A Novel Machine-Learning-Based Procedure to Determine the Surface Finish Quality of Titanium Alloy Parts Obtained by Heat Assisted Single Point Incremental Forming

Bautista-Monsalve, Fernando; Garcı́a-Sevilla, Francisco; Miguel, V.; Naranjo, Jesús Andrés; Manjabacas, M.C.

doi:10.3390/met11081287

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…Furthermore, an optimised tool path will be created to improve the geometric accuracy at critical area during the SPIF process. Similarly, Bautista-Monsalve et al [80] investigated an image database to make training of different classification algorithms as learning approach to study the wear and cracks on the forming surface to predict the surface quality for heat-assisted SPIF for Ti-6Al-4 V sheets. The image network is efficient for all ISF systems in capturing the forming effects, including the geometric coordinates, thickness distribution and surface quality to optimise the tool path plan, which compensate the error from tool motion, wear and cracks according to the process.…”

Section: Compensation and Rsm Optimisationmentioning

confidence: 99%

Heat-assisted incremental sheet forming for high-strength materials — a review

Attallah²,

Essa³

2022

Int J Adv Manuf Technol

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Single-point incremental forming (SPIF) is a sheet forming technique that deforms sheet materials incrementally to a designated shape. The process has shown high ability to deform low-strength materials for good geometrical accuracy and formability at room temperature. Deforming high-temperature alloys, such as high-strength steels and Ti-6AI-4 V, requires integrated heat sources to increase the ductility of the metal sheets for deformation. However, the integration of heating results in unpredictable thermal behaviours and impacts the formability, geometric accuracy, thickness distribution and surface quality. Considerable research efforts have invented different heating methods and designed novel tools and analytical modelling to resolve the limitations. The current challenge remains improving the localised and stable heating, functional tool design to reduce the thermal expansion and friction at the tool-surface contact area and the analysis of relationship between thermal and mechanical effects. This study aims to review the heating-assisted SPIF systems for high-strength alloy sheets to solve the current limitations. The method includes analysis of heating systems, tool, tool path design, lubricants and macro- and micro-numerical analyses. Additionally, the study aims to correlate the microstructural properties to the mechanical behaviours and subsequent effects on forming force, strain, springback, geometrical accuracy and surface quality.

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Section: Compensation and Rsm Optimisationmentioning

confidence: 99%

Heat-assisted incremental sheet forming for high-strength materials — a review

Attallah²,

Essa³

2022

Int J Adv Manuf Technol

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“…Recently, researchers are focusing on the application of machine learning (ML) techniques for predicting several process specific dimensions [3]. These include forming accuracy [4], surface quality [5], tool load [6], forming temperature [7], the pillow effect [8] and the material flow curve [9]. Due to the lack of industrial ISF production lines, the data used for training the ML models has to be gathered by the research institutes themselves.…”

Section: Introductionmentioning

confidence: 99%

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Möllensiep,

Detering,

Kulessa

et al. 2024

Preprint

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While incremental sheet metal forming offers the potential for producing sheet metal parts in small lot sizes, the relatively low forming accuracy prevents a widespread industrial use. For improving the forming accuracy, research institutes are using machine learning techniques to predict the geometric accuracy and modify the toolpath based on the prediction. A critical challenge is it to ensure the generalisability of the prediction model as only a small amount of process data is available to train the model due to the lack of industrial collaborations. This publication presents a highly transferable feature engineering approach where surface representations of the part's geometry around each toolpath point are transferred into a standardized coordinate system. Several artificial neural networks were trained and used for predicting the forming accuracy and modifying the toolpath. During the validation experiments, the forming errors of parts which were independent of the training process were reduced by up to 68.5 %. The framework for computing the surface representations alongside with several pre-trained artificial neural networks is publicity available for download.

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“…Uz M M et al [18] predicted the mechanical flow behavior of titanium alloy in a certain temperature range by establishing an artificial neural network model. Bautista-Monsalve F et al [19] established a single-point incremental prediction model of titanium alloy based on a machine learning algorithm, which can predict the surface quality of titanium alloy formed by single-point incremental forming. Datta S et al [20] established a neural network prediction model for laser welding of titanium alloy, which can be used to predict the processing parameters of laser forming of titanium alloy.…”

Section: Introductionmentioning

confidence: 99%

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Wang,

et al. 2024

JMSE

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The application of titanium alloy in shipbuilding can reduce ship weight and carbon emissions. To solve the problem of titanium alloy forming, the deformation prediction of titanium alloy line heating based on a backpropagation (BP) neural network and sparrow search algorithm (SSA) was researched. Based on the thermal–elastic–plastic finite element method, the numerical calculation model of TA5 titanium alloy overlapping heating forming was established. The feasibility of the model was verified by comparing it with the numerical calculation and experiment of low-carbon steel. Considering the characteristics of the titanium alloy-forming process, 73 groups of titanium alloy-forming schemes were obtained by the Latin hypercube sampling method. The deformation data of the samples were obtained by using the numerical calculation model of titanium alloy forming. The prediction methods of titanium alloy-forming deformation based on BP, genetic algorithm–backpropagation (GA-BP), and SSA-BP were proposed. The accuracy of different neural network prediction models was analyzed. The mean absolute percentage errors (MAPEs) of BP, GA-BP, and SSA-BP in shrinkage prediction were 7.45%, 4.08%, and 2.96%, respectively. The MAPEs of BP, GA-BP, and SSA-BP in deflection prediction were 8.44%, 4.73%, and 2.64%, respectively. The goodness of fit (R2) of SSA-BP is closest to 1 among the three models. The calculation results show that SSA-BP is better than BP and GA-BP in predicting the forming deformation of titanium alloy. The maximum prediction error of SSA-BP is 4.95%, which is within the allowable range of engineering error. The SSA-BP prediction model is suitable for the rapid and accurate prediction of the deformation of titanium alloy line heating forming. The intelligent prediction model provides data support for intelligent decisions for titanium alloy forming.

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A Novel Machine-Learning-Based Procedure to Determine the Surface Finish Quality of Titanium Alloy Parts Obtained by Heat Assisted Single Point Incremental Forming

Cited by 12 publications

References 28 publications

Heat-assisted incremental sheet forming for high-strength materials — a review

Heat-assisted incremental sheet forming for high-strength materials — a review

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

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