Experimental investigation of geometric accuracy in single point incremental forming process of an aluminium alloy
25 Feb 2019-International Journal of Materials Engineering Innovation (Inderscience Publishers)-Vol. 10, Iss: 1, pp 46
TL;DR: In this article, the effect of four process parameters such as step depth, tool diameter, spindle speed and feed rate on the geometrical error in the form of average radial error of the formed part is discussed.
Abstract: Single point incremental forming (SPIF) is an emerging sheet metal forming process which is used to build a prototype of sheet metal parts with complex geometries. Geometrical inaccuracies of the formed parts are the significant obstruction in the commercialisation of this process. In this paper, Taguchi method and analysis of variance have been used to comprehensively study the effect of four process parameters such as step depth, tool diameter, spindle speed and feed rate on the geometrical error in the form of average radial error of the formed part. Parametric analysis and formulation of an empirical model for predicting the amount of geometric error in this process have been discussed. From the Taguchi method and analysis of variance, the optimal results are identified adequately and proficiently to produce a minimum geometric error.
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04 Oct 2021
TL;DR: In this paper, a modified adaptive neuro-fuzzy inferences system (MANFIS)-centred geometric deviation prediction and enhanced squirrel search algorithm (ESSA)-centered optimal parameter selection for SPIF in AA2024-O aluminium alloy sheets were proposed.
Abstract: In the manufacturing sector, the section that has attained the most demand is metal forming. Most traditional methods in the manufacturing industry are not flexible, thus opted for the single point incremental forming (SPIF) method. However, it encompasses a geometric deviation issue. The prediction of geometric deviation and optimal parameter selection is more tedious in the existing works. In order to trounce these issues, this paper proposes a modified adaptive neuro-fuzzy inferences system (MANFIS)-centred geometric deviation prediction and enhanced squirrel search algorithm (ESSA)-centred optimal parameter selection for SPIF in AA2024-O aluminium alloy sheets. The proposed methodology encompasses '3' sections. Initially, the SPIF designing is performed in which truncated cone shape is chosen and inputted into Computer-Aided Designs/Computer-Aided Manufacturing software. Next, the designed shape is constructed and coordinate-measuring machines gauges the geometric metrics. Therefore, to envisage the geometric deviations, the measured geometric values are rendered as the input to the MANFIS. If the deviation is presented, then the ESSA algorithm chooses the optimal initial parameters, and that selected parameters are suggested to the SPIF design. The minimum roundness deviation together with the position deviation is fixed as the Fitness Function in ESSA. The AA2024-O aluminium alloy sheet is taken for the experimental investigation. In addition, the proposed method's performance is contrasted with prevailing methods centred on statistical metrics and MSE, which exhibits that the proposed methods outweigh the prevailing methods. A better result is attained by the proposed works when analogized to the existent methods by obtaining values of 97.90%, 96.78%, 95.84%, 96.23%, and 0.017% for accuracy, precision, recall, F-measure along with mean error.
TL;DR: In this article , the effects of four process parameters, namely tool diameter, feed rate, step size, and sheet thickness, on the characteristics of the final product were examined using principal component analysis (PCA).
Abstract: The process of single-point incremental forming (SPIF) is a relatively new technology that is primarily used in the production of prototypes and small quantities of products. However, the process has several limitations with respect to the quality characteristics of its products. This study examined the effects of four process parameters—namely, tool diameter, feed rate, step size, and sheet thickness—on the characteristics of the final product. A total of 15 product responses were measured and/or calculated during the experiments. The responses fell under three different categories; surface profile accuracy, strain/stress/thinning, and forming forces. In previous published work, responses were studied separately for each category. The aim of this paper was to determine the relationships between responses using a principal component analysis (PCA). PCA is a well-known multivariate analysis technique used to reduce the dimensionality of data. As a result of the PCA, the product’s characteristic dimensions were reduced from 15 while 71% of the total variance of data was preserved. The results showed that only 8 responses were enough to characterize the final product, rather than 15. A relationship was detected between the side wall accuracy and forming forces and between strain, circularity, and surface roughness. These findings could not be detected with single-variable analyses.