1 option
AI-Driven Predictive Methodology for Bolt Integrity in Vehicle Durability Testing Tata Consultancy Services
- Format:
- Book
- Conference/Event
- Author/Creator:
- Sivakrishna, Masani, author.
- Conference Name:
- Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2026
- Summary:
- The application of AI/ML techniques to predict truck endgate bolt loosening represents a major innovation for the automotive industry, aligning with the principles of Industry 4.0. Traditional physical testing methods are both expensive and time-consuming, often identifying issues late in the development process and necessitating costly design changes and prototype builds. By harnessing AI/ML, manufacturers can now analyze endgate slam and bolt preload data to accurately forecast potential bolt loosening issues. This predictive capability not only enhances quality and safety standards but also significantly reduces the costs associated with tooling and builds. The AI/ML tool described in this paper can simulate a variety of load conditions and predict bolt loosening with over 90% accuracy, considering factors such as changes in loads, bolt diameters, washer sizes, and unexpected masses added to the endgate. It provides valuable design insights, such as recommending optimal bolt diameters and the use of high-friction washers to ensure strong and reliable connections. By enabling continuous monitoring and real-time adjustments, this tool helps maintain the integrity of bolted joints under diverse operational conditions. This methodology reduces dependence on physical testing along with considerable cost avoidance and accelerates the vehicle development process. It offers a more efficient and cost-effective approach to vehicle development. Through the integration of these advanced technologies, the automotive industry can fully embrace the concepts of Industry 4.0, leading to smarter manufacturing processes and improved product reliability
- Notes:
- Vendor supplied data
- Publisher Number:
- 2026-26-0648
- Access Restriction:
- Restricted for use by site license
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.