Digital Twin for Energy Optimization in an SMT-PCB Assembly Line
Abstract
This paper presents a case study for the use of an IoT-driven digital twin for energy optimization in an automated Surface Mount Technology (SMT) PCB assembly line containing legacy machines. The line was instrumented with multiple sensors for measuring machine-wise activity and energy consumption. A software platform for data aggregation and a discrete-event digital twin of the line were built entirely using open-source tools. Based on the insights gained from data collected over several days, we propose a buffering-based solution for improving the energy efficiency of the line, and evaluate its impact using simulations of the digital twin. The results show that a 2.7x reduction in the energy consumption is possible via buffer insertion without significantly affecting line throughput.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Digital twins monitor a machine tool.
“monitoring a machine tool [30,31]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [31]
Proposed improvements are reached by reducing energy consumption.
“The proposed improvements are reached by reducing the energy consumption [37,40]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [37]
In most reviewed cases, DT are developed on a single scope level.
“but in most of the cases DT are usually developed on a single scope level, i.e. [24,30,31].”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [31]
In other reviewed cases, the production system is represented with a Discrete Event System (DES) model.
“in other cases the representation of the production system is done with a Discrete Event System (DES) model [28,37,41,44].”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [37]
Cites
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276 · see citing works
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920 · see citing works
Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing
Tao, F., & Zhang, M. (2017) · IEEE Access · doi:10.1109/access.2017.2756069 · Cited by 1,397 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Review of digital twin applications in manufacturing
Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874 · see citing works
A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418 · see citing works
Reinpold, L. M., Wagner, L. P., Gehlhoff, F., Ramonat, M., Kilthau, M., Gill, M. S., Reif, J. T., Henkel, V., Scholz, L., & Fay, A. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02278-y · Cited by 27 · see citing works
- Authors
- Karanjkar, N., Joglekar, A., Mohanty, S., Prabhu, V., Raghunath, D., & Sundaresan, R.
- Affiliations
- Robert Bosch (India) (IN)
- Venue
- 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 85-89
- OpenAlex
- W2910716232
- Cited by
- 76
- References
- 3
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 12 · 2023: 17 · 2022: 15 · 2021: 15 · 2020: 5 · 2019: 4
- FWCI
- 9.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) (publishedVersion)
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Keywords
- digital twin, energy optimization, surface mount technology, Internet of Things, discrete event simulation, buffer insertion, energy consumption, open source software, data aggregation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
