Machinability investigation in electric discharge machining of carbon fiber reinforced composites for aerospace applications

Hassan, Abual; He, Yan; Rehman, Mudassar; Ishfaq, Kashif; Zahoor, Sadaf; Hussain, Mirza Zahid; Siddique, Farah; Wang, De Chao

doi:10.1002/pc.26878

Cited by 11 publications

(8 citation statements)

References 56 publications

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“…Carbon fiber reinforced polymer (CFRP) is a composite material with resin as matrix and carbon fiber bundle as reinforcement, which is widely used in aerospace, automotive and wind power blades. [1][2][3][4] Due to its excellent properties such as high strength, high-specific elastic modulus, light weight, fatigue resistance and good corrosion resistance, more and more attention has been focused on application of CFRP in recent years. It improves the energy efficiency while ensuring the strength of the structure.…”

Section: Introductionmentioning

confidence: 99%

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

et al. 2023

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Combining acoustic emission (AE) and machine learning algorithms to understand damage and failure of carbon fiber reinforced polymer (CFRP) under bending loads is a challenging task in their practical applications. This work aims to identify different characteristics of the signal induced by damage patterns of carbon/epoxy composites using machine learning model. By carrying out wavelet packet analysis of the AE signals allowing the identification of four macroscopic damage modes. Four types of signals corresponding to matrix cracking, delamination, fiber/matrix debonding and fiber breakage are characterized by the frequency bands of the main energy distribution in the original waveform. The frequency bands are (62.5–125 kHz) or (125–187.5 kHz), (187.5–250 kHz), (250–312.5 kHz) and (312.5–375 kHz) and above, respectively. To test the actual performance of the established model, called support vector machine (SVM) classifier, several precracked and untreated specimens have been fabricated and subjected to three‐point bending test. The classification result of constructed classifier was compared with the k‐means algorithm, which is widely accepted for classifying AE signals, and the similarity of the two results is analyzed. The results indicated that the similarity of different clusters exceeded 92%, 84%, 83% and 77%, respectively. It can be seen that the SVM classifier was considered promising to provide new ideas for the health monitoring of composite structures.

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Section: Introductionmentioning

confidence: 99%

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

et al. 2023

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“…Also, utilization of brass tool produces higher rate of material removal compared to copper and tungsten carbide tools [21]. During electrical dischage machining of carbon nanotube‐carbon fibre composite, the electrode wear rate (EWR) and material removal rate (MRR) are greatly influenced by gap voltage, with contributions of 43.59 % and 20.11 % respectively [22]. Next to pulse current and pulse‐on time, the carbon nanotube contents in (0.15 wt.…”

Section: Introductionmentioning

confidence: 99%

Machinability and surface morphology investigations of multiwall carbon nanotube reinforced aluminium 7075 metal matrix composite during electrical discharge machining: A grey relational approach

Pradhan,

Tripathy,

Prasad

et al. 2023

Materialwissenschaft Werkst

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Multiwall carbon nanotube buttressed aluminium 7075 metal matrix composite was synthesized through an amended liquid metallurgy method, which consisted semisolid stirring, ultrasonic treatment and squeeze casting. Aim was to investigate its machinability and surface morphology during electrical discharge machining. Variable machining factors were peak current, pulse‐on time and gap voltage, whereas the responses under investigation were electrode wear rate, material removal rate and average surface roughness. Results revealed electrode wear rate, material wear rate and average surface roughness increased on increasing peak current and pulse‐on time, but all these responses behaved inversely with the increase of gap voltage. Average surface roughness reduced by around 44 % on reducing the peak current from 10 A to 4 A and increasing gap voltage from 55 V to 80 V at constant pulse‐on time of 300 μs; however, it increased by around 25 % on reducing the gap voltage from 80 V to 55 V and increasing the pulse‐on time from 100 μs to 300 μs at constant peak current of 10 A. Significance of the process parameters were verified, regression models were developed and morphology of the machined surfaces was studied. Finally, multiple response optimization was conducted following grey relational approach.

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“…FRP are used in different fields of application such as transport, construction, sanitary, consumer goods, wind turbine, military, biomedical, biogas digesters, and others. [1][2][3][4][5][6][7] Unsaturated polyester resin and glass fiber are major components of FRP composite products. Several methods have been developed to combine unsaturated polyester resin and glass fiber to produce a glass fiber-polyester composite products.…”

Section: Introductionmentioning

confidence: 99%

Spray and deposition characteristics of unsaturated polyester resin applied for impregnating long fiber preform composites

Zegeye,

Delele,

Tsegaw

et al. 2023

Polymer Composites

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Spray up is a method intensively applied for short fiber reinforced plastic (FRP) products manufacturing. The current study investigated the application of spray up for impregnation and manufacturing of long fiber FRP products, which can bring major advantages of minimized impregnation time and fewer product defects. Computational fluid dynamics (CFD) modeling was applied to investigate the spray and deposition characteristics of the viscous general‐purpose unsaturated polyester resin on a glass fiber preform. Validation of CFD results was done using phase Doppler anemometer and camera imaging techniques, and errors were calculated. Droplet velocity distributions 30 cm from the nozzle tip showed an average absolute percentage error of 4.3% and root mean square error value of 0.68 m/s, which are minimal. CFD and experimental values of sprayed resin area coverage (main area) after 4 s of spray were 17 and 16.3 cm in diameter, respectively; this is a relatively small error. Resin pool thickness on the target surface was variable, values predicted numerically. Non‐uniformity of pool thickness can be compensated by using appropriate compaction mechanisms. It is also possible to impregnate large size and complex shape products using multiple nozzles of proper arrangement.

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Machinability investigation in electric discharge machining of carbon fiber reinforced composites for aerospace applications

Cited by 11 publications

References 56 publications

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

Machinability and surface morphology investigations of multiwall carbon nanotube reinforced aluminium 7075 metal matrix composite during electrical discharge machining: A grey relational approach

Spray and deposition characteristics of unsaturated polyester resin applied for impregnating long fiber preform composites

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