Recent Developments and Future Challenges in Incremental Sheet Forming of Aluminium and Aluminium Alloy Sheets

Trzepieciński, Tomasz; Najm, Sherwan Mohammed; Oleksik, Valentin; Vasilca, Delia; Paniti, Imre; Szpunar, Marcin

doi:10.3390/met12010124

Cited by 25 publications

(17 citation statements)

References 199 publications

(265 reference statements)

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“…Due to the lack of a need to use an additional material, which increases the weight of the structure and the possibility of welding aluminium alloys of the 2xxx, 6xxx, and 7xxx series [ 11 , 12 ], FSW is increasingly used in the aircraft industry. The FSW method was used for the production of the wing panels and the fuselage of the Eclipse 500 aircraft, the rear ramp of the Boeing C-17 Globemaster III transport plane, the fuselage panels of the Airbus A380, and the construction of the Boeing 747 Large Cargo Freighter [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

et al. 2022

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The aim of this research was the selection of friction stir welding (FSW) parameters for joining stiffening elements (Z-stringers) to a thin-walled structure (skin) made of 1 mm-thick EN AW-2024 T3 aluminium alloy sheets. Overlapping sheets were friction stir welded with variable values of welding speed, pin length (plunge depth), and tool rotational speed. The experimental research was carried out based on a three-factor three-level full factorial Design of Experiments plan (DoE). The load capacity of the welded joints was determined in uniaxial tensile/pure shear tests. Based on the results of the load capacity of the joint and the dispersion of this parameter, multi-criteria optimisation was carried out to indicate the appropriate parameters of the linear FSW process. The optimal parameters of the FSW process were determined based on a regression equation assessed by the Fisher–Senecor test. The vast majority of articles reviewed concern the optimisation of welding parameters for only one selected output parameter (most often joint strength). The aim of multi-criteria optimisation was to determine the most favourable combination of parameters in terms of both the smallest dispersion and highest load capacity of the joints. It was found that an increase in welding speed at a given value of pin length caused a decrease in the load capacity of the joint, as well as a significant increase in the dispersion of the results. The use of the parameters obtained as a result of multi-criteria optimisation will allow a minimum load capacity of the joints of 5.38 kN to be obtained with much greater stability of the results.

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Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

et al. 2022

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“…First of all, the drawpieces show a relatively large springback; incorrectly selected input parameters may result in unsatisfactory roughness of the inner surface of the drawpiece. In areas with small rounding radii, a reduction in the geometric accuracy of the products is observed [17,18].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Surface Roughness in Incremental Sheet Forming of Conical Drawpieces from Pure Titanium Sheets

et al. 2022

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The article presents the results of the analysis of the influence of incremental sheet forming process parameters on surface roughness measured on both sides of conical drawpieces made from pure titanium Grade 2 sheets. The experimental plan was created on the basis of a central composite design. The study assumed the variability of feed rate, spindle speed, and incremental step size in the following range: 500–2000 mm/min, 0–600 rpm, and 0.1–0.5 mm, respectively. Two strategies differing in the direction of the tool rotation in relation to the feed direction were also analysed. Analysis of variance is performed to understand the adequacy of the proposed model and the influence of the input parameters on the specific roughness parameter. The sensitivity of the process parameter on the selected surface roughness parameters was assessed using artificial neural networks. It was found that the change in the surface roughness of the inner surface of the drawpiece is not related to the change of surface roughness of the outer side. The morphology of the outer surface of the draw pieces was uniform with a much greater profile height than the inner surface that had interacted with the tool. Taking into account the outer surface of the drawpiece, the direction of tool rotation is also most closely correlated with the parameters Sa, Sz, and Sku. Step size and feed rate provide the highest information capacity in relation to skewness and kurtosis of the inner surface of the drawpiece.

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“…The first patent mentioned before can be considered a SPIF, and TPIF was first presented by Matsubara (Matsubara, 1994). SPIF is more appropriate than conventional sheet-forming methods to manufacture prototypes, small production batches, and customized components (Mezher et al, 2018;Paniti et al, 2020;Trzepieciński et al, 2022a). SPIF is flexible and with its help it is very simple to produce complex geometries by following a programmed path strategy using a CNC machine with at least three controlled axes (Trzepieciński et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

Najm

Paniti

2022

J Intell Manuf

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Today the topic of incremental sheet forming (ISF) is one of the most active areas of sheet metal forming research. ISF can be an essential alternative to conventional sheet forming for prototypes or non-mass products. Single point incremental forming (SPIF) is one of the most innovative and widely used fields in ISF with the potential to form sheet products. The formed components by SPIF lack geometric accuracy, which is one of the obstacles that prevents SPIF from being adopted as a sheet forming process in the industry. Pillow effect and wall displacement are influential contributors to manufacturing defects. Thus, optimal process parameters should be selected to produce a SPIF component with sufficient quality and without defects. In this context, this study presents an insight into the effects of the different materials and shapes of forming tools, tool head diameters, tool corner radiuses, and tool surface roughness (Ra and Rz). The studied factors include the pillow effect and wall diameter of SPIF components of AlMn1Mg1 aluminum alloy blank sheets. In order to produce a well-established study of process parameters, in the scope of this paper different modeling tools were used to predict the outcomes of the process. For that purpose, actual data collected from 108 experimentally formed parts under different process conditions of SPIF were used. Neuron by Neuron (NBN), Gradient Boosting Regression (GBR), CatBoost, and two different structures of Multilayer Perceptron were used and analyzed for studying the effect of parameters on the factors under scrutiny. Different validation metrics were adopted to determine the quality of each model and to predict the impact of the pillow effect and wall diameter. For the calculation of the pillow effect and wall diameter, two equations were developed based on the research parameters. As opposed to the experimental approach, analytical equations help researchers to estimate results values relatively speedily and in a feasible way. Different partitioning weight methods have been used to determine the relative importance (RI) and individual feature importance of SPIF parameters for the expected pillow effect and wall diameter. A close relationship has been identified to exist between the actual and predicted results. For the first time in the field of incremental forming study, through the construction of Catboost models, SHapley Additive exPlanations (SHAP) was used to ascertain the impact of individual parameters on pillow effect and wall diameter predictions. CatBoost was able to predict the wall diameter with R2 values between the range of 0.9714 and 0.8947 in the case of the training and testing dataset, and between the range of 0.6062 and 0.6406 when predicting pillow effect. It was discovered that, depending on different validation metrics, the Levenberg–Marquardt training algorithm performed the most effectively in predicting the wall diameter and pillow effect with R2 values in the range of 0.9645 and 0.9082 for wall diameter and in the range of 0.7506 and 0.7129 in the case of the pillow effect. NBN has no results worthy of mentioning, and GBR yields good prediction only of the wall diameter.

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Recent Developments and Future Challenges in Incremental Sheet Forming of Aluminium and Aluminium Alloy Sheets

Cited by 25 publications

References 199 publications

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

Investigation of Surface Roughness in Incremental Sheet Forming of Conical Drawpieces from Pure Titanium Sheets

Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

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