AI-Driven Predictive Methodology for Bolt Integrity in Vehicle Durability Testing

The application of AI/ML techniques to predict truck tailgate 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 tailgate 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 tailgate. 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, avoiding costs that can exceed $0.5M, 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.