Short answer

Adopt a systematic, ontology-driven approach to digital twin development and prioritize automation in the deployment phase to increase manufacturing system responsiveness.

Field: Commercial Production
Source: Journal of Intelligent Manufacturing (2021)
Method: Conceptual framework development and case study application.
Evidence: Strong effect

A standardized, ontology-driven pipeline for creating and deploying digital twins can significantly reduce the complexity and time required for manufacturers to adapt to individualized product demands. This commercial production research insight is drawn from a 2021 study published in Journal of Intelligent Manufacturing. Using Conceptual framework development and case study application., researchers explored how this design variable affects real-world outcomes. The key design takeaway: Adopt a systematic, ontology-driven approach to digital twin development and prioritize automation in the deployment phase to increase manufacturing system responsiveness.

Study

Commercial ProductionHigh ImpactStrong effect

Automated Digital Twin Deployment Accelerates Manufacturing System Agility

Journal of Intelligent Manufacturing · 2021

Key Findings

01A consistent workflow from ontology-driven definition to standardized modeling of digital twins is currently lacking.
02An end-to-end digital twin pipeline can lower the barrier to entry for creating and deploying digital twins.
03Automation concepts, by providing structured protocol data, can streamline the repetitive task of establishing communication connections for digital twins.

Application

Design takeaway

Adopt a systematic, ontology-driven approach to digital twin development and prioritize automation in the deployment phase to increase manufacturing system responsiveness.

How to apply

When designing or implementing digital twin solutions, begin by establishing a clear ontology for data representation and explore opportunities to automate the connection and deployment processes.

Project actions

01Consider using ontologies to define the structure and relationships of data for your design project.
02Think about how you can automate repetitive tasks in your design or testing process.

Method & Evidence

AimTo develop and demonstrate an end-to-end pipeline for ontology-based modeling and automated deployment of digital twins to enhance the planning and control of adaptable manufacturing systems.

MethodConceptual framework development and case study application.

ProcedureThe research defines a three-phase pipeline: ontology-based schema definition, standardized data modeling, and automated deployment with communication protocols. This pipeline was then applied and explained using a use-case involving a line-less assembly system with manual stations, a mobile robot, and an industrial dog as the product.

ContextManufacturing systems, specifically adaptable and flexible production environments driven by individualized product demands.

Variables

IVOntology-based modeling pipeline, automated deployment concept.

DVEase of creation and deployment of digital twins, adaptability and control of manufacturing systems.

CVType of manufacturing system (line-less assembly), specific resources (manual stations, mobile robot), product type (industrial dog).

Strengths & Limitations

Strengths

+Provides a clear, end-to-end pipeline for digital twin development.
+Demonstrates practical application through a relevant use-case.

Limitations

The complexity of implementing a full ontology-based system and automated deployment might be challenging for smaller-scale projects.

Reliability & validity

The study's validity is supported by its application to a specific use-case, demonstrating the practical feasibility of the proposed pipeline. Reliability would depend on the consistency of results when applied to other similar manufacturing scenarios.

Think critically

How might the initial effort in defining a robust ontology impact the perceived 'lowered threshold' for digital twin creation in the short term?

Design Principles

"Standardization and automation are key enablers for agile manufacturing through digital twin technology."

In today's market, the ability to quickly reconfigure manufacturing processes is crucial for meeting diverse customer needs. Digital twins offer a powerful way to simulate and manage these changes, but their implementation can be resource-intensive. This research highlights a method to streamline their creation and deployment, making advanced manufacturing control more accessible.

What This Means for Your Design

This research shows how to make digital twins (digital copies of real-world systems) easier to build and use in factories, especially when you need to change production quickly for custom products.

How to use in your project

1.Reference this paper when discussing the importance of standardization and automation in the development of complex systems like digital twins for your design project.

Add to My Project

Quick Cite

Paragraph starter

The research by Göppert et al. (2021) emphasizes the need for standardized, ontology-driven pipelines for the creation and automated deployment of digital twins. This approach is crucial for enhancing the adaptability and control of modern manufacturing systems, particularly in response to demands for individualized products, by streamlining data interoperability and reducing implementation complexity.

Source

Journal of Intelligent Manufacturing

Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems

journal · 2021

View source

Questions About This Research

What does the research say about automated digital twin deployment accelerates manufacturing system agility?: Adopt a systematic, ontology-driven approach to digital twin development and prioritize automation in the deployment phase to increase manufacturing system responsiveness. Evidence: Journal of Intelligent Manufacturing (2021).
Why does "Automated Digital Twin Deployment Accelerates Manufacturing System Agility" matter for design?: In today's market, the ability to quickly reconfigure manufacturing processes is crucial for meeting diverse customer needs. Digital twins offer a powerful way to simulate and manage these changes, but their implementation can be resource-intensive. This research highlights a method to streamline their creation and deployment, making advanced manufacturing control more accessible.
How can designers apply this research?: Adopt a systematic, ontology-driven approach to digital twin development and prioritize automation in the deployment phase to increase manufacturing system responsiveness.
What were the main findings?: A consistent workflow from ontology-driven definition to standardized modeling of digital twins is currently lacking.. An end-to-end digital twin pipeline can lower the barrier to entry for creating and deploying digital twins.. Automation concepts, by providing structured protocol data, can streamline the repetitive task of establishing communication connections for digital twins.
What research method was used?: Conceptual framework development and case study application..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2021 journal from Journal of Intelligent Manufacturing.
What should I do differently in my next project?: When designing or implementing digital twin solutions, begin by establishing a clear ontology for data representation and explore opportunities to automate the connection and deployment processes.
What are the limitations?: The presented pipeline and automation concept were demonstrated on a specific use-case, and further validation across a wider range of manufacturing scenarios may be necessary.