Optimization of Machine Learning for Production and Maintenance Repairs in the Automotive Industry

With the rapid advancement of machine learning (ML) and artificial intelligence (AI), these technologies are increasingly being adopted by scholars, researchers, and automotive manufacturers to optimize processes and outcomes across the industry. This study examines how ML enhances automotive production and maintenance, highlighting its transformative effects and exploring future possibilities. By synthesizing insights from ten key studies and analyzing forward-looking projections, the research concludes that the integration of ML into automotive manufacturing is steadily evolving from theoretical exploration to practical application. ML has become a critical driver for optimizing production processes, improving maintenance efficiency, and fostering innovation within the sector. Although many approaches remain in the experimental phases, the potential of this integration is growing rapidly. The adoption of ML in automotive manufacturing and maintenance represents an inevitable and highly promising trend, paving the way for groundbreaking advancements and a significant impact on the industrys future.

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Publication Details

Journal
Applied and Computational Engineering
Published
2025-04-07
DOI
https://doi.org/10.54254/2755-2721/2025.21900
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimization of Machine Learning for Production and Maintenance Repairs in the Automotive Industry

Zhengwei Gu
1 links
Applied and Computational Engineering
Fault Detection and Control Systems
article

Optimization of Machine Learning for Production and Maintenance Repairs in the Automotive Industry

Zhengwei Gu
article en

Abstract

With the rapid advancement of machine learning (ML) and artificial intelligence (AI), these technologies are increasingly being adopted by scholars, researchers, and automotive manufacturers to optimize processes and outcomes across the industry. This study examines how ML enhances automotive production and maintenance, highlighting its transformative effects and exploring future possibilities. By synthesizing insights from ten key studies and analyzing forward-looking projections, the research concludes that the integration of ML into automotive manufacturing is steadily evolving from theoretical exploration to practical application. ML has become a critical driver for optimizing production processes, improving maintenance efficiency, and fostering innovation within the sector. Although many approaches remain in the experimental phases, the potential of this integration is growing rapidly. The adoption of ML in automotive manufacturing and maintenance represents an inevitable and highly promising trend, paving the way for groundbreaking advancements and a significant impact on the industrys future.

Applied and Computational EngineeringVol. 145(1)
Shanghai Maritime University (CN)
Openalex Percentile: Top 93%
Fault Detection and Control Systems
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Optimization of Machine Learning for Production and Maintenance Repairs in the Automotive Industry — Zhengwei Gu · Applied and Computational Engineering (2025) | TGRS Research Map | TGRS