PhD

Master

Master's Degree in Die and Tools Techniques

Bachelor

Bachelor in Die and Tools Techniques

Mechanical Power Techniques Engineering Department [Head of Department]

Technical Engineering College - Kirkuk

Power Mechanics Techniques [Head of Department]

Kirkuk Technical Institute

Application of the Gradient-Boosting with Regression Trees to Predict the Coefficient of Friction on Drawbead in Sheet Metal Forming

Journal Materials

publisher MDPI

DOI https://doi.org/10.3390/ma17184540

Issue 18

Volume 17

Correct design of the sheet metal forming process requires knowledge of the friction phenomenon occurring in various areas of the drawpiece. Additionally, the friction at the drawbead is decisive to ensure that the sheet flows in the desired direction. This article presents the results of experimental tests enabling the determination of the coefficient of friction at the drawbead and using a specially designed tribometer. The test material was a DC04 carbon steel sheet. The tests were carried out for different orientations of the samples in relation to the sheet rolling direction, different drawbead heights, different lubrication conditions and different average roughnesses of the countersamples. According to the aim of this work, the Features Importance analysis, conducted using the Gradient-Boosted Regression Trees algorithm, was used to find the influence of several parameter features on the coefficient of friction. The advantage of gradient-boosted decision trees is their ability to analyze complex relationships in the data and protect against overfitting. Another advantage is that there is no need for prior data processing. According to the best of the authors’ knowledge, the effectiveness of gradient-boosted decision trees in analyzing the friction occurring in the drawbead in sheet metal forming has not been previously studied. To improve the accuracy of the model, five MinLeafs were applied to the regression tree, together with 500 ensembles utilized for learning the previously learned nodes, noting that the MinLeaf indicates the minimum number of leaf node observations. The least-squares-boosting technique, often known as LSBoost, is used to train a group of regression trees. Features Importance analysis has shown that the friction conditions (dry friction of lubricated conditions) had the most significant influence on the coefficient of friction, at 56.98%, followed by the drawbead height, at 23.41%, and the sample width, at 11.95%. The average surface roughness of rollers and sample orientation have the smallest impact on the value of the coefficient of friction at 6.09% and 1.57%, respectively. The dispersion and deviation observed for the testing dataset from the experimental data indicate the model’s ability to predict the values of the coefficient of friction at a coefficient of determination of R2 = 0.972 and a mean-squared error of MSE = 0.000048. It was qualitatively found that in order to ensure the optimal (the lowest) coefficient of friction, it is necessary to control the friction conditions (use of lubricant) and the drawbead height.

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Thermodynamic Evaluation of Low-GWP and Environmentally Friendly Alternative Refrigerants

Journal Mechanical Engineering Research

publisher Canadian Center of Science and Education

DOI https://doi.org/10.3390/ma17184540

Issue 1

Volume 12

Refrigerant systems, crucial for modern life, are increasingly important due to their environmental impact and rising energy costs, with their advancement influenced by social life evolution and widespread use in homes and buildings. A systematic search using thermodynamic models identified 48 possible ternary mixtures and 5 pure refrigerants. These combinations, based on thermodynamics, could provide energy savings, paving the way for real-world testing and definitive conclusions, not yet studied in literature. REFPROP refers to the reference fluid properties program, developed by NIST version 9.0 for 2010, is a program for calculating the thermodynamic and transport properties of industrially important fluids and their mixtures. This program was used to evaluate the refrigerant properties in different mixing ratios. Then, using the MATLAB version of 2020 apparatus to arrange and solve all the variables to generate the results under set boundary conditions, all the characteristics were incorporated into thermodynamic equations. when compared to R134a, the results demonstrated that mixtures of natural refrigerants usually have acceptable thermal performance; these mixtures may be recommended as suitable replacements for refrigeration and air conditioning systems because they are environmentally harmless and have a low GWP.

