Experimental investigation of geometric accuracy in single point incremental forming process of an aluminium alloy

2019 ◽  
Vol 10 (1) ◽  
pp. 46
Author(s):  
R.M. Belokar ◽  
Narinder Kumar
Keyword(s):  
Experimental Investigation ◽  
Aluminium Alloy ◽  
Incremental Forming ◽  
Single Point ◽  
Single Point Incremental Forming ◽  
Geometric Accuracy ◽  
Forming Process

Experimental Investigation of Single Point Incremental Forming of Aluminum Sheet in Groove Test

2017 ◽  
Vol 867 ◽  
pp. 177-183 ◽  
Author(s):  
Vikrant Sharma ◽  
Ashish Gohil ◽  
Bharat Modi
Keyword(s):  
Experimental Investigation ◽  
Incremental Forming ◽  
Single Point ◽  
Fractional Factorial ◽  
Single Point Incremental Forming ◽  
Forming Process ◽  
Backing Plate ◽  
Groove Test ◽  
Factorial Design Of Experiments ◽  
Tool Diameter

Incremental sheet forming is one of the latest processes in sheet metal forming industry which has drawn attention of various researchers. It has shown improved formability compared to stamping process. Single Point Incremental Forming (SPIF) process requires only hemispherical tool and no die is required hence, it is a die-less forming process. In this paper experimental investigation on SPIF for Aluminium sheet has been presented. A groove test on Vertical Machining Centre has been performed. Factors (Step depth, Blank holder clamping area, Backing plate radius, Program strategy, Feed rate and Tool diameter) affecting the process are identified and experiments are carried out using fractional factorial design of experiments. Effect of the factors on fractured depth, forming time and surface finish have been analyzed using Minitab 17 software.

Comparison of the Geometric Accuracy by DSIF Toolpath with SPIF Toolpath

2014 ◽  
Vol 494-495 ◽  
pp. 497-501 ◽  
Author(s):  
Jin Han Wu ◽  
Qiu Cheng Wang
Keyword(s):  
Incremental Forming ◽  
Single Point ◽  
Metal Sheet ◽  
Single Point Incremental Forming ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Forming Accuracy ◽  
Finite Method

As there is no sufficient support between the single moving tool and fixture, the formed metal sheet is easy to bend in single point incremental forming (SPIF). Double sided incremental forming (DSIF) is proposed in which two tools are used on each side of the sheet to improve the components forming accuracy. Element finite method is introduced to simulate the forming process with both DSIF and SPIF toolpaths and the component geometric accuracies are compared. The simulation result shows the DSIF toolpaths can obtain better geometric accuracy than SPIF.

scholarly journals Roundness and positioning deviation prediction in single point incremental forming using deep learning approaches

2019 ◽  
Vol 11 (7) ◽  
pp. 168781401986446
Author(s):  
Sofien Akrichi ◽  
Amira Abbassi ◽  
Sabeur Abid ◽  
Noureddine Ben yahia
Keyword(s):  
Deep Learning ◽  
Incremental Forming ◽  
Single Point ◽  
Deep Belief Network ◽  
Superior Performance ◽  
Single Point Incremental Forming ◽  
Learning Approaches ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Belief Network

This article proposes a deep learning technique for the prevision of the geometric accuracy in single point incremental forming. Moreover, predicting geometric accuracy is one of the most crucial measures of part quality. Accordingly, roundness and positioning deviation are two indicators for measuring geometric accuracy and presenting two output variables. Two types of artificial intelligence learning approaches, that is, shallow learning and deep learning, are investigated and compared for forecasting geometrical accuracy in the single point incremental forming process. Therefore, the back-propagation neural network with one hidden layer is selected as the representative for shallow learning and deep belief network and stack autoencoder are chosen as the representatives for deep learning. Accurate prediction is closely related to the feature learning of single point incremental forming process parameters. The following six parameters were considered as input variables: sheet thickness, tool path direction, step depth, speed rate, feed rate, and wall angle. The results of these studies indicate that deep learning could be a powerful tool in the current search for geometric accuracy prediction in single point incremental forming. Otherwise, the deep learning approach shows the best performance prediction with shallow learning. In addition, the deep belief network model achieves superior performance accuracy for the prediction of roundness and position deviation in comparison with the stack autoencoder approach.

scholarly journals Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7263
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti ◽  
Tomasz Trzepieciński ◽  
Sami Ali Nama ◽  
Zsolt János Viharos ◽  
...  
Keyword(s):  
Aluminium Alloy ◽  
Incremental Forming ◽  
Sheet Material ◽  
Single Point ◽  
Alloy Sheet ◽  
Single Point Incremental Forming ◽  
Regression Equations ◽  
Effective Parameters ◽  
Forming Process ◽  
Tool Diameter

When using a unique tool with different controlled path strategies in the absence of a punch and die, the local plastic deformation of a sheet is called Single Point Incremental Forming (SPIF). The lack of available knowledge regarding SPIF parameters and their effects on components has made the industry reluctant to embrace this technology. To make SPIF a significant industrial application and to convince the industry to use this technology, it is important to study mechanical properties and effective parameters prior to and after the forming process. Moreover, in order to produce a SPIF component with sufficient quality without defects, optimal process parameters should be selected. In this context, this paper offers insight into the effects of the forming tool diameter, coolant type, tool speed, and feed rates on the hardness of AA1100 aluminium alloy sheet material. Based on the research parameters, different regression equations were generated to calculate hardness. As opposed to the experimental approach, regression equations enable researchers to estimate hardness values relatively quickly and in a practicable way. The Relative Importance (RI) of SPIF parameters for expected hardness, determined with the partitioning weight method of an Artificial Neural Network (ANN), is also presented in the study. The analysis of the test results showed that hardness noticeably increased when tool speed increased. An increase in feed rate also led to an increase in hardness. In addition, the effects of various greases and coolant oil were studied using the same feed rates; when coolant oil was used, hardness increased, and when grease was applied, hardness decreased.

Geometry Compensation Methods for Increasing the Accuracy of the SPIF Process

2021 ◽  
Vol 883 ◽  
pp. 217-224
Author(s):  
Yannick Carette ◽  
Marthe Vanhulst ◽  
Joost R. Duflou
Keyword(s):  
Control Strategy ◽  
Incremental Forming ◽  
Single Point ◽  
Single Point Incremental Forming ◽  
Calculation Methods ◽  
Forming Process ◽  
Complex Deformation ◽  
Commercial Use ◽  
Compensation Methods ◽  
Deformation Mechanics

Despite years of supporting research, commercial use of the Single Point Incremental Forming process remains very limited. The promised flexibility and lack of specific tooling is contradicted by its highly complex deformation mechanics, resulting in a process that is easy to implement but where workpiece accuracy is very difficult to control. This paper looks at geometry compensation as a viable control strategy to increase the accuracy of produced workpieces. The input geometry of the process can be compensated using knowledge about the deformations occurring during production. The deviations between the nominal CAD geometry and the actual produced geometry can be calculated in a variety of different ways, thus directly influencing the compensation. Two different alignment methods and three deviation calculation methods are explained in detail. Six combined deviation calculation methods are used to generate compensated inputs, which are experimentally produced and compared to the uncompensated part. All different methods are able to noticeably improve the accuracy, with the production alignment and closest point deviation calculation achieving the best results

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