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Lightweight materials have become an important strategy in the automotive industry to enable vehicle weight reduction and reduce fuel consumption. However, when developing specific strategies, the overall benefits of any material should be analyzed throughout its life cycle to comprehend energy/environmental differences that arise during its processing and its final use. A key example is aluminum which despite having great potential in the use phase requires large amounts of energy to process. This paper provides a comparison between aluminum and steel utilizing a life-cycle approach. This approach reveals the importance of incorporating a recycling strategy to leverage aluminum’s low-weight attributes.

A crucial issue in sheet stamping optimization problems is related to the process robustness improvement: critical scattering in the investigated performances arises due to some noise variables influence, often evolving up design failure itself. In fact, strong variations in the final stamped part or fluctuations of strain distribution may lead to an uncontrolled process design. Such variability cannot be controlled but anyway it is possible to develop proper design tools able to identify robust process calibrations above which the noises variations effects are admissible. In this paper, a multi‐objective optimization problem was analyzed, with the aim to minimize both excessive thinning and springback occurrences in a sheet stamping process of an aluminum alloy. In fact, major stamping operations are characterized by conflicting goals to be accomplished, introducing a further difficulty in the process variability control. Two kinds of robust design approaches were compared, a hybrid deterministic‐stochas...

A very critical issue for stamping operations is the improvement of process robustness. The reliability of final results, in fact, strongly depends on the intrinsic variability due to stochastic behavior of many parameters, namely, operative ones and material properties. A given process performance may undergo a variation around the value which would be obtained neglecting stochastic behaviors of the operative or material parameters (i.e., considering a deterministic parameters behavior). Such variation introduces a significant source of uncertainty within the process design: a possible consequence may be the rejection of some stamped parts. In this paper, reliability analyses aimed to evidence and quantify the effects of material coil-to-coil variations on springback and thinning phenomena is proposed. In particular, an aluminum alloy typical of automotive applications was considered and an S-shaped U-channel process was investigated. The stochastic analysis was performed within several operative windows at the varying of restraining forces. Formerly, a sensitivity analysis was carried out in order to evaluate the single effect of each selected material parameter and to screen the most influent ones. Subsequently, a finite element method-response surface methodology-Monte Carlo simulation-integrated approach was implemented to quantify such effects. The proposed methodology provides the possibility to powerfully analyze material variability effect on the final process quality, assisting the designer in a subsequent process optimization.

The aim of this paper is to develop a design tool for stamping processes, which is able to deal with the scattering of the final part quality due to the inner variability of such operations. Such variability is one of the main drawbacks for a robust process design. It results in a scattering of the most significant process results and depends on several parameters. The so called noise factors greatly influence final result variability, which often means rejecting parts and anyway achieving final properties different from the specified ones. The process investigated in the paper is an S-shaped U-channel stamping operation carried out on a lightweight aluminum alloy of automotive interest. The main topic of the paper is the prevention of excessive part thinning and the control of springback phenomena; thus, thinning and springback are the objective functions taken into account. The blank holder force (BHF) value was considered as process design variable while two noise factors were considered: lubricating conditions (represented by the friction coefficient μ ) and strain-hardening index of the material (exponent n in material flow rule). The approach proposed in this paper is a multi-objective optimization problem consisting of an integration among finite element (FEM) numerical simulation, Response Surface Methodology (RSM) and Monte Carlo Simulation (MCS) method. The developed tool starts from a Pareto optimal solutions search technique and takes into account noise factors. The design procedure is able to foresee the potential direction along which a Pareto solution may move due to the effects of the noise factors. In this way, the proposed design tool is fully able to take into account process variability effects and to provide a precise overview of the possible perturbations the analyzed objective functions may undergo.