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Analysis of the friction performance of deep-drawing steel sheets using network models

Journal The International Journal of Advanced Manufacturing Technology

publisher Springer

DOI https://doi.org/10.1007/s00170-024-13565-0

Issue 1

Volume 123

This article presents the results of pilot studies on the lubrication of the blankholder zone in sheet metal forming using a pressurised lubricant. The authors invented a method and built a special tribometer for pressure-assisted lubrication. This approach reduces friction in sheet metal forming processes compared to conventional lubrication. Moreover, the artificial neural network approach combined with a force-directed Fruchterman-Reingold graph algorithm and Spearman’s correlation was used for the first time to analyse the relationships between the friction process parameters and the output parameters (the coefficient of friction and the resulting surface roughness of the sheet metal). The experimental tests were conducted utilising strip drawing on four grades of steel sheets known to be outstanding for deep-drawing quality. Different oils, oil pressures and contact pressures were used. Artificial neural network models were used for the first time to determine these relationships in a strip drawing test where every parameter is represented by one node, and all nodes are connected by edges with each other. R Software version 4.2.3 was used to construct the network using the ‘qgraph’ and ‘networktools’ packages. It was found that friction conditions had a highly significant negative correlation with coefficient of friction (COF) and a moderately significant negative correlation with the final surface roughness. However, the initial surface roughness of the as-received sheets had a negative correlation with the COF and a positive one with the resulting surface roughness of the sheet metal. The parameters most related to the COF are the strength coefficient, the ultimate tensile strength and the friction conditions (dry friction or pressurised lubrication). Spearman’s correlation coefficients showed a strong correlation between the kinematic viscosity and the friction conditions.

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Application of Categorical Boosting to Modelling the Friction Behaviour of DC05 Steel Sheets in Strip Drawing Test

Journal Advances in Mechanical and Materials Engineering

publisher Scientific Letters of Rzeszów University of Technology. Mechanics

DOI https://doi.org/10.7862/rm.2024.7

Issue 1

Volume 41

It is challenging to model the coefficient of friction, surface roughness, and related tribological processes during metal contact because of flattening, ploughing, and adhesion. It is important to choose the appropriate process parameters carefully when creating analytical models to overcome the challenges posed by complexity. This will ensure the production of sheet metal formed components that meets the required quality standards and is free from faults. This research analyses the impacts of nominal pressure, kinematic viscosity of lubricant, and lubricant pressure on the coefficient of friction and average roughness of DC05 deep-drawing steel sheets. The strip drawing test was used to determine the coefficient of friction. This work utilises the Categoric Boosting (CatBoost) machine learning algorithm created by Yandex to estimate the COF and surface roughness, intending to conduct a comprehensive investigation of process parameters. A Shapley decision plot exhibits the coefficient of friction prediction models via cumulative SHapley Additive exPlanations (SHAP) data. CatBoost has outstanding prediction accuracy, as seen by R2 values ranging from 0.955 to 0.894 for both the training and testing datasets for the COF, as well as 0.992 to 0.885 for surface roughness.

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Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry

Journal Materials

publisher MDPI

DOI https://doi.org/10.3390/ma17030590

Issue 3

Volume 17

The development of lightweight and durable materials for car body panels and load-bearing elements in the automotive industry results from the constant desire to reduce fuel consumption without reducing vehicle performance. The investigations mainly concern the use of these alloys in the automotive industry, which is characterised by mass production series. Increasing the share of lightweight metals in the entire structure is part of the effort to reduce fuel consumption and carbon dioxide emissions into the atmosphere. Taking into account environmental sustainability aspects, metal sheets are easier to recycle than composite materials. At the same time, the last decade has seen an increase in work related to the plastic forming of sheets made of non-ferrous metal alloys. This article provides an up-to-date systematic overview of the basic applications of metallic materials in the automotive industry. The article focuses on the four largest groups of metallic materials: steels, aluminium alloys, titanium alloys, and magnesium alloys. The work draws attention to the limitations in the development of individual material groups and potential development trends of materials used for car body panels and other structural components.

