Selection of natural fibre reinforced composites using fuzzy VIKOR for car front hood

Ishak, Noordiana Mohd; Malingam, Sivakumar Dhar; Mansor, Muhd Ridzuan

doi:10.1504/ijmpt.2016.10000368

Cited by 12 publications

(12 citation statements)

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“…To support the results, there are many reports on suitability and application of kenaf fiber as the reinforcement of polymer composites material in producing automotive components, such as spare tire cover, door trims, pillars, instrument panels, door panels, and others to replace plastic and synthetic fibers [24,36,37]. The results showed similar findings on the selection of kenaf fiber as a natural fiber polymer composites for producing automotive parts such as dashboard panels [13,38], bumper beam [39], hand brake levers [40], automotive spoiler [41], automotive brake pad [42], and for car front hood [18]. This shows the suitability of kenaf as the reinforcement material in natural fiber composites fabrication for automotive applications compared to other types of natural fiber materials.…”

Section: Resultsmentioning

confidence: 68%

“…The criteria shown in Fig. 3 was derived from literature [16][17][18]. Calculate priority ranking and sorting in decreasing order.…”

Section: Methodsmentioning

confidence: 99%

“…The same method was used by Ahmed Ali et al and Al-Oqla et al to select the best natural fiber/polypropylene composites for automotive components while Mansor et al selected the best hybrid natural/glass fiber material using the AHP method for hand brake lever [14][15][16]. Mastura et al used an integrated strategy of AHP method and Quality Function Deployment (QFD) to select suitable natural fiber materials for automotive anti-roll bar, and Ishak et al used VIKOR method for natural fiber materials selection for car front hood [17,18]. Besides natural fiber selection, there are studies in matrix materials selection where Ilangkumaran et al used integrated fuzzy AHP and PROMETHEE, and Mansor et al used TOPSIS to select the best matrix for bumper beam [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Decision Support Strategy in Selecting Natural Fiber Materials for Automotive Side-Door Impact Beam Composites

Shaharuzaman¹,

Sapuan²,

Mansor³

et al. 2019

Journal of Renewable Materials

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The enforcement on sustainable design and environmental-friendly products has attracted the interest of researchers and engineers in the context of replacing metals and synthetic fibers with natural based fibers, especially in the automotive industry. However, studies on sustainable natural fiber material selection in the automotive industry are limited. Evaluation for the side-door impact beam was conducted by gathering product design specification from literature which amounted to seven criteria and it was forwarded to ten decision makers with automotive engineering and product design background for evaluation. The weightage required for decision-making was obtained using the Analytic Hierarchy Process (AHP) method based on six criteria. Following this, the best natural fiber materials to be used as reinforcement in polymer composites were selected using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The results using both the AHP and VIKOR method showed that kenaf was the best natural fiber for the side-door impact beam composites. The result showed the lowest VIKOR value, QA1 = 0.0000, which was determined to be within the acceptable advantage and acceptable stability conditions. It can be concluded that the application of integrated AHP-VIKOR method resulted in a systematic and justified solution towards the decision-making process.

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Section: Resultsmentioning

confidence: 68%

“…The criteria shown in Fig. 3 was derived from literature [16][17][18]. Calculate priority ranking and sorting in decreasing order.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decision Support Strategy in Selecting Natural Fiber Materials for Automotive Side-Door Impact Beam Composites

Shaharuzaman¹,

Sapuan²,

Mansor³

et al. 2019

Journal of Renewable Materials

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“…FML was used in the upper fuselage and on the tail leading edges of the Airbus A380 due to its lightweight, high impact strength, good corrosion resistance, and relatively long fatigue life compared with conventional aluminum structure. [ 1–7 ] With the advances of the automation manufacturing of FML, [ 8,9 ] the interest in FML is growing again as aero companies are searching for robust fuselage material adaptable to their new narrow‐body commercial aircraft. [ 10 ] Also, there are increasing needs in the automobile industries to exert for lightweight, high stiffness material to enhance the weight efficiency of the vehicle structures.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Sai

Chai

Srikanth

2020

Advcd Theory and Sims

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With the advancement of machine learning in leading technologies, it is perceived that machine learning is a new and effective alternative for the classic fatigue life prediction. This paper provides a regression tree ensemble‐based machine learning approach to predict the fatigue life of GLARE composites. In the model, mechanical, geometrical properties and fatigue loading stresses are selected as the training parameters (so‐called features), and the GLARE fatigue life is predicted as the output of the model. Experimental data of a total of 98 pieces of GLARE specimens with nine different layups are used for the training and validation of the machine learning model. Results show that the model can provide good fatigue life prediction accuracy and model stability. The most correlated, either positively or negatively, parameters to the fatigue life span are the stress developed in the aluminum layer, the maximum cyclic stress, alternating stress, and mean fatigue stress.

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“…Kenaf bast and core fibers possess moderately higher cellulose content, which amounts 60.8 and 50.6% respectively . Ishak et al have shown that kenaf fibers have high potential to be used in FMLs to meet the requirement of the front hood design for cars by means of fuzzy VIKOR method, compared to other types of natural fibers. However, kenaf as one of the lignocellulosic fibers is not free from drawbacks such as hydrophilic nature, lower modulus and lower strength compared to synthetic fibers .…”

Section: Introductionmentioning

confidence: 99%

The effects of stacking configuration on the response of tensile and quasi‐static penetration to woven kenaf/glass hybrid composite metal laminate

et al. 2017

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The hybridization of natural and synthetic fibers in composite materials has gained the attention in the research field due to environmental awareness. Therefore, this study focuses on the effects of fiber stacking configuration on the tensile and quasi‐static penetration of kenaf/glass hybrid fiber metal laminates (FMLs). Kenaf/glass reinforced polypropylene FMLs with two different hybrid stacking configurations were prepared using hot compression process. Non‐hybrid kenaf and glass fiber reinforced metal laminates were also prepared for comparison. The tensile test was conducted according to ASTM E8, while the quasi‐static penetration test was carried out in accordance with ASTM D 6264 using 12.7 and 20 mm hemispherical indenters. The fractured surface of FMLs due to tensile loading was examined using scanning electron microscope, while optical micrograph was used to investigate the failure mechanism of quasi‐statically penetrated laminates. From the results, FMLs with the glass plies at the outer layers of composite (H1) showed a positive hybrid effect as they displayed better tensile and penetration resistance, compared to the non‐hybrid kenaf and glass reinforced FMLs. Furthermore, it could be seen that the overall performance of FMLs decreased as the kenaf content in the laminates further increases for hybrid FML. POLYM. COMPOS., 40:568–577, 2019. © 2017 Society of Plastics Engineers

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Selection of natural fibre reinforced composites using fuzzy VIKOR for car front hood

Cited by 12 publications

References 0 publications

Decision Support Strategy in Selecting Natural Fiber Materials for Automotive Side-Door Impact Beam Composites

Decision Support Strategy in Selecting Natural Fiber Materials for Automotive Side-Door Impact Beam Composites

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

The effects of stacking configuration on the response of tensile and quasi‐static penetration to woven kenaf/glass hybrid composite metal laminate

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