufacturing steps are performed sequentially on separate systems, it is necessary to define a common coordinate system for part transfer. This can be difficult because AM preforms do not inherently contain features that can serve as datums. Additionally, it is important to confirm that the intended final geometry is contained within the AM preform. The approach described here addresses these challenges by using structured light scanning to create a stock model for machining. Results show that a freeform surface was machined with approximately 70 µm of maximum deviation from that which was planned.

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“…Digital twin models are the prerequisite for digital twin applications, and their quality directly affects the quality of digital twin services in monitoring, simulation, prediction, optimization, and other areas. [2].The digital twin of the machine makes it easy for external engineers to program it [3]. Digital twins are already very closely linked to CNC machines and are gradually being applied in various fields [4].…”

Section: Introductionmentioning

confidence: 99%