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Experimental and Numerical Investigations of the Fatigue Life of AA2024 Aluminium Alloy‑Based Nanocomposite Reinforced by TiO2 Nanoparticles Under the Effect of Heat Treatment

Journal International Journal of Precision Engineering and Manufacturing

publisher Springer

DOI https://doi.org/10.1007/s12541-023-00906-4

Issue 1

Volume 25

Using aluminium metal matrix nanocomposites has recently gained increased attention in the industry due to their high strength and ductility. In this paper, TiO2 nanoparticles in volume percentages of 5 wt. % were added to the AA2024 alloy using the stir casting method. Using a novel powder injection system, TiO2 nanoparticles with an average particle size of 30 ± 5 nm was added to the matrix. The influence of TiO2 content on the fatigue life before and after heat treatment was studied. The results showed the fatigue properties of AA2024 with TiO2 nanoparticles increased after heat treatment. The optimum improvement in fatigue properties was obtained at 5 wt. % TiO2 after heat treatment, with an improving fatigue life in 14.71% compared with sample based. This is due to an increased number of fine precipitates besides its uniformly distributed after heat treatment. The fatigue life of the composite materials with added nanoparticles was investigated using a finite element-based ANSYS workbench. There was a good match between what happened in the experiments and what happened to the numerical fatigue strength. For the composite materials, the difference between the experimental and numerical values of fatigue strength was not greater than 4% for the matrix. The results also, indicated that, after ageing, the precipitate-free zone at the inter-dendritic zone disappeared or became smaller. However, after adding 5 wt. % of titanium and, also, performing heat treatment, it is not possible to precipitate the Al2CuMg precipitates, and, instead of it, the Al3TiCu and Al7TiCu phases precipitates have been formed.

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Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

Journal Materials

publisher MDPI

DOI https://doi.org/10.3390/ma16155207

Issue 15

Volume 16

first_pageDownload PDFsettingsOrder Article Reprints Open AccessArticle Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron by Tomasz Trzepieciński 1,*ORCID,Sherwan Mohammed Najm 2,3ORCID,Omar Maghawry Ibrahim 4 andMarek Kowalik 5ORCID 1 Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, Poland 2 Kirkuk Technical Institute, Northern Technical University, 36001 Kirkuk, Iraq 3 Department of Manufacturing Science and Engineering, Budapest University of Technology and Economics, Műegyetemrkp 3, H-1111 Budapest, Hungary 4 Plant Production Department, Arid Land Cultivation Research Institute, City of Scientific Research and Technological Applications SRTA-City, Borg Al-Arab 21934, Egypt 5 Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, 54 Stasieckiego Street, 26-600 Radom, Poland * Author to whom correspondence should be addressed. Materials 2023, 16(15), 5207; https://doi.org/10.3390/ma16155207 Submission received: 5 June 2023 / Revised: 18 July 2023 / Accepted: 20 July 2023 / Published: 25 July 2023 (This article belongs to the Special Issue Forming Technologies and Mechanical Properties of Advanced Materials - 2nd Volume) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF’s most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress Rp0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R2 (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R2 = 0.90 ± 0.028). Ra values greater than 1.1 and Rp0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9–35 for viscosity and 105–190 for Rp0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9–35, and the Ra was 0.95–1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the Rp0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

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Minimizing the Main Strains and Thickness Reduction in the Single Point Incremental Forming Process of Polyamide and High-Density Polyethylene Sheets

Journal Materials

publisher MDPI

DOI https://doi.org/10.3390/ma16155207

Issue 4

Volume 16

Polymeric materials are increasingly used in the automotive industry, aeronautics, medical device industry, etc. due to their advantage of providing good mechanical strength at low weight. The incremental forming process for polymeric materials is gaining increasing importance because of the advantages it offers: relatively complex parts can be produced at minimum cost without the need for complex and expensive dies. Knowing the main strains and especially the thickness reduction is particularly important as it directly contributes to the mechanical strength of the processed parts, including in operation. For the design of experiments, the Taguchi method was chosen, with an L18 orthogonal array obtained by varying the material on two levels (polyamide and polyethylene) and the other three parameters on three levels: punch diameter (6 mm, 8 mm and 10 mm), wall angle (50°, 55° and 60°) and step down (0.5 mm, 0.75 mm and 1 mm). The output parameters were strain in the x direction, strain in the y direction, major strain, minor strain, shear angle and thickness reduction. Two analyses were conducted: signal-to-noise ratio analysis with the smaller-is-better condition and analysis of variance. The optimum values for which the thickness was reduced were the following: wall angle of 50°, punch diameter of 10 mm and step down of 0.75 mm.

