Knowledge-based substep deterministic optimization of large diameter thin-walled Al-alloy tube bending

Abstract

Regarding increasing applications with mass quantities, diverse specifications, and close quality tolerance, the precision bending of large diameter thin-walled (LDTW) Al-alloy tube should be efficiently achieved. However, bending of LDTW Al-alloy tube is a highly tri-nonlinear process with possible multi-defect, needing strict coordination of various bending tools and processing parameters. Considering the coupling effects of various forming parameters on multiple defects, this study developed a knowledge-based substep methodology to solve the deterministic optimization of LDTW Al-alloy tube bending with multi-objective and multi-variable under multiple factor constraints. Considering narrow forming window under small bending radii (Rb < 2D, Rb—bending radius, D—initial tube diameter), a finite element (FE)-based stepwise iterative search method is proposed to optimize key forming parameters of LDTW Al-alloy tube under small Rb, and the search direction is based on bending knowledge. While for large Rb bending with wide optional ranges of forming parameters, a hybrid optimization approach is used by combining virtual design of experiment, FE simulation, approximate response surface model, sequential quadratic programming algorithm, or genetic algorithm. Using orthogonal experimental method, three-dimensional (3D)-FE simulation, experiential data, and analytical formulae, knowledge on key forming parameters, coupling effects on multiple defects, effect significance, and design rules are obtained as well as initial values and design ranges. By several practical bending scenarios with D up to 100 mm, the proposed substep deterministic optimization methodology for LDTW Al-alloy tube bending is evaluated.