Optimasi Proses Cetak 3D FDM untuk Komponen Bearing Berbasis PLA Menggunakan Metode TaguchiSania Nadillah Permata Dewi1, Wahyu Dwi Lestari2*Email corresponding author: wahyu.dwi.tm@upnjatim.ac.id1,2Teknik Mesin, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Jl.Raya Rungkut Madya, Gunung Anyar, SurabayaArticle history: Received: 21 Juli 2025 | Revised: 21 Oktober 2025 | Accepted: 27 Oktober 2025Abstract. The quality of 3D printing plays a crucial role in ensuring the reliability and performance of mechanical components such as bearings. This study aims to identify the effect of three main parameters of Fused Deposition Modeling (FDM) infill density, layer height, and nozzle temperature on two print quality indicators in PLA-based bearing components, namely print time and mold weight. Taguchi method with L9 (3³) orthogonal array was applied to design nine experimental combinations. Each combination was tested using a Flashforge Guider II Series printer with PLA filament, and analyzed using signal - to - noise (S/N) ratio (the “smaller is better” criterion) and Analysis of Variance (ANOVA). The results showed that layer height most dominantly affected molding time (81% contribution), followed by infill density (17%), while nozzle temperature was not significant. For mold weight, none of the parameters had a statistically significant effect (p-value > 0.05), although infill density had the largest contribution (28%). Based on these findings, the optimal combination to minimize molding time for bearing components is 60% infill density, 0.20 mm layer height, and 200°C nozzle temperature. Future research is recommended to explore additional factors (e.g. print speed, platform temperature) and conduct mechanical strength tests on bearing components to ensure the stability of mechanical properties.Keywords - 3D Printing; FDM; Taguchi Method; ANOVA; Bearing ComponentsAbstrak. Kualitas hasil cetak 3D memegang peranan krusial dalam menjamin keandalan dan performa komponen mekanik seperti bearing. Penelitian ini bertujuan mengidentifikasi pengaruh tiga parameter utama Fused Deposition Modeling (FDM) infill density, layer height, dan suhu nozzle terhadap dua indikator mutu cetak pada komponen bearing berbahan PLA, yaitu waktu cetak dan berat cetakan. Metode Taguchi dengan orthogonal array L9 (3³) diterapkan untuk merancang sembilan kombinasi eksperimen. Setiap kombinasi diuji menggunakan printer Flashforge Guider II Series dengan filamen PLA, lalu dianalisis menggunakan rasio signal – to – noise (S/N) (kriteria “smaller is better”) dan Analysis of Variance (ANOVA). Hasil penelitian menunjukkan bahwa layer height paling dominan memengaruhi waktu cetak (kontribusi 81 %), diikuti infill density (17 %), sedangkan suhu nozzle tidak signifikan. Untuk berat cetakan, tidak terdapat parameter yang berpengaruh signifikan secara statistik (p-value > 0,05), meski infill density memiliki kontribusi terbesar (28 %). Berdasarkan temuan ini, kombinasi optimal untuk meminimalkan waktu cetak pada komponen bearing adalah infill density 60 %, layer height 0,20 mm, dan suhu nozzle 200 °C. Penelitian selanjutnya direkomendasikan untuk mengeksplorasi faktor tambahan (misalnya print speed, suhu platform) serta melakukan uji kekuatan mekanik pada komponen bearing untuk memastikan kestabilan sifat mekanis.Kata Kunci - 3D Printing; FDM; Metode Taguchi; ANOVA; Komponen BearingPendahuluan Perkembangan pesat teknologi 3D Printing, terutama metode Fused Deposition Modeling (FDM), telah membuka peluang besar dalam bidang manufaktur