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Application of Artificial Neural Networks to the Analysis of Friction Behaviour in a Drawbead Profile in Sheet Metal Forming

Journal Materials

publisher MDPI

DOI https://doi.org/10.3390/ma15249022

Issue 24

Volume 15

Drawbeads are used when forming drawpieces with complex shapes to equalise the flow resistance of a material around the perimeter of the drawpiece or to change the state of stress in certain regions of the drawpiece. This article presents a special drawbead simulator for determining the value of the coefficient of friction on the drawbead. The aim of this paper is the application of artificial neural networks (ANNs) to understand the effect of the most important parameters of the friction process (sample orientation in relation to the rolling direction of the steel sheets, surface roughness of the counter-samples and lubrication conditions) on the coefficient of friction. The intention was to build a database for training ANNs. The friction coefficient was determined for low-carbon steel sheets with various drawability indices: drawing quality DQ, deep-drawing quality DDQ and extra deep-drawing quality EDDQ. Equivalents of the sheets tested in EN standards are DC01 (DQ), DC03 (DDQ) and DC04 (EDDQ). The tests were carried out under the conditions of dry friction and the sheet surface was lubricated with machine oil LAN46 and hydraulic oil LHL32, commonly used in sheet metal forming. Moreover, various specimen orientations (0° and 90°) in relation to the rolling direction of the steel sheets were investigated. Moreover, a wide range of surface roughness values of the counter-samples (Ra = 0.32 μm, 0.63 μm, 1.25 μm and 2.5 μm) were also considered. In general, the value of the coefficient of friction increased with increasing surface roughness of the counter-samples. In the case of LAN46 machine oil, the effectiveness of lubrication decreased with increasing mean roughness of the counter-samples Ra = 0.32–1.25 μm. With increasing drawing quality of the sheet metal, the effectiveness of lubrication increased, but only in the range of surface roughness of the counter-samples in which Ra = 0.32–1.25 μm. This study investigated different transfer functions and training algorithms to develop the best artificial neural network structure. Backpropagation in an MLP structure was used to build the structure. In addition, the COF was calculated using a parameter-based analytical equation. Garson partitioning weight was used to calculate the relative importance (RI) effect on coefficient of friction. The Bayesian regularization backpropagation (BRB)—Trainbr training algorithm, together with the radial basis normalized—Radbasn transfer function, scored best in predicting the coefficient of friction with R2 values between 0.9318 and 0.9180 for the training and testing datasets, respectively.

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Modelling and parameter identification of coefficient of friction for deep‑drawing quality steel sheets using the CatBoost machine learning algorithm and neural networks

