Industry 4.0 in Automotive Manufacturing: A Digital Twin Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study introduces a Digital Twin framework for real-time monitoring and decision-making in automotive strut tower manufacturing via automated high-pressure die casting. As a key enabler of Industry 4.0, the Digital Twin concept integrates physical and digital domains, allowing bi-directional communication for process optimization. The proposed model collects real-time data from sensors in the manufacturing cell and utilizes FlexSim simulation software alongside machine learning algorithms, specifically Artificial Neural Networks (ANN) and Logistic Regression, to classify product outcomes. The ANN achieved high accuracy, supporting rapid, data-driven decision-making. Additionally, FlexSim’s Emulation tool and Open Platform Communications United Architecture (OPC UA) protocol enable seamless communication between the physical and digital systems, ensuring consistent and reliable performance. Results demonstrate the effectiveness of the Digital Twin in enhancing manufacturing efficiency and predictive accuracy, highlighting its potential for broader applications across various manufacturing stages and industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it