Development and application of the material constitutive model in springback prediction of cold-bending

Effect of material scatter on the plastic behavior and stretchability in sheet metal forming

The susceptibility to hydrogen embrittlement (HE) of the 7046 aluminium alloy (AA 7046) was investigated. Samples of AA 7046 corresponding to different ageing temperature/time couples were hydrogenated by cathodic charging in a H2SO4 solution. Scanning Kelvin Probe Force Microscopy (SKPFM) combined with global hydrogen amount measurements allowed apparent hydrogen diffusion coefficients (Dapp) to be measured: the decrease of the Dapp values with the increase of the ageing duration was attributed to hydrogen trapping by hardening η’ and η precipitates for the aged alloy. Additional SKPFM measurements were carried out on hydrogen charged samples after desorption at 25 °C and combined with SEM observations of the fracture surfaces after tensile tests. Results showed that hydrogen could be trapped at the grain boundaries leading to brittle intergranular fracture. However, hardening precipitates could act as efficient trapping sites and reduce hydrogen trapping at the grain boundaries. Conclusion is that the most critical microstructural parameters for HE of AA 7046 correspond to the grain boundaries while ageing could contribute to improve the resistance to HE of the alloy by a well-controlled precipitation.

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Hydrogen diffusion and trapping in a low copper 7xxx aluminium alloy investigated by Scanning Kelvin Probe Force Microscopy

1.1. Vehicle Mass Reduction
New greenhouse gas (GHG) standards for cars and light trucks are taking effect for model year 2017, progressing towards an anticipated sales-weighted average level of 173 g/mile C02 for model year 2025, and fuel economy standards increasing each year to the Corporate Average Fuel Economy (CAFE) target of 51.4 mpg fleet-wide by 2025 (for a projected vehicle sales mix). As a result, vehicle manufacturers are looking for solutions that can meet these goals without sacrificing marketable vehicle attributes (Nehuis et al., 2014; U.S. EPA, 2012a, 2014). Reducing mass enables vehicles to operate more efficiently during the use phase because energy demands (e.g., acceleration, rolling friction) on the powertrain are reduced. This reduction in mass can have major benefits on the total life-cycle impacts of vehicles because the current use phase accounts for 84–88% of the total life-cycle energy consumption and GFIG emissions for conventional light-duty vehicles. Comparatively, the manufacturing contributes approximately 4–7% of the energy consumption over the life of a light-duty vehicle (Keoleian and Sullivan, 2012; Mcauley, 2003; Sullivan and Cobas-Flores, 2001; Sullivan et al., 1998). Because of this dominant contribution of impacts from the use phase, mass reduction efforts and other use-phase efficiency measures provide an effective means to reduce the total life-cycle impacts. Flowever, the share of life-cycle impacts between the production and use phase for vehicles is likely to shift away from the use phase with increasing efficiency and with reduced light-duty vehicle GFIG emissions standards, as shown in the example comparison in Fig. 1






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Fig. 1.

Energy consumption for material production, manufacturing, and use for a light-duty vehicle now and trends for the future.
Total life-cycle energy use will decrease as the proportion of impacts for the material production and manufacturing phases are expected to increase and the use-phase impacts are expected to decrease. This is the expected trend with substitutions of more intensive but lighter materials such as aluminum alloys or carbon fiber replacing less intensive but heavier materials such as mild steel. Stronger, higher strength steels are also expected to displace mild steels by reducing total volumes required. Values for the current chart are a simplified breakdown based on Keoleian and Sullivan (2012), Sullivan and Cobas-Flores (2001), and Sullivan etal. (1998).

Critical factors affecting life cycle assessments of material choice for vehicle mass reduction.

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were a...

Optimization of aluminium sheet hot stamping process using a multi-objective stochastic approach

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.

Influence of material properties variability on springback and thinning in sheet stamping processes: a stochastic analysis

Design of sheet stamping operations to control springback and thinning: A multi-objective stochastic optimization approach

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.

Material Substitution for Automotive Applications: A Comparative Life Cycle Analysis

Computer aided procedures to design and optimize forming processes have become crucial research topics as the industrial interest in cost and time reduction has been increasing. A standalone numerical simulation approach could make the design too time consuming while meta-modeling techniques enables faster approximation of the investigated phenomena, reducing the simulation time. Many researchers are, nowadays, facing such research challenge by using various approaches. Response surface method (RSM) is probably the most known one, since its effectiveness was demonstrated in the past years. The effectiveness of RSM depends both on the definition of the Design of Experiments (DoE) and the accuracy of the function approximation. The number of numerical simulations can be strongly reduced if a proper optimization approach is implemented: one of the main issues about optimization techniques is related to the design necessity of performing either global or local approximation. This paper aims to test the efficacy of some meta-modeling techniques in the optimization of a T-shaped hydroforming process. In this paper three optimization approaches based on different meta-modeling techniques are implemented. In particular, classical Polynomial Regression approach (PR), Moving Least Squares approximation (MLS) and Kriging method are applied. The results showed that, thanks to the peculiarities of MLS and Kriging methods, it is possible to strongly reduce the computational effort in sheet metal forming optimization, particularly in comparison with a classical PR approach. Differences were highlighted and quantified.

A Comparison between Three Meta-Modeling Optimization Approaches to Design a Tube Hydroforming Process

In sheet metal forming most of the problems are multi objective problems, generally characterized by conflicting objectives. The definition of proper parameters aimed to prevent both wrinkles and fracture is a typical example of an optimization problem in sheet metal forming characterized by conflicting goals. What is more, nowadays, a great interest would be focused on the availability of a cluster of possible optimal solutions instead of a single one, particularly in an industrial environment. Thus, the design parameters calibration, accomplishing all the objectives, is difficult and sometimes unsuccessful. In order to overcome this drawback a multi-objectives optimization procedure based on Pareto optimal solution search techniques seems a very attractive approach to deal with sheet metal forming processes design. In this paper, an integration between numerical simulations, response surface methodology and Pareto optimal solution search techniques was applied in order to design a rectangular deep drawing process. In particular, the initial blank shape and the blank holder force history were optimized as design variables in order to accomplish two different objectives: reduce excessive thinning and avoid wrinkling occurrence. The steps of the optimization procedure include: 1) application of Central Composite Design (CCD) for the identification of the necessary data over the domain of variation of the design variables; 2) numerical simulations of the samples identified by CCD; 3) development of a response surface model to interpret the final objectives as functions of the design variables; 4) Pareto optimal solution analysis to reach the most performing design variables. The final aim is to develop a predictive tool able to identify a sort of process window for the analyzed process also minimizing the computational effort in particular with respect to mono-objective optimization techniques or traditional trial and error methods. Many possible technological scenarios were investigated by the implemented procedure and a set of reliable solutions, i.e. able to satisfy different design requirements, were obtained.

Laura Marretta

Papers

Influence of material properties variability on springback and thinning in sheet stamping processes: a stochastic analysis

Design of sheet stamping operations to control springback and thinning: A multi-objective stochastic optimization approach

Material Substitution for Automotive Applications: A Comparative Life Cycle Analysis

A Comparison between Three Meta-Modeling Optimization Approaches to Design a Tube Hydroforming Process

Deep Drawing Process Design: A Multi Objective Optimization Approach