Prediction of average surface roughness and formability in single point incremental forming using artificial neural network

Mulay, Amrut; Ben, B. Satish; Ismail, Syed; Kocańda, A.

doi:10.1016/j.acme.2019.06.004

Cited by 28 publications

(20 citation statements)

References 37 publications

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“…They reported a result of 94.744% for ANN simulation performance with a mean absolute error of 1.068%. Also, an ANN model was utilized by Mulay et al [30] to predict the average surface roughness and the wall angle of AA5052-H3 parts manufactured using SPIF. Oraon et al [31] trained feed-forward backpropagation (FFBP) in an ANN model with a structure 6-6-1 to predict the surface roughness of a brass Cu67Zn33 piece formed by way of SPIF.…”

Section: Introductionmentioning

confidence: 99%

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

Najm

Paniti

2021

Int J Adv Manuf Technol

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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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Section: Introductionmentioning

confidence: 99%

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

Najm

Paniti

2021

Int J Adv Manuf Technol

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“…[6][7][8] Therefore, numerous studies available in literature discussed the effect of process parameters, like step depth, wall angle, spindle speed, feed rate, and tool diameter on formability of these alloys. 9,10 However, limited literature is reported so far on the formability of titanium alloy at room temperature thorough ISF, as it is difficult to form due to its hardness, higher cost, and poor formability. The formability of commercially pure titanium (CP-Ti) in ISF was first investigated by Hussain et al 11 They discussed the effect of the pitch, feed rate, tool diameter, and friction at tool-sheet interface on formability.…”

Section: Introductionmentioning

confidence: 99%

Process capabilities of commercially pure titanium grade 2 formed through warm incremental sheet forming

Kumar

Tandon

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Commercially pure titanium (CP-Ti) Grade-2 has many applications due to its good weldability, strength, ductility, formability, and superior corrosion resistance. Although, CP-Ti Grade-2 can be formed at room temperature, however, it has lower ductility at room temperature. Therefore, heat treatment or thermal activation is required to increase its ductility and formability. In this paper, the process capabilities of CP-Ti Grade-2 to form the components through warm incremental sheet forming (ISF) has been investigated. To identify the optimal temperature at which CP-Ti Grade-2 sheets can be incrementally formed, straight groove tests were performed experimentally at various temperatures. Two geometries, namely, varying wall angle truncated cone, and constant wall angle truncated cone were used as test cases to evaluate the formability of CP-Ti Grade-2, in terms of limiting wall angle. The formability was also assessed through forming limit diagram obtained by Finite Element (FE) simulation. With forming limit damage criterion, fracture in the formed component was predicted with FE simulation using Abaqus Explicit software. To assess the process capabilities of CP-Ti Grade-2 sheet formed through warm ISF, thickness distribution, forming forces, geometrical accuracy, and surface roughness were analyzed through both FE simulation and experimental work.

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“…Up to now, numerical simulations and optimisation techniques have greatly contributed to the development of SPIF [296][297][298][299][300], especially for metallic components with In another study, Al-Obaidi et al [295] continue with the same hot SPIF technology to form a basalt fibre reinforced thermoplastic polymer (BFRTP).…”

Section: Application Of Spif To Hybrid Metal-polymer Compositesmentioning

confidence: 99%

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

et al. 2021

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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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Prediction of average surface roughness and formability in single point incremental forming using artificial neural network

Cited by 28 publications

References 37 publications

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

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

Process capabilities of commercially pure titanium grade 2 formed through warm incremental sheet forming

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

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