Journal The International Journal of Advanced Manufacturing Technology

publisher Springer

DOI https://doi.org/10.1007/s00170-022-10544-1

Issue 1

Volume 124

The development of models for the coefficient of friction is difficult due to many factors influencing its value and many tribological phenomena that accompany contact between metals (i.e., flattening, ploughing, adhesion), the influence of which also depends on the friction conditions. Therefore, developing an analytical model of friction is difficult. In this article, the CatBoost machine learning algorithm, newly developed by Yandex researchers and engineers, is used for modelling and parameter identification of friction coefficients for three grades of deep-drawing quality steel sheets. Experimental tests to determine the friction coefficient were carried out using the strip drawing method with the use of a specially designed tribological device. Lubrication conditions, normal force, and the surface roughness of countersample surfaces were used as input parameters. The friction tests were conducted in dry friction and lubricated conditions with three grades of oils with a wide range of viscosities. Different transfer functions and various training algorithms were tested to build the optimal structure of the artificial neural networks. An analytical equation based on the parameters that were being investigated was created to calculate the COF of each material. Different methods of partitioning weight were employed for the expected COF to assess the relative importance (RI) and individual feature’s relevance. A Shapley decision plot, which uses cumulative Shapley additive explanations (SHAP) values, was used to depict models for predicting COF. CatBoost was able to predict the coefficient of friction with R2 values between 0.9547 and 0.9693 as an average for the training and testing dataset, depending on the grade of steel sheet. When considering all the materials that were tested, it was discovered that the Levenberg–Marquardt training algorithm performed the best in predicting the coefficient of friction.

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Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

Journal Journal of Intelligent Manufacturing

publisher Springer

DOI https://doi.org/10.1007/s10845-022-02026-8

Issue 1

Volume 34

Today the topic of incremental sheet forming (ISF) is one of the most active areas of sheet metal forming research. ISF can be an essential alternative to conventional sheet forming for prototypes or non-mass products. Single point incremental forming (SPIF) is one of the most innovative and widely used fields in ISF with the potential to form sheet products. The formed components by SPIF lack geometric accuracy, which is one of the obstacles that prevents SPIF from being adopted as a sheet forming process in the industry. Pillow effect and wall displacement are influential contributors to manufacturing defects. Thus, optimal process parameters should be selected to produce a SPIF component with sufficient quality and without defects. In this context, this study presents an insight into the effects of the different materials and shapes of forming tools, tool head diameters, tool corner radiuses, and tool surface roughness (Ra and Rz). The studied factors include the pillow effect and wall diameter of SPIF components of AlMn1Mg1 aluminum alloy blank sheets. In order to produce a well-established study of process parameters, in the scope of this paper different modeling tools were used to predict the outcomes of the process. For that purpose, actual data collected from 108 experimentally formed parts under different process conditions of SPIF were used. Neuron by Neuron (NBN), Gradient Boosting Regression (GBR), CatBoost, and two different structures of Multilayer Perceptron were used and analyzed for studying the effect of parameters on the factors under scrutiny. Different validation metrics were adopted to determine the quality of each model and to predict the impact of the pillow effect and wall diameter. For the calculation of the pillow effect and wall diameter, two equations were developed based on the research parameters. As opposed to the experimental approach, analytical equations help researchers to estimate results values relatively speedily and in a feasible way. Different partitioning weight methods have been used to determine the relative importance (RI) and individual feature importance of SPIF parameters for the expected pillow effect and wall diameter. A close relationship has been identified to exist between the actual and predicted results. For the first time in the field of incremental forming study, through the construction of Catboost models, SHapley Additive exPlanations (SHAP) was used to ascertain the impact of individual parameters on pillow effect and wall diameter predictions. CatBoost was able to predict the wall diameter with R2 values between the range of 0.9714 and 0.8947 in the case of the training and testing dataset, and between the range of 0.6062 and 0.6406 when predicting pillow effect. It was discovered that, depending on different validation metrics, the Levenberg–Marquardt training algorithm performed the most effectively in predicting the wall diameter and pillow effect with R2 values in the range of 0.9645 and 0.9082 for wall diameter and in the range of 0.7506 and 0.7129 in the case of the pillow effect. NBN has no results worthy of mentioning, and GBR yields good prediction only of the wall diameter.

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Incremental Sheet Forming of Metal-Based Composites Used in Aviation and Automotive Applications

Journal Journal of Composites Science

publisher MDPI

DOI https://doi.org/10.3390/jcs6100295

Issue 10

Volume 6

For several years, the aviation industry has seen dynamic growth in the use of composite materials due to their low weight and high stiffness. Composites are being considered as a means of building lighter, safer, and more fuel-efficient automobiles. Composite materials are the building material of a relatively new kind of unmanned aerial vehicle, commonly known as a drone. Incremental forming methods allow materials to be quickly formed without the need to manufacture conventional metal dies. Their advantage is the high profitability during the production of prototypes and a small series of products when compared with the conventional methods of plastic forming. This article provides an overview of the incremental forming capabilities of the more commonly produced aluminium- and titanium-based laminates, which are widely used in the aircraft industry. In addition, for composites that are not currently incrementally formed, i.e., aramid-reinforced aluminium laminates, the advantages and potential for incremental forming are presented.

