Improving the Accuracy of Parts Manufactured by Single Point Incremental Forming

Radu, Crina; Cristea, Ion; Herghelegiu, Eugen; Tabacu, Ştefan

doi:10.4028/www.scientific.net/amm.332.443

Cited by 10 publications

(7 citation statements)

References 14 publications

(14 reference statements)

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“…Oraon et al [31] trained feed-forward backpropagation (FFBP) in an ANN model with a structure 6-6-1 to predict the surface roughness of a brass Cu67Zn33 piece formed by way of SPIF. Radu et al [32] evaluated the effectiveness of the Response Surface Method (RSM) and the Neural Network (NN) method for improving and controlling the accuracy of SPIF components. Basing their claims on the accuracy of their experiments, they suggest further research of a broader range of process parameters; they claim that such investigation will help to generate valid general empirical models.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Najm

Paniti

2021

Int J Adv Manuf Technol

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Incremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Najm

Paniti

2021

Int J Adv Manuf Technol

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“…The SPIF variant has been widely studied, but still the desired rigorous dimensional accuracy in an industrial application that meets the strict control of dimensional quality [18] has not been obtained, which makes this variant to have a huge disadvantage with fewer opportunities for development and study in respect to the DPIF variant.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis between the SPIF and DPIF Variants for Die-less Forming Process for an Automotive Workpiece

Lozano¹,

Bermúdez²,

Correa³

2015

Inge cuc

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Abstract-Over time the process of incremental deformation Die-less has been developed in many ways to meet the needs of flexible production with no investment in tooling and low production costs. Two of their configurations are the SPIF (Single point incremental forming) and DPIF (Double point Incremental forming) technique. The aim of this study is to compare both techniques with the purpose of exposing their advantages and disadvantages in the production of industrial parts, as well as to inform about Die-less as an alternative manufacturing process. Experiments with the exhaust pipe cover of a vehicle are performed, the main process parameters are described, and formed workpieces without evidence of defects are achieved. Significant differences between the two techniques in terms of production times and accuracy to the original model are also detected. Finally, it is suggested when is more convenient to use each of these.

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“…Waved impression roughness is the major surface quality problem of SPIF since the roughness value of the waved impression is larger than with the friction trace [123,124]. Recently, many studies have focused on applying different strategies to improve the geometric accuracy of finished parts made by SPIF, i.e., neural network strategies [125,126], iterative algorithms methodology [127,128] and the transfer functions approach [129].…”

Section: Forming Strategymentioning

confidence: 99%

Single-Point Incremental Forming of Titanium and Titanium Alloy Sheets

et al. 2021

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Incremental sheet forming of titanium and its alloys has a significant role in modern manufacturing techniques because it allows for the production of high-quality products with complex shapes at low production costs. Stamping processes are a major contributor to plastic working techniques in industries such as automotive, aerospace and medicine. This article reviews the development of the single-point incremental forming (SPIF) technique in titanium and its alloys. Problems of a tribological and microstructural nature that make it difficult to obtain components with the desired geometric and shape accuracy are discussed. Great emphasis is placed on current trends in SPIF of difficult-to-form α-, α + β- and β-type titanium alloys. Potential uses of SPIF for forming products in various industries are also indicated, with a particular focus on medical applications. The conclusions of the review provide a structured guideline for scientists and practitioners working on incremental forming of titanium and titanium alloy sheets. One of the ways to increase the formability and minimize the springback of titanium alloys is to treat them at elevated temperatures. The main approaches developed for introducing temperature into a workpiece are friction heating, electrical heating and laser heating. The selection of an appropriate lubricant is a key aspect of the forming process of titanium and its alloys, which exhibit unfavorable tribological properties such as high adhesion and a tendency to adhesive wear. A review of the literature showed that there are insufficient investigations into the synergistic effect of rotational speed and tool rotation direction on the surface roughness of workpieces.

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Improving the Accuracy of Parts Manufactured by Single Point Incremental Forming

Cited by 10 publications

References 14 publications

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Comparative Analysis between the SPIF and DPIF Variants for Die-less Forming Process for an Automotive Workpiece

Single-Point Incremental Forming of Titanium and Titanium Alloy Sheets

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