Analysis of the incremental sheet forming process and process parameters on aluminum sheets based on the vertical step sizes

Malyer, Elif; Sönmez, Fikret

doi:10.1177/09544062221075987

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Results showed that a decrease in vertical step size and an increase in feed rate resulted in a considerable reduction in surface roughness. Malyer and Sonmez 19 studied the effect of selected parameters on the response such as forming time, geometric tolerance, and sheet thickness while incrementally forming AA5754 sheets. According to the Pareto analysis, spindle speed and feed rate are the two parameters that significantly affect toolpath strategies.…”

Section: Introductionmentioning

confidence: 99%

Optimization and prediction of incremental sheet forming parameters of Titanium grade 5 sheet using a response surface methodology and artificial neural network

Ajay¹,

Elangovan

AS³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Single point incremental forming (SPIF) is an advanced, flexible, and cost-effective approach for producing complicated sheet metal objects quickly. Because of its low equipment cost, the process is best suited toward low or medium quantity batch production as the conventional stamping method remains cost-effective only for mass manufacture. During SPIF formation of titanium grade 5 materials, a response surface methodology and artificial neural network (ANN) model was created to optimize and estimate wall angle (Ømax) and average surface roughness ( Ra). The ANN model is developed using feed forward back propagation networks. Various combinations of transfer functions and number of neurons were used to create the ANNs (3- n-1, 3- n-2). The confirmation runs have been used to ensure that the ANN anticipated and practical findings have been in agreement. The generated ANN model (3- n-1) was capable of accurately forecasting the results of the experiment, with an overall R-value and Mean Square Error (MSE) of 0.99987 and 0.010905 for Ra, and 0.99999 and 0.00700 for wall angle. The best 3- n-2 models has an average R-value of 0.99992 and MSE of 0.05532, respectively. As a consequence of rapid ANN modeling technique, it became discovered that technical effort inside the SPIF process could be decreased.

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Section: Introductionmentioning

confidence: 99%