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Current Concepts for Cutting Metal-Based and Polymer-Based Composite Materials

Journal Journal of Composites Science

publisher MDPI

DOI https://doi.org/10.3390/jcs6050150

Issue 5

Volume 6

Due to the variety of properties of the composites produced, determining the choice of the appropriate cutting technique is demanding. Therefore, it is necessary to know the problems associated with cutting operations, i.e., mechanical cutting (blanking), plasma cutting plasma, water jet cutting, abrasive water jet cutting, laser cutting and electrical discharge machining (EDM). The criterion for choosing the right cutting technique for a specific application depends not only on the expected cutting speed and material thickness, but it is also related to the physico-mechanical properties of the material being processed. In other words, the large variety of composite properties necessitates an individual approach determining the possibility of cutting a composite material with a specific method. This paper presents the achievements gained over the last ten years in the field of non-conventional cutting of metal-based and polymer-based composite materials. The greatest attention is paid to the methods of electrical discharge machining and ultrasonic cutting. The methods of high-energy cutting and water jet cutting are also considered and discussed. Although it is well-known that plasma cutting is not widely used in cutting composites, the authors also took into account this type of cutting treatment. The volume of each chapter depends on the dissemination of a given metal-based and polymer-based composite material cutting technique. For each cutting technique, the paper presents the phenomena that have a direct impact on the quality of the resulting surface and on the formation of the most important defects encountered. Finally, the identified current knowledge gaps are discussed.

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Recent Developments and Future Challenges in Incremental Sheet Forming of Aluminium and Aluminium Alloy Sheets

Journal Metals

publisher MDPI

DOI https://doi.org/10.3390/met12010124

Issue 1

Volume 12

Due to a favourable strength-to-density ratio, aluminium and its alloys are increasingly used in the automotive, aviation and space industries for the fabrication of skins and other structural elements. This article explores the opportunities for and limitations of using Single- and Two Point Incremental Sheet Forming techniques to form sheets from aluminium and its alloys. Incremental Sheet Forming (ISF) methods are designed to increase the efficiency of processing in low- and medium-batch production because (i) it does not require the production of a matrix and (ii) the forming time is much higher than in conventional methods of sheet metal forming. The tool in the form of a rotating mandrel gradually sinks into the sheet, thus leading to an increase in the degree of deformation of the material. This article provides an overview of the published results of research on the influence of the parameters of the ISF process (feed rate, tool rotational speed, step size), tool path strategy, friction conditions and process temperature on the formability and surface quality of the workpieces. This study summarises the latest development trends in experimental research on, and computer simulation using, the finite element method of ISF processes conducted in cold forming conditions and at elevated temperature. Possible directions for further research are also identified.

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

Journal Materials

publisher MDPI

DOI https://doi.org/10.3390/ma14237263

Issue 23

Volume 14

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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New Advances and Future Possibilities in Forming Technology of Hybrid Metal–Polymer Composites Used in Aerospace Applications

Journal Journal of Composites Science

publisher MDPI

DOI https://doi.org/10.3390/jcs5080217

Issue 8

Volume 5

Fibre metal laminates, hybrid composite materials built up from interlaced layers of thin metals and fibre reinforced adhesives, are future-proof materials used in the production of passenger aircraft, yachts, sailplanes, racing cars, and sports equipment. The most commercially available fibre–metal laminates are carbon reinforced aluminium laminates, aramid reinforced aluminium laminates, and glass reinforced aluminium laminates. This review emphasises the developing technologies for forming hybrid metal–polymer composites (HMPC). New advances and future possibilities in the forming technology for this group of materials is discussed. A brief classification of the currently available types of FMLs and details of their methods of fabrication are also presented. Particular emphasis was placed on the methods of shaping FMLs using plastic working techniques, i.e., incremental sheet forming, shot peening forming, press brake bending, electro-magnetic forming, hydroforming, and stamping. Current progress and the future directions of research on HMPCs are summarised and presented.

