test_moissonnage_selune
type: Agent

Methods and industrial applications in multicriteria optimization of process parameters in sheet metal forming
type: Distribution
access URL: https://theses.hal.science/tel-00976639
format: HTML
issued: 2026-05-05T15:32:21.921326
modified: 2026-05-05T15:32:21.887165

Methods and industrial applications in multicriteria optimization of process parameters in sheet metal forming
type: Dataset
contact point: Organization
  fn: CCSD
keyword: metaheuristic
keyword: pareto-front
keyword: rupture
keyword: pareto-optimal-solutions
keyword: stamping
keyword: front-de-pareto
keyword: singlemulti-objective-optimization
keyword: theses
keyword: meta-modele
keyword: spiotherengineering-sciences-physicsother
keyword: infoeu-reposemanticsdoctoralthesis
keyword: emboutissage
keyword: retour-elastique
keyword: optimisation-monomulti-objectifs
keyword: springback
keyword: solutions-optimales-de-pareto
keyword: failure
keyword: metaheuristique
landing page: https://theses.hal.science/tel-00976639
description: The processing of sheet metal forming is of vital importance to a large range of industries as production of car bodies, cans, appliances, etc. It generates complex and precise parts. Although, it is an involved technology combining elastic-plastic bending and stretch deformation of the workpiece. These deformations can lead to undesirable problems in the desired shape and performance of the stamped. To perform a successful stamping process and avoid shape deviations such as springback and failure defects, process variables should be optimized.In the present work, the objective is the prediction and the prevention of, especially, springback and failure. These two phenomena are the most common problems in stamping process that present much difficulties in optimization since they are two conflicting objectives. The forming test studied in this thesis concern the stamping of an industrial workpiece stamped with a cross punch. To solve this optimization problem, the approach chosen was based on the hybridization of an heuristic and a direct descent method. This hybridization is designed to take advantage from both disciplines, stochastic and deterministic, in order to improve the robustness and the efficiency of the hybrid algorithm. For the multi-objective problem, we adopt methods based on the identification of Pareto front. To have a compromise between the convergence towards the front and the manner in which the solutions are distributed, we choose two appropriate methods. This methods have the capability to capture the Pareto front and have the advantage of generating a set of Pareto-optimal solutions uniformly spaced. The last property can be of important and practical.
identifier: NNT: 2014NICE4008
issued: 2026-05-05T15:32:21.908628
language: fr
modified: 2026-05-05T15:32:21.908634