Design and Development of Digital Twin of Single Cell Manufacturing Unit using Delmia

The rising demand for smart manufacturing has accelerated the adoption of Digital Twin (DT) technology, enabling real-time monitoring, simulation, and process optimization in industrial settings. This research focuses on developing a Digital Twin model for a Single Cell Manufacturing Unit (SCMU) using DELMIA, a widely recognized digital manufacturing platform. The proposed virtual model accurately represents the manufacturing cell, analyzing key factors such as cycle time, workstation utilization, material flow, and energy consumption. Through iterative simulations and data-driven optimization, the Digital Twin framework enhances decision-making, reduces inefficiencies, and improves overall productivity. The research methodology involves creating a 2D layout, transforming it into a 3D model, and conducting process simulations within DELMIA to assess different configurations. A comparative analysis of multiple iterations helps determine the most efficient and operational-effective manufacturing setup. This study underscores the importance of Digital Twin technology in enhancing single-cell manufacturing processes, lowering operational constraints, and fostering data-driven, intelligent production systems in alignment with Industry 5.0 principles.

Authors

Institutions

Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2025-04-24
DOI
https://doi.org/10.48175/ijarsct-25601
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design and Development of Digital Twin of Single Cell Manufacturing Unit using Delmia

S. Saravanan, M. Kannan, Arun Kumar G, Dr A.R Arvind et al.
International Journal of Advanced Research in Science Communication and Technology
Digital Transformation in Industry
article

Design and Development of Digital Twin of Single Cell Manufacturing Unit using Delmia

S. Saravanan, M. Kannan, Arun Kumar G, Dr A.R Arvind, S M Avinash, E Gurunathraj
article en

Abstract

The rising demand for smart manufacturing has accelerated the adoption of Digital Twin (DT) technology, enabling real-time monitoring, simulation, and process optimization in industrial settings. This research focuses on developing a Digital Twin model for a Single Cell Manufacturing Unit (SCMU) using DELMIA, a widely recognized digital manufacturing platform. The proposed virtual model accurately represents the manufacturing cell, analyzing key factors such as cycle time, workstation utilization, material flow, and energy consumption. Through iterative simulations and data-driven optimization, the Digital Twin framework enhances decision-making, reduces inefficiencies, and improves overall productivity. The research methodology involves creating a 2D layout, transforming it into a 3D model, and conducting process simulations within DELMIA to assess different configurations. A comparative analysis of multiple iterations helps determine the most efficient and operational-effective manufacturing setup. This study underscores the importance of Digital Twin technology in enhancing single-cell manufacturing processes, lowering operational constraints, and fostering data-driven, intelligent production systems in alignment with Industry 5.0 principles.

International Journal of Advanced Research in Science Communication and Technology
Sri Venkateswara University (IN), Ashok Leyland (India) (IN)
Openalex Percentile: Top 89%
Digital Transformation in Industry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.