Predict the Effects of Forming Tool Characteristics on Surface Roughness of Aluminum Foil Components Formed by SPIF Using ANN and SVR

Najm, Sherwan Mohammed; Paniti, Imre

doi:10.1007/s12541-020-00434-5

Cited by 24 publications

(20 citation statements)

References 29 publications

(32 reference statements)

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“…Appropriate selection of the parameters of the sheet metal forming process using single point incremental forming (SPIF) is intended to ensure appropriate surface quality in terms of roughness and, at the same time, an economically justified forming time [ 1 , 2 ]. The basic parameters in the process are: tool rotational speed, feed rate, step size, size and shape of the tool, type of sheet metal, and tool path [ 3 , 4 , 5 ]. The lubricant also plays a very important role.…”

Section: Introductionmentioning

confidence: 99%

“…Alsaman et al [ 32 ] applied ANNs and the regression model to analyse the SPIF process for conical draw pieces. Najm and Paniti [ 4 ] used ANNs to predict the Ra and Rz parameters by adopting the data collected from frustum cones that were formed by SPIF. The results showed that an ANN with one argument in the output predicted the outcome sufficiently well when compared with a two-argument structure.…”

Section: Introductionmentioning

confidence: 99%

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Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panels

et al. 2021

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The article presents the results of the analysis of the interactions between the single point incremental forming (SPIF) process parameters and the main roughness parameters of stiffened ribs fabricated in Alclad aluminium alloy panels. EN AW-7075-T6 and EN AW-2024-T3 Alclad aluminium alloy sheets were used as the research material. Panels with longitudinal ribs were produced with different values of incremental vertical step size and tool rotational speed. Alclad is formed of high-purity aluminium surface layers metallurgically bonded to aluminium alloy core material. The quality of the surface roughness and unbroken Alclad are key problems in SPIF of Alclad sheets destined for aerospace applications. The interactions between the SPIF process parameters and the main roughness parameters of the stiffened ribs were determined. The influence of forming parameters on average roughness Sa and the 10-point peak–valley surface roughness Sz was determined using artificial neural networks. The greater the value of the incremental vertical step size, the more prominent the ridges found in the inner surface of stiffened ribs, especially in the case of both Alclad aluminium alloy sheets. The predictive models of ANNs for the Sa and the Sz were characterised by performance measures with R2 values lying between 0.657 and 0.979. A different character of change in surface roughness was found for sheets covered with and not covered with a soft layer of technically pure aluminium. In the case of Alclad sheets, increasing the value of the incremental vertical step size increases the value of the surface roughness parameters Sa and Sz. In the case of the sheets not covered by Alclad, reduction of the tool rotational speed increases the Sz parameter and decreases the Sa parameter. An obvious increase in the Sz parameter was observed with an increase in the incremental vertical step size.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panels

et al. 2021

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“…They found that predictive models of ANNs for Ra and Rz were characterised by performance measures of R 2 values between 0.657 and 0.979. In other studies, different tool materials and shapes were investigated experimentally to study factors including formability, geometric accuracy [ 31 ], and surface roughness [ 32 ] on an AlMn1Mg1 sheet formed using SPIF under various forming conditions. The researchers evaluated the performance of an Artificial Neural Network (ANN) and Support Vector Regression (SVR).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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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%

“…In [ 6 ], a Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) focuses on geometric deviation prediction, and an Enhanced Squirrel Search Algorithm (ESSA) is used for the optimal selection of SPIF parameters in AA2024-O aluminum alloy sheets. Several shaping experiments have been carried out using SPIF under different forming conditions to measure the surface roughness, the arithmetic mean roughness (Ra), and ten-point mean roughness (Rz) of the AlMn1Mg1 sheet [ 7 ]. In addition, an ANN was used to predict (Ra) and (Rz), given the data collected from the SPIF components.…”

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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Predict the Effects of Forming Tool Characteristics on Surface Roughness of Aluminum Foil Components Formed by SPIF Using ANN and SVR

Cited by 24 publications

References 29 publications

Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panels

Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panels

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

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

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