Performance evaluation of high-speed incremental sheet forming technology for AA5754 H22 aluminum and DC04 steel sheets

Mulay, Amrut; Ben, B. Satish; Ismail, Syed; Kocańda, A.; Jasiński, C.

doi:10.1016/j.acme.2018.03.004

Cited by 25 publications

(14 citation statements)

References 30 publications

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“…The full forming processes were carried out using the same parameters: a 1500 mm/min feed rate and a 2000 rpm spindle speed were applied. A constant step-down of a value of 0.05 mm was used, as the application of a smaller stepdown would have resulted in better geometric accuracy and surface finish of the SPIF components [47,48]. To increase the reliability of the measurements, each sample was formed three times experimentally; the total number of formed components were thus 108.…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…4,5 Steel and aluminium alloys show good formability at room temperature. [6][7][8] Therefore, numerous studies available in literature discussed the effect of process parameters, like step depth, wall angle, spindle speed, feed rate, and tool diameter on formability of these alloys. 9,10 However, limited literature is reported so far on the formability of titanium alloy at room temperature thorough ISF, as it is difficult to form due to its hardness, higher cost, and poor formability.…”

Section: Introductionmentioning

confidence: 99%

Process capabilities of commercially pure titanium grade 2 formed through warm incremental sheet forming

Kumar

Tandon

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Commercially pure titanium (CP-Ti) Grade-2 has many applications due to its good weldability, strength, ductility, formability, and superior corrosion resistance. Although, CP-Ti Grade-2 can be formed at room temperature, however, it has lower ductility at room temperature. Therefore, heat treatment or thermal activation is required to increase its ductility and formability. In this paper, the process capabilities of CP-Ti Grade-2 to form the components through warm incremental sheet forming (ISF) has been investigated. To identify the optimal temperature at which CP-Ti Grade-2 sheets can be incrementally formed, straight groove tests were performed experimentally at various temperatures. Two geometries, namely, varying wall angle truncated cone, and constant wall angle truncated cone were used as test cases to evaluate the formability of CP-Ti Grade-2, in terms of limiting wall angle. The formability was also assessed through forming limit diagram obtained by Finite Element (FE) simulation. With forming limit damage criterion, fracture in the formed component was predicted with FE simulation using Abaqus Explicit software. To assess the process capabilities of CP-Ti Grade-2 sheet formed through warm ISF, thickness distribution, forming forces, geometrical accuracy, and surface roughness were analyzed through both FE simulation and experimental work.

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“…The SPIF process aims to form products with the most accurate shape possible [ 5 , 6 , 7 , 8 , 9 ]. A deep learning technique to propagate geometric accuracy in SPIF was proposed in [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

Ostaševičius

Paleviciute

Paulauskaitė-Tarasevičienė

et al. 2021

Sensors

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This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.

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Performance evaluation of high-speed incremental sheet forming technology for AA5754 H22 aluminum and DC04 steel sheets

Cited by 25 publications

References 30 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

Process capabilities of commercially pure titanium grade 2 formed through warm incremental sheet forming

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

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