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Emerging Trends in Single Point Incremental Sheet Forming of Lightweight Metals

Journal Metals

publisher MDPI

DOI https://doi.org/10.3390/met11081188

Issue 11

Volume 8

Lightweight materials, such as titanium alloys, magnesium alloys, and aluminium alloys, are characterised by unusual combinations of high strength, corrosion resistance, and low weight. However, some of the grades of these alloys exhibit poor formability at room temperature, which limits their application in sheet metal-forming processes. Lightweight materials are used extensively in the automobile and aerospace industries, leading to increasing demands for advanced forming technologies. This article presents a brief overview of state-of-the-art methods of incremental sheet forming (ISF) for lightweight materials with a special emphasis on the research published in 2015–2021. First, a review of the incremental forming method is provided. Next, the effect of the process conditions (i.e., forming tool, forming path, forming parameters) on the surface finish of drawpieces, geometric accuracy, and process formability of the sheet metals in conventional ISF and thermally-assisted ISF variants are considered. Special attention is given to a review of the effects of contact conditions between the tool and sheet metal on material deformation. The previous publications related to emerging incremental forming technologies, i.e., laser-assisted ISF, water jet ISF, electrically-assisted ISF and ultrasonic-assisted ISF, are also reviewed. The paper seeks to guide and inspire researchers by identifying the current development trends of the valuable contributions made in the field of SPIF of lightweight metallic materials.

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Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Journal The International Journal of Advanced Manufacturing Technology

publisher Springer

DOI https://doi.org/10.1007/s00170-021-06712-4

Issue 1

Volume 114

Incremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

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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

Journal International Journal of Precision Engineering and Manufacturing

publisher Springer

DOI https://doi.org/10.1007/s12541-020-00434-5

Issue 1

Volume 22

In the present work, multiple forming tests were conducted under different forming conditions by Single Point Incremental Forming (SPIF). In which surface roughness, arithmetical mean roughness (Ra) and the ten-point mean roughness (Rz) of AlMn1Mg1 sheet were experimentally measured. Also, an Artificial Neural Network (ANN) was used to predict the (Ra) and (Rz) by adopting the data collected from 108 components that were formed by SPIF. Forming tool characteristics played a key role in all the predictions and their effect on the final product surface roughness. In the aim to explore the proper materials and geometry of forming tools, different ANN structures, different training, and transfer functions have been applied to predict (Ra) and (Rz) as an output argument. Furthermore, Support Vector Regression (SVR) with different kernel types have been used for prediction, together with Gradient Boosting regression to sort the effective parameters on the surface roughness. The input arguments were tool materials, tool shape, tool end/corner radius, and tool surface roughness (Ra and Rz). The actual data subjected to a fit regression model to generate prediction equations of Ra and Rz. The results showed that ANN with one output gives the best R-Square (R2). Levenberg-Marquardt backpropagation (Trainlm) training function recorded the highest value of R2, 0.9628 for prediction Ra using Softmax transfer function whereas 0.9972 for Rz by Log- Sigmoid transfer function. Furthermore, tool materials, together with tool surface (Ra), are playing a significant importance role, affecting the sheet surface roughness (Ra). Whereas tool roughness Rz was the critical parameter effected on the Rz of the product. Also, there was a significant positive effect of tool geometry on the sheet surface roughness.

