Experimental Investigation of Formability and Surface Finish into Resistance Single-Point Incremental Forming of Ti–6Al–4V Titanium Alloy Using Taguchi Design

2019 ◽  
Vol 72 (4) ◽  
pp. 1031-1041
Author(s):  
Mostafa Vahdani ◽  
Mohammad Javad Mirnia ◽  
Hamid Gorji ◽  
Mohammad Bakhshi-Jooybari
Keyword(s):  
Experimental Investigation ◽  
Titanium Alloy ◽  
Surface Finish ◽  
Incremental Forming ◽  
Single Point ◽  
Taguchi Design ◽  
Single Point Incremental Forming

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.

Feature Based Approach for Increasing the Accuracy of the SPIF Process

2007 ◽  
Vol 344 ◽  
pp. 527-534 ◽  
Author(s):  
Johan Verbert ◽  
Joost R. Duflou ◽  
Bert Lauwers
Keyword(s):  
Surface Finish ◽  
Feature Detection ◽  
Incremental Forming ◽  
Single Point ◽  
Single Point Incremental Forming ◽  
Freeform Surfaces ◽  
Single Pass ◽  
Feature Based ◽  
Manufacturing Time ◽  
Achievable Accuracy

One of the main issues of the single point incremental forming (SPIF) process is still the achievable accuracy. A number of methods have been suggested to increase this accuracy, but many of these contain a significant drawback. Reprocessing the workpiece can increase the accuracy but also significantly increases the manufacturing time and leads to a worse surface finish of the part. Other methods iteratively correct the toolpath based upon the deviations measured on the previously manufactured parts. This method is not very well suited for one of a kind products, since instead of one part, multiple parts need to be manufactured before the desired accuracy can be reached. Our method proposes to use feature detection to split the workpiece in a configuration of planes, edges, freeform surfaces and other features. For each of these features an optimised toolpath strategy can be determined and the toolpath in that zone can be adjusted for this strategy. The proposed method generates a single pass toolpath that leads to more accurate parts compared to the standard CAM toolpaths. This paper describes the feature based optimised toolpath generation method (FSPIF) and contains the results of experiments performed to validate this method.

scholarly journals A Novel Machine-Learning-Based Procedure to Determine the Surface Finish Quality of Titanium Alloy Parts Obtained by Heat Assisted Single Point Incremental Forming

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1287
Author(s):  
Fernando Bautista-Monsalve ◽  
Francisco García-Sevilla ◽  
Valentín Miguel ◽  
Jesús Naranjo ◽  
María Carmen Manjabacas
Keyword(s):  
Machine Learning ◽  
Surface Finish ◽  
Incremental Forming ◽  
Single Point ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Single Point Incremental Forming ◽  
Forming Process ◽  
Metal Forming Process ◽  
And Performance

Single point incremental forming (SPIF) is a cheap and flexible sheet metal forming process for rapid manufacturing of complex geometries. Additionally, it is important for engineers to measure the surface finish of work pieces to assess their quality and performance. In this paper, a predictive model based on machine learning and computer vision was developed to estimate arithmetic mean surface roughness (Ra) and maximum peak to valley height (Rz) of Ti6Al4V parts obtained by SPIF. An image database was prepared to train different classification algorithms in accordance with a supervised learning approach. A speeded up robust feature (SURF) detector was used to obtain visual vocabulary so that the classifiers are able to group the photographs into classes. The experimental results indicated that the proposed predictive method shows great potential to determine the surface quality, as classifiers based on a support vector machine with a polynomial kernel are suitable for this purpose.

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