Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite
This work focuses on optimization of abrasive machining parameters of the natural fiber reinforced sandwich composite, which is rarely reported in the literature. A sandwich made of vegetable fiber composite skins and polyvinyl chloride (PVC) foam of 80 gsm was machined for optimal conditions. The d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-10-01
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| Series: | Journal of Natural Fibers |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15440478.2021.1958427 |
| _version_ | 1797680021125464064 |
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| author | Shinde Avinash Siva Irulappasamy Chithirai Pon Selvan MTH Sultan Lee Seng Hua Yashwant Munde |
| author_facet | Shinde Avinash Siva Irulappasamy Chithirai Pon Selvan MTH Sultan Lee Seng Hua Yashwant Munde |
| author_sort | Shinde Avinash |
| collection | DOAJ |
| description | This work focuses on optimization of abrasive machining parameters of the natural fiber reinforced sandwich composite, which is rarely reported in the literature. A sandwich made of vegetable fiber composite skins and polyvinyl chloride (PVC) foam of 80 gsm was machined for optimal conditions. The design of experiment and analysis were adopted to confirm the influence of machining parameters. The machining characters of bio-sandwich were compared with synthetic and hybrid sandwich panels to optimize the machinability of the target. The panels were manufactured through vacuum infusion bagging. The machining studies were done using the abrasive water jet cutting machine. The machining characteristics were optimized for the parameters and L18 Taguchi technique was employed in parameter optimization. Three controlled levels of machining parameters were chosen to be optimized: standoff distance (SOD), abrasive water jet pressure (JP), and nozzle traverse rate (TR). The response of kerf taper (KT), surface roughness (SR), and material removal rate (MRR) were investigated. It is observed that highest levels of these parameters gave minimum kerf taper and lowest levels produce lower surface roughness. The surface roughness and damage on the surface was observed using scanning electron microscopy (SEM). It Shows that flowing abrasive particle’s directional distortion noted at the foam regions due to their higher damping nature. The prediction model shows a good agreement with the experimental value. |
| first_indexed | 2024-03-11T23:23:43Z |
| format | Article |
| id | doaj.art-4a38be1fd3a0450c9f9fa2f13c04722c |
| institution | Directory Open Access Journal |
| issn | 1544-0478 1544-046X |
| language | English |
| last_indexed | 2024-03-11T23:23:43Z |
| publishDate | 2022-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj.art-4a38be1fd3a0450c9f9fa2f13c04722c2023-09-20T13:04:29ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2022-10-0119147956797210.1080/15440478.2021.19584271958427Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich CompositeShinde Avinash0Siva Irulappasamy1Chithirai Pon Selvan2MTH Sultan3Lee Seng Hua4Yashwant Munde5Kalasalingam Academy of Research and EducationKalasalingam Academy of Research and EducationCurtin University DubaiUniversiti Putra MalaysiaUniversiti Putra MalaysiaMKSSS’s Cummins College of Engineering for WomenThis work focuses on optimization of abrasive machining parameters of the natural fiber reinforced sandwich composite, which is rarely reported in the literature. A sandwich made of vegetable fiber composite skins and polyvinyl chloride (PVC) foam of 80 gsm was machined for optimal conditions. The design of experiment and analysis were adopted to confirm the influence of machining parameters. The machining characters of bio-sandwich were compared with synthetic and hybrid sandwich panels to optimize the machinability of the target. The panels were manufactured through vacuum infusion bagging. The machining studies were done using the abrasive water jet cutting machine. The machining characteristics were optimized for the parameters and L18 Taguchi technique was employed in parameter optimization. Three controlled levels of machining parameters were chosen to be optimized: standoff distance (SOD), abrasive water jet pressure (JP), and nozzle traverse rate (TR). The response of kerf taper (KT), surface roughness (SR), and material removal rate (MRR) were investigated. It is observed that highest levels of these parameters gave minimum kerf taper and lowest levels produce lower surface roughness. The surface roughness and damage on the surface was observed using scanning electron microscopy (SEM). It Shows that flowing abrasive particle’s directional distortion noted at the foam regions due to their higher damping nature. The prediction model shows a good agreement with the experimental value.http://dx.doi.org/10.1080/15440478.2021.1958427sisal fibercompositepvc foam coreawjmoptimizationprediction model |
| spellingShingle | Shinde Avinash Siva Irulappasamy Chithirai Pon Selvan MTH Sultan Lee Seng Hua Yashwant Munde Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite Journal of Natural Fibers sisal fiber composite pvc foam core awjm optimization prediction model |
| title | Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite |
| title_full | Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite |
| title_fullStr | Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite |
| title_full_unstemmed | Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite |
| title_short | Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite |
| title_sort | abrasive machining characteristics and prediction model for sisal polyester sandwich composite |
| topic | sisal fiber composite pvc foam core awjm optimization prediction model |
| url | http://dx.doi.org/10.1080/15440478.2021.1958427 |
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