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Study on Effecting Parameters of Flat and Hemispherical end Tools in SPIF of Aluminium Foils

Journal Technical Gazette

publisher Portal of Croatian scientific and professional journals

DOI https://doi.org/10.17559/TV-20190513181910

Issue 6

Volume 27

Single Point Incremental Forming (SPIF) is a fast technique in the range of flexible prototype production without using a punch or die. Absence of the molding tools makes SPIF useful to form a complex product and these parts usually need different tool shapes. The aim of this study was to compare the performance of flat end and hemispherical end tools in micro-SPIF and evaluate the threshold value of the tool radius in relation to initial blank thickness. This paper investigated the best results of the final geometry, thickness homogeneity, minimum pillow surface, and maximum forming depth using different shapes and different sizes of the tool. The analysis of the results on AlMn1Mg1 foils with 0,22 mm initial thickness shows that the flat tool improves the geometry accuracy and decreases the pillow effect. Furthermore, in micro-ISF higher formability and more stable thickness distribution can be achieved with a flat end tool.

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Experimental and Numerical Investigation of Single Point Incremental Forming of Aluminium Alloy Foils

Journal ACTA IMEKO

publisher International Measurement Confederation

DOI https://doi.org/10.21014/acta_imeko.v9i1.750

Issue 1

Volume 9

Single Point Incremental Forming (SPIF) is a flexible process to manufacture sheet metal parts that is well adapted and profitable for prototypes or small batch production. Compared to traditional sheet forming technologies this relatively slow process can be used in different applications in automotive and aircraft industries, in architecture engineering and in medical aids manufacturing. In this paper indirectly obtained axial forming force on SPIF of variable wall angle geometry were studied under different process parameters. The estimation of the forces on AlMn1Mg1 sheets with 0.22 mm initial thickness is performed by continuous monitoring of servo motor currents. The deformation states of the formed parts were analysed using the ARGUS optical strain measurement system of GOM, while the roughness measurements were carried out by a System of Mitutoyo. Some initial Finite Element Analysis simulations and a crack monitoring method together with an interaction plot of forming speed, incremental depth, tool diameter and lubrication were also reported.

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The Effect of using Grease on the Surface Roughness of Aluminum 1100 Sheet during the Single Point Incremental Forming Process

Journal Trends in Machine Design

publisher STM Journals

DOI chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://engineeringjournals.stmjournals.in/index.php/TMD/article/viewFile/4528/3575

Issue 1

Volume 1

Incremental forming is a sheet metal forming technique in which a uniform sheet is plastically deformed through the progressive action of a rounded tool. The movement of the tool is governed by a CNC machine. By this way the tool locally deforms the sheet through pure stretching deformation. In this research incremental forming experiments were carried out on Aluminum 1100 sheets to form a cone shape. Roughness was studied by varying the input parameters (tool speed, feed rate and lubricant). From the study, it was found that the surface roughness was improved as tool speed and feed rate increases. Also it was found that using grease improve the surface roughness compared with using the coolant oil.

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Lubricants and Affecting Parameters on Hardness in SPIF of AA1100 Aluminium

Publisher 17th IMEKO TC10 Conference "Global trends in Testing, Diagnostics & Inspection for 2030” (2nd Conference jointly organized by IMEKO and EUROLAB aisbl)

DOI IMEKO-TC10-2020-057.pdf

Country CROATIA

Location 17th IMEKO TC10 Conference "Global trends in Testing, Diagnostics & Inspection for 2030” (2nd Conference jointly organized by IMEKO and EUROLAB aisbl)

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Heat Transfer and Fluid Flow Over a Bank of Circular Tubes Heat Exchanger Using Nanofluids: CFD Simulation

Publisher IOP Conference Series: Materials Science and Engineering

DOI https://doi:10.1088/1757-899X/928/2/022017

Country Iraq

Location 2nd International Scientific Conference of Al-Ayen University (ISCAU-2020) 15-16 July 2020, Thi-Qar, Iraq

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Experimental Investigation on the Single Point Incremental Forming of AlMn1Mg1 Foils using Flat End Tools

Publisher IOP Conference Series: Materials Science and Engineering

DOI https://doi:10.1088/1757-899X/448/1/012032

Country Hungary

Location XXIII International Conference on Manufacturing (Manufacturing 2018)7–8 June 2018, Kecskemét

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