Study Of The Influence Of 3D Printing Process Parameters On The Tensile Strength Of Kenaf Fiber Composite Materials

Fadilah, Reisya Ilham (2025) Study Of The Influence Of 3D Printing Process Parameters On The Tensile Strength Of Kenaf Fiber Composite Materials. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

The advancement of three-dimensional (3D) printing technology has significantly transformed modern manufacturing by enabling the fabrication of intricate geometries with high precision, speed, and customization. Concurrently, it has fostered the development of more sustainable materials to meet growing environmental concerns. One notable innovation is the integration of natural fibers such as kenaf into thermoplastic polymers like polypropylene (PP), which has proven to be a highly promising composite formulation. Kenaf offers desirable mechanical properties, biodegradability, and widespread availability, positioning it as a strong candidate for reinforcing eco-friendly composites. When blended with polypropylene, a polymer known for its excellent recyclability and enhanced mechanical performance, the resulting kenaf/PP composite demonstrates high suitability for Fused Deposition Modelling (FDM), the most commonly employed 3D printing method. This combination supports the goals of environmentally conscious manufacturing while maintaining the structural integrity essential for practical applications. To optimize the mechanical performance specifically, the tensile strength of printed kenaf/PP components, this study integrates experimental and computational techniques. The experimental design follows the Taguchi L18 orthogonal array to efficiently explore multiple parameter combinations. Furthermore, Artificial Neural Networks (ANN) and Genetic Algorithms (GA) are employed to model the relationships between input parameters namely, fiber composition, printing speed, infill density, and layer thickness and the output response, which is the maximum tensile load. This hybrid ANN-GA approach facilitates the identification of the optimal parameter set that yields the highest predicted tensile strength ~24.8773 MPa, thereby advancing the capability and sustainability of composite 3D printing technologies.
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Kemajuan teknologi pencetakan tiga dimensi (3D printing) telah secara signifikan mentransformasi dunia manufaktur modern dengan memungkinkan pembuatan geometri yang kompleks dengan presisi tinggi, kecepatan, dan kemampuan kustomisasi. Seiring dengan itu, perkembangan material yang lebih ramah lingkungan juga semakin didorong guna menjawab kekhawatiran terhadap dampak lingkungan. Salah satu inovasi yang menonjol adalah integrasi serat alam seperti kenaf ke dalam polimer termoplastik seperti polypropylene (PP), yang telah terbukti sebagai formulasi komposit yang sangat menjanjikan. Serat kenaf menawarkan sifat mekanik yang baik, sifat biodegradable, serta ketersediaan yang melimpah, menjadikannya kandidat kuat untuk memperkuat komposit ramah lingkungan. Ketika dicampurkan dengan polypropylene yang dikenal memiliki daya daur ulang yang baik dan performa mekanik yang ditingkatkan komposit kenaf/PP yang dihasilkan menunjukkan kesesuaian tinggi untuk proses Fused Deposition Modelling (FDM), metode pencetakan 3D yang paling umum digunakan. Kombinasi ini mendukung tujuan manufaktur yang berwawasan lingkungan tanpa mengorbankan integritas struktural yang penting bagi aplikasi praktis. Untuk mengoptimalkan performa mekanik, khususnya kekuatan tarik (tensile strength) dari komponen kenaf/PP hasil cetak 3D, studi ini menggabungkan pendekatan eksperimental dan komputasional. Rancangan eksperimen mengikuti metode Taguchi dengan susunan ortogonal L18 untuk secara efisien mengeksplorasi berbagai kombinasi parameter. Selanjutnya, model Jaringan Saraf Tiruan (Artificial Neural Network/ANN) dan Algoritma Genetika (Genetic Algorithm/GA) digunakan untuk memodelkan hubungan antara parameter input yaitu komposisi serat, kecepatan pencetakan, kerapatan infill, dan ketebalan layer dengan respon output berupa kekuatan tarik maksimum. Pendekatan hibrida ANN-GA ini memungkinkan identifikasi kombinasi parameter optimal yang menghasilkan prediksi kekuatan tarik tertinggi, yaitu sekitar 24,8773 MPa, sehingga mendorong kemajuan teknologi pencetakan 3D komposit yang berkelanjutan dan berperforma tinggi.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	3D printing, Artificial Neural Network, Genetic Algorithm
Subjects:	T Technology > TS Manufactures > TS156 Quality Control. QFD. Taguchi methods (Quality control) T Technology > TS Manufactures > TS161 Materials management. T Technology > TS Manufactures > TS170 New products. Product Development T Technology > TS Manufactures > TS183 Manufacturing processes. Lean manufacturing.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21201-(S1) Undergraduate Thesis
Depositing User:	Reisya Ilham Fadilah
Date Deposited:	31 Jul 2025 08:58
Last Modified:	19 Aug 2025 07:52
URI:	http://repository.its.ac.id/id/eprint/124585

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