Modern vehicle integration has become exponentially more difficult due to the complicated structure of designing wiring harnesses for multiple variants that have diverse design iterations and requirements. This paper proposes an AI-driven solution for addressing variant complexity. By using Convolutional Networks and Deep Neural Networks (CNN & DNN) to generate harness routing using defined specifications and constraints, the proposed solution uses minimal human intervention, substantially less time, and enables less complexity in designing. AI trained modelled systems can generally even predict failures in production methods which also reduces downtime and increases productivity. The new AI system automatically converts design specifications to manufacturable design specifications to avoid confusion with design parameters, by optimizing concepts with connector placements, grommet fittings, clip alignments, and other tasks. The solution coping with the inherent dynamic complexity of variant design, is developed to learn the unique design constraints and updates in real-time detailed in a new framework.
As opposed to another static master/slave co-ordinate system, this dynamic AI system takes input parameters like but not limited to; the routing through the shortest spline path of an area with geometry and takes that information to automatically develop a harness network based on practical, and most simply possible design. The learning algorithms allows for intelligently scalable designs through truck variant capability optimization. Continual integration occurs at order booking which allows specific order requirements to automatically integrate into the designs. The system continues to manage the process to ensure the design performs optimally. By removing manual intervention and allowing to automatically adapt to variant configurations, this AI system transforms the wiring harness design process and enhances the scalability of production processes. This research proposes a novel solution for reductions in variant complexity, in a scalable developed from the time being reasonable and accurate harness design approach to the wiring harness for modern trucks.