In the early stages of vehicle suspension design and development, decisions are generally made regarding suspension architecture within the constraints of a given packaging space, vehicle weight, and required travel. Kinematic characteristics are traditionally derived from a disciplined model. A disciplined model is a software program that studies the behavior of interconnected rigid and flexible mechanical components as they undergo translational and rotational displacements resulting from applied forces or motion as measured by displacement, velocity, and acceleration. We design the suspension based on the data produced using the disciplined models. This paper helps the community of automotive engineers to use artificial intelligence and make their design process fast.

With the rise of artificial intelligence, the automotive industry searched for novel ways to improve future product design. We focus on designing automatic MacPherson suspension architecture for the automotive sector. It takes time for an automotive engineer to design vehicle parts and thus slows the pace of innovation in this field. Given the car's particular kinematic characteristics, we propose to predict an architecture by positioning the hardpoints. This work deals with the biased data generated using the discipline models using the dataset shift learning paradigm. The optimized data are created with random and uniform sampling, with more samples with random sampling. We resolve the bias in the data, using a novel criterion for tuning the kernel mean matching and a weight estimation algorithm and designing the required target characteristics.