komponen mekanik, mulai dari prototipe hingga produksi lintas skala. Teknologi ini memungkinkan pembuatan geometri kompleks secara langsung dari model digital tanpa memerlukan cetakan atau alat pemotong yang mahal. Salah satu material termoplastik yang paling banyak digunakan dalam FDM adalah Polylactic Acid (PLA). PLA diminati karena berbahan dasar sumber terbarukan (misalnya jagung atau tebu), mudah terurai secara hayati, serta memiliki sifat mekanik yang cukup baik untuk aplikasi skala ringan hingga menengah [1]. Selain itu, rentang suhu proses PLA (180–220 °C) relatif stabil, sehingga banyak printer FDM yang sudah dapat mengakomodasi material ini tanpa perlu melakukan modifikasi berarti. Di sisi lain, komponen bearing memegang peran penting dalam sistem mesin berputar. Fungsi utamanya adalah menopang poros agar dapat berputar atau bergerak geser dengan gesekan minimal, sehingga menjaga efisiensi dan umur panjang sistem secara keseluruhan [2]. Dalam aplikasi industri, bearing sering kali beroperasi pada kecepatan tinggi dan beban yang variatif [3]. Oleh karena itu, kualitas dimensi, tingkat presisi, serta kekuatan antar lapisan pada penerapan FDM menjadi sangat krusial. Meskipun ada beberapa studi yang menguji kekuatan mekanik PLA untuk elemen struktural [4], riset yang secara khusus menangani optimasi parameter FDM guna memproduksi bearing masih relatif terbatas. Beberapa penelitian telah menunjukkan bahwa ketebalan lapisan (layer height) dan kepadatan isi (infill density) berpengaruh signifikan terhadap waktu cetak dan kekasaran permukaan [5], namun belum banyak yang memfokuskan pada kombinasi parameter yang menghasilkan komponen bearing dengan karakteristik fungsional optimal.Dalam praktik sehari – hari, banyak pengguna FDM masih mengandalkan pendekatan trial – and – error untuk menentukan parameter pencetakan (seperti tingkat infill, ketebalan lapisan, dan suhu nozzle) [6]. Akibatnya, proses produksi komponen bearing berbahan PLA terkadang memakan waktu lama, menggunakan filamen secara tidak efisien, dan menghasilkan variasi kualitas permukaan serta dimensi yang sulit dipertahankan konsistensinya [7]. Sebagai contoh, percobaan dengan infill 100 % dan lapisan setebal 0,10 mm dapat menjamin kekuatan internal yang relatif tinggi, tetapi membutuhkan waktu cetak yang sangat lama dan material lebih banyak padahal beban kerja bearing dalam banyak aplikasi tidak selalu membutuhkan struktur sepadat itu. Sebaliknya, infill yang terlalu rendah bisa saja memangkas waktu dan bobot, namun meningkatkan risiko cacat struktural atau distorsi dimensi pada komponen bearing, terutama saat menahan beban geser dalam jangka panjang [8]Pendekatan Metode Taguchi menjadi pilihan yang tepat karena mampu mengurangi jumlah eksperimen tanpa mengorbankan kualitas analisis [9]. Dengan merancang orthogonal array L₉ (3³), penelitian ini akan menguji tiga faktor utama infill density, layer height, dan suhu nozzle pada tiga tingkat yang berbeda. Analisis rasio signal – to – noise (S/N) dan uji Analysis of Variance (ANOVA) akan digunakan untuk menentukan sejauh mana masing – masing faktor mempengaruhi variabel respons, yaitu waktu cetak dan berat cetakan komponen bearing PLA[10] . Pendekatan ini tidak hanya membantu menemukan kombinasi parameter optimal dengan cepat, tetapi juga mengidentifikasi kontribusi relatif setiap faktor, sehingga perancang proses dapat lebih fokus pada variabel yang paling krusial.Penelitian ini memiliki beberapa tujuan utama. Pertama, menggali pengaruh variasi ketebalan lapisan, tingkat kepadatan isi, dan suhu ekstrusi PLA terhadap efisiensi waktu cetak serta bobot komponen bearing. Kedua, menentukan faktor dominan melalui analisis statistik sehingga upaya optimasi dapat lebih terarah. Ketiga, menghasilkan rekomendasi praktis bagi pengguna FDM, khususnya dalam memproduksi bearing PLA yang membutuhkan presisi dimensi dan efisiensi waktu serta material dua hal yang kerap menjadi trade – off di lapangan. Meskipun beberapa penelitian sebelumnya telah menelaah karakteristik mekanis PLA dan parameter FDM secara umum, riset ini menjadi unik karena secara khusus menetapkan target fungsional untuk komponen bearing dan melakukan validasi ulang kombinasi parameter optimal yang ditemukan.Dengan demikian, skripsi atau artikel ini diharapkan tidak hanya sekadar menambah pengetahuan teoritis dalam bidang manufaktur aditif, tetapi juga memberikan kontribusi nyata bagi pelaku industri dan penelitian lanjutan. Hasil yang diperoleh dapat diaplikasikan pada pengaturan mesin FDM seperti Flashforge Guider II Series sehingga produksi bearing PLA menjadi lebih konsisten, hemat waktu, dan hemat biaya, tanpa mengorbankan stabilitas dimensi dan kekuatan dasar yang diperlukan untuk fungsi bearing. Di sisi lain, penelitian ini juga membuka peluang pengembangan lebih lanjut misalnya, pengujian kekuatan geser atau keausan pada bearing hasil cetak PLA sebagai bahan referensi dan landasan bagi studi lanjutan tentang optimasi parametrisasi FDM yang lebih komprehensif.MetodePenelitian ini dirancang untuk mengoptimasi parameter pencetakan 3D dengan metode FDM karena kemampuannya memproduksi komponen dengan cepat dan fleksibel [11], pada komponen bearing berbahan PLA dengan menggunakan Metode Taguchi. Metodologi mencakup lima tahapan utama: (1) perancangan model komponen bearing, (2) pemilihan material dan peralatan, (3) desain eksperimen Taguchi, (4) prosedur percobaan, dan (5) validasi parameter optimal. Setiap tahapan dijelaskan secara rinci sebagai berikut.Desain Model Komponen BearingBearing adalah elemen penting dalam sistem mekanik yang menopang poros agar dapat berputar atau bergerak bolak-balik dengan lancar dan tahan lama. Kinerja bearing sangat berpengaruh terhadap stabilitas dan efisiensi mesin secara keseluruhan [12]. Adapun untuk mengurangi gesekan dan meningkatkan efisiensi, berbagai metode telah diusulkan, termasuk penggunaan tekstur permukaan sebagai strategi peningkatan performa bantalan jurnal [13]. Dalam penelitian ini, dibuat desain model bearing menggunakan SolidWorks dengan tekstur heksagonal sedalam 1,5 mm, diameter dalam 20 mm, diameter luar 30 mm, dan lebar 10 mm. Pola tekstur heksagonal, diaplikasikan pada permukaan bearing dengan dimensi lebar dan tinggi masing-masing 0,5 mm. Desain lengkap bearing dan teksturnya ditampilkan pada Gambar 1.(a)(b)Gambar SEQ Gambar \* ARABIC 1. (a) Tekstur Permukaan Berbentuk Heksagonal, (b) BearingMaterial dan PeralatanMaterial dan peralatan yang digunakan dalam proses penelitian. Pemilihan material dan peralatan yang tepat menjadi salah satu faktor penting yang mempengaruhi keberhasilan penelitian, khususnya dalam proses pencetakan 3D. Material yang digunakan adalah filamen PLA (Polylactic Acid), polimer ini mudah diproses untuk membuat bagian yang dicetak, film, atau serat dengan peralatan plastik biasa [14], memiliki karakteristik mekanik dan termal, sedangkan peralatan utama yang digunakan adalah mesin cetak 3D Flashforge Guider II Series, yang memiliki spesifikasi teknis mendukung untuk proses pencetakan model yang presisi. Informasi lengkap mengenai spesifikasi material dan peralatan tersebut akan dijabarkan pada Tabel 1.Tabel SEQ Tabel \* ARABIC 1. Alat dan Bahan dalam PenelitianNamaGambarSpesifikasiMesin cetak 3D (FDM) Flashforge Guider IIMetode: Fused DepositionModeling (FDM)Bahan: PLA, ABS, PETGResolusi cetak: 100 – 300 mikronFilament PLA (Polylactic Acid)Diameter Filamen: 1,75 mmEkstrusion Temp: 180-120 oCToleransi Diameter: ± 0.02 mmDesain EksperimenDesain eksperimen ini mengadopsi metode Taguchi untuk mengevaluasi pengaruh tiga faktor (infill density, layer height, nozzle temperature) masing ‐ masing pada tiga level (Tabel 2), dengan memanfaatkan orthogonal array L9 (3³) seperti yang terlihat pada Tabel 3. Pendekatan ini mengurangi jumlah percobaan, sekaligus meminimalkan variabilitas akibat faktor ‐ faktor lingkungan yang tidak terkontrol. Kombinasi faktor tersebar merata dalam orthogonal array, sehingga setiap level diuji secara seimbang dan efisiensi waktu serta sumber daya dapat dioptimalkan.Tabel SEQ Tabel \* ARABIC 2. Parameter dan Level EksperimenFactorLevels123Infill Density60 %80 %100 %Layer Height0.1 mm0.15 mm0.2 mmNozzle Temperature190oC200oC210oCTabel SEQ Tabel \* ARABIC 3. Desain Orthogonal Array L9 33PercobaanInfill Density (%)Layer Height (mm)Nozzle Temperature (oC)1600.11902600.152003600.22104800.12005800.152106800.219071000.121081000.1519091000.2200Prosedur Optimasi ParameterPenelitian ini dirancang untuk mengoptimasi parameter pencetakan 3D FDM karena kemampuannya memproduksi komponen dengan cepat dan fleksibel [15], pada komponen bearing berbahan PLA dengan menggunakan Metode Taguchi. Metodologi mencakup lima tahapan utama: (1) perancangan model komponen bearing, (2) pemilihan material dan peralatan, (3) desain eksperimen Taguchi, (4) prosedur percobaan, dan (5) validasi parameter optimal. Setiap tahapan dijelaskan secara rinci sebagai berikut.. Prosedur penelitian ini dapat dilihat pada Gambar 2.Gambar SEQ Gambar \* ARABIC 2. Diagram Skematik Optimasi Parameter Manufaktur BearingValidasi Parameter OptimalPerhitungan Nilai S/N RasioPada tahap analisis data, semua hasil percobaan yaitu waktu pencetakan dan berat bearing diolah menggunakan perangkat lunak Minitab 2019 untuk menghitung rasio signal – to – noise (S/N) dengan karakteristik “smaller is better”. Nilai S/N ini mengukur kestabilan proses terhadap variasi, sehingga kombinasi parameter yang menghasilkan S/N terendah dianggap paling optimal. Rumus S/N Ratio “Smaller is Better”:S/N = -10 log⁡(1ni=1ny2 ) Dimana:S/N = Signal – to – noise ration = Jumlah percobaan atau sampely = Nilai keluaran dari percobaan ke -i Metode taguchi digunakan di sini untuk mengoptimalkan proses untuk mengevaluasi bagaimana parameter pencetakan proses FDM memengaruhi akurasi dimensi lebar komponen. Metode Taguchi sangat berguna dalam meningkatkan desain dan kualitas produk dengan menerapkan analisis eksperimen secara sistematis. Jenis masalah terbaik didefinisikan sebagai Nominal oleh metodologi Taguchi. Nilai target adalah atribut yang menunjukkan kinerja terbaik dalam hal akurasi dimensi [16]ANOVAPenggunaan analisis ANOVA untuk optimasi merupakan strategi yang efisien dalam meningkatkan kualitas produk dan efektivitas proses. ANOVA digunakan untuk membantu menentukan kombinasi faktor yang paling berpengaruh terhadap respon. Selain itu metode ini juga dapat mengidentifiksi faktor – faktor apa saja yang mempengaruhi respon. Data S/N yang diperoleh dari perhitungan nilai S/N Ratio dianalisis lebih lanjut dengan Analysis of Variance (ANOVA) pada taraf signifikansi α = 0,05 untuk menentukan kontribusi relatif masing-masing faktor (infill density, layer height, nozzle temperature) terhadap variasi waktu cetak. Hasil perhitungan S/N ratio disajikan dalam Tabel 5, 7, sedangkan temuan ANOVA meliputi nilai F, p-value, dan persentase kontribusi faktor dapat dilihat pada Tabel 6, 8 . Besarnya nilai delta (selisih antara S/N tertinggi dan terendah) pada setiap faktor juga digunakan untuk memeringkat pengaruh faktor, sehingga kombinasi parameter dengan ranking tertinggi dan kontribusi signifikan dipilih sebagai konfigurasi optimal [16] .Validasi akhirnya dilakukan dengan mencetak ulang menggunakan parameter tersebut untuk memverifikasi konsistensi dan keandalan prosedur optimasi. Uji KonfirmasiUji konfirmasi untuk memastikan konsistensi dan validitas dari prediksi rasio SN yang telah dihitung sebelumnya. Uji konfirmasi menjadi penting dalam konteks eksperimen atau pengujian faktor – faktor yang mempengaruhi kualitas atau kinerja suatu sistem, karena dapat memberikan keyakinan bahwa hasil prediksi benar – benar merepresentasikan kondisi yang diharapkan. Terdapat dua langkah utama dalam uji konfirmasi ini, yaitu perhitungan rata – rata prediksi dan perhitungan interval kepercayaan.1. Menghitung rata – rata prediksi rasio SN Persamaan 2 digunakan untuk menghitung nilai prediksi rata – rata rasio SN (μ_prediction) berdasarkan nilai rata-rata hasil eksperimen y_m dan deviasi dari nilai rata-rata. Ini membantu memperoleh estimasi nilai prediksi yang lebih akurat berdasarkan data percobaan yang ada.μprediction=ymi=1nyi-ym Dimana:μprediction adalah nilai rata – rata prediksi dari rasio SN (Signal to Noise).ym merupakan nilai rata – rata dari hasil eksperimen pada tahap konfirmasi.yi adalah nilai individu dari pengamatan hasil eksperimen.Persamaan ini digunakan untuk menghitung nilai prediksi berdasarkan rata – rata pengamatan yang telah diperoleh, sehingga memberikan estimasi terbaik dari nilai prediksi tersebut.2. Confidence Interval Calculation Persamaan 3 digunakan untuk menentukan rentang kepercayaan dari rata – rata prediksi yang telah dihitung sebelumnya. Confidence interval ini sangat penting untuk memastikan bahwa nilai rata-rata prediksi berada dalam batas yang dapat diterima secara statistik.CIp=Fa:df1df2MSEneff Dimana:CIp adalah confidence interval (batas kepercayaan) pada tingkat signifikansi tertentu.Fa:df1df2 adalah nilai F – tabel pada taraf signifikansi α dengan derajat kebebasan df1 dan df2.MSE adalah nilai mean square error (variansi error).neff adalah jumlah uji efektif.3. Jumlah Uji EfektifPersamaan 4 digunakan untuk menghitung jumlah uji efektif neff, yaitu ukuran sampel yang telah disesuaikan dengan jumlah derajat kebebasan. Nilai neffpenting untuk menghitung CIp pada persamaan sebelumnya, agar interval kepercayaan yang diperoleh menjadi representatif dan valid.neff=Total number of trials1+number off degress of freedomDimana:neff adalah jumlah uji efektif yang disesuaikan berdasarkan derajat kebebasan.Ini digunakan untuk menghitung nilai CIp pada langkah sebelumnya.4. Kesimpulan Uji KonfirmasiPersamaan 5 ini digunakan sebagai kriteria akhir validasi hasil uji konfirmasi. Jika nilai μconfirmation (hasil eksperimen pada tahap konfirmasi) berada dalam rentang prediksi μprediction +CIp maka hasil prediksi dianggap valid dan konsisten.μprediction-CIk≤μconfirmationCIk≤μk+CIp Hasil dan PembahasanBagian ini menyajikan hasil eksperimen yang diperoleh dari sembilan percobaan Taguchi (orthogonal array L₉) serta analisis mendalam terhadap pengaruh parameter proses FDM yaitu infill density, layer height, dan suhu nozzle pada dua variabel respons: waktu cetak dan berat cetakan komponen bearing berbahan PLA. Analisis dilakukan dalam beberapa tahap: penyajian data mentah, perhitungan rasio S/N, analisis ANOVA, dan interpretasi hasil untuk menarik kesimpulan tentang faktor dominan dan rekomendasi parameter optimal.Hasil EksperimenHasil eksperimen penelitian ini ditunjukkan pada Tabel 4 berikut. Data waktu pencetakan dari sembilan kombinasi parameter dianalisis menggunakan rasio signal to noise (S/N) dan ANOVA. Integrasi hasil kedua analisis tersebut akan mengidentifikasi kondisi parameter optimal dan divalidasi melalui percobaan ulang untuk menegaskan penurunan waktu pencetakan yang signifikan dan konsistensi hasil.Tabel SEQ Tabel \* ARABIC 4. Hasil EksperimenPercobaanInfill Density(%)Layer Height (mm)Nozzle Temperature(oC)Print Time (minute)Weight (gram)1600,1190749.06 2600,15200519.11 3600,2210409.12 4800,12008811.21 5800,152106011.246800,21904711.2471000,121010113.3781000,151906913.3891000,22005313.36Analisis Taguchi: Waktu Cetak Komponen BearingHasil analisis Taguchi mengenai pengaruh parameter-parameter proses cetak 3D, yaitu Infill Density, Layer Height, dan Nozzle Temperature, terhadap waktu pencetakan dapat dilihat pada tabel 5. Analisis ini menggunakan Signal to Noise Ratios (S/N Ratio) dengan pendekatan “smaller is better”, yang berarti semakin kecil nilai S/N Ratio, maka semakin optimal kondisi parameter yang dicapai dalam mengurangi waktu pencetakan. Parameter – parameter yang diuji dikelompokkan menjadi tiga level, yang kemudian dihitung nilai Delta dan Rank untuk menunjukkan pengaruh masing-masing parameter terhadap waktu pencetakan.Tabel SEQ Tabel \* ARABIC 5. Analisis Taguchi: Waktu Cetak versus Infill Density, Layer Height, Nozzle TemperatureResponse Table for Signal to Noise RatiosSmaller is betterLevelInfill Density (%)Layer Height (mm)Nozzle Temperature (oC)1-34.53-38.79-35.872-35.96-35.50-35.843-37.12-33.32-35.90Delta2.595.460.05Rank213Pada Tabel 5 menunjukkan response table for signal to noise ratios (SNR) untuk parameter cetak 3D terhadap waktu cetak, dengan metode “smaller is better”. Nilai SNR infill density pada level 1 sebesar -34.53, level 2 sebesar -35.96, level 3 sebesar -37.12, dapat dilihat dari beberapa nilai SNR infill density pada level 1 dengan nilai sebesar -34.53 adalah yang terbaik untuk proses cetak. Nilai SNR layer height pada level 1 sebesar -38.79, level 2 sebesar -35.50, level 3 sebesar -33.32, dapat dilihat dari beberapa nilai SNR layer height pada level 3 sebesar -33.32 adalah yang terbaik untuk proses cetak. Nilai SNR nozzle temperature pada level 1 sebesar -35.87, level 2 sebesar -35.84, level 3 sebesar -35.90, dapat dilihat dari beberapa nilai SNR nozzle temperature pada level 2 sebesar -35.84 adalah yang terbaik untuk proses cetak. Adapun grafik Main Effects Plot for S/N ratios, yang digunakan untuk menunjukkan pengaruh utama dari masing-masing parameter proses cetak 3D, yaitu Infill Density, Layer Height, dan Nozzle Temperature terhadap waktu pencetakan dapat dilihat pada Gambar 3. Grafik ini memberikan visualisasi bagaimana variasi setiap parameter memengaruhi nilai rata – rata Signal to Noise Ratio (S/N Ratio) dan membantu dalam memahami parameter mana yang memberikan pengaruh paling signifikan terhadap waktu cetak.Gambar 3. Grafik main effects plot for SN (Signal – to – Noise)Pada Gambar 3 menunjukkan grafik main effects plot for SN (Signal – to – Noise), yang digunakan dalam analisis Taguchi untuk