Sheet metal forming, as one of the most important branches of metal forming, has a special place in various industrial fields. Improving the quality of deep drawn parts, in terms of uniformity in the thickness distribution, is still an open question. The present research work proposes a methodology to improve the overall quality in light components manufactured by sheet hydroforming assisted by radial pressure. The improvement of the final quality was achieved by exploiting the potentialities of adaptive neural-fuzzy inference system (ANFIS) combined with the multiattribute decision-making (MADM) methods. Results from numerical simulations were used to optimize the process and state the optimal values of the most influencing process parameters, namely the die entrance radius, the punch tip radius and the radial clearance between the punch and die. According to ANFIS–MADM hybrid technique outcomes, the average weights of thickness strain and thickness variation were computed to 39% and 61%, respectively. In the optimized case, the amounts of thickness strain and thickness variation were calculated to, in turn, 0.182 and 2.118. The integration of an AI-based methodology in the optimization of a sheet metal forming process can be regarded as a preliminary step toward a smarter and more cost-effective, precise and sustainable sheet metal forming process.