mengevaluasi pengaruh masing – masing faktor terhadap hasil respon penelitian, dengan kriteria “smaller is better”. Grafik menampilkan rata – rata S/N ratio untuk tiap level dari tiga faktor proses cetak 3D (infill density, layer height, dan nozzle temperature) terhadap waktu cetak. Pada infill density terlihat garis menurun dari kiri ke kanan, yang artinya semakin tinggi infill density semakin buruk (nilai S/N ratio semakin kecil). Level infill 60% memberikan performa terbaik dalam proses cetak. Pada layer height terlihat garis naik dari 0.10 ke 0.20 mm, yang artinya semakin besar tinggi layer semakin baik. Tinggi layer 0.20 mm adalah yang paling optimal. Pada nozzle temperature terlihat garis hampir datar, yang artinya bahwa perubahan suhu nozzle tidak berdampak signifikan terhadap rata – rata S/N ratio. Nozzle temperature kurang berpengaruh terhadap performa cetak. Selanjutnya analisis varians (ANOVA) dilakukan untuk mengevaluasi kontribusi masing-masing parameter proses cetak 3D terhadap waktu pencetakan. Pada Tabel 6 menyajikan hasil analisis varians yang mencakup F-Value, P-Value, dan kontribusi persentase dari setiap parameter (Infill Density, Layer Height, dan Nozzle Temperature). Melalui analisis ini, kita dapat melihat parameter mana yang paling berpengaruh terhadap waktu pencetakan.Tabel 6. Analysis of Variance (Print Time)Analysis of Variance (Print Time)SourceAdj SSAdj MSF-ValueP-ValuePercent ContributionInfill Density560.89280.4420.350.04717%Layer Height2624.221312.1195.230.0181%Nozzle Temperature22.8911.440.830.5461%Error27.5613.781%Total3235.56100%Dari hasil analisis varians yang disajikan pada Tabel 6, terlihat bahwa parameter layer height memiliki nilai F-Value tertinggi yaitu 95.23 dan P-Value sebesar 0.01. Hal ini menunjukkan bahwa Layer Height memberikan pengaruh yang sangat signifikan terhadap waktu pencetakan, dengan kontribusi persentase sebesar 81%. Sementara itu, Infill Density memiliki F-Value sebesar 20.35 dan P-Value 0.047, yang juga signifikan meskipun tidak sebesar layer height, dengan kontribusi sebesar 17%. Parameter nozzle temperature memiliki kontribusi yang sangat kecil (hanya 1%) dan P-Value 0.546 yang menunjukkan bahwa pengaruhnya tidak signifikan terhadap waktu pencetakan, hal ini disebabkan oleh fakta bahwa variasi suhu nozzle dalam rentang yang digunakan tidak secara langsung mempengaruhi kecepatan pergerakan printer atau jumlah lapisan yang harus dicetak. Selama suhu tetap berada dalam kisaran yang cukup untuk mencairkan dan mengekstrusi filament dengan baik, perubahan suhu tidak memberikan dampak berarti terhadap durasi pencetakan.. Hasil ini memberikan wawasan penting dalam optimasi parameter cetak 3D. Fokus pada pengaturan layer height dan infill density dapat membantu meminimalkan waktu pencetakan, sementara variasi pada nozzle temperature tampaknya tidak berpengaruh besar.Analisis Taguchi: Berat Cetakan Komponen BearingHasil analisis Taguchi mengenai pengaruh parameter-parameter proses cetak 3D, yaitu Infill Density, Layer Height, dan Nozzle Temperature, terhadap berat cetakan dapat dilihat pada Tabel 7. Analisis ini menggunakan Signal to Noise Ratios (S/N Ratio) dengan pendekatan “smaller is better”, yang berarti semakin kecil nilai S/N Ratio, maka semakin optimal kondisi parameter yang dicapai dalam mengurangi waktu pencetakan. Parameter – parameter yang diuji dikelompokkan menjadi tiga level, yang kemudian dihitung nilai Delta dan Rank untuk menunjukkan pengaruh masing-masing parameter terhadap berat cetakan.Tabel 7. Analisis Taguchi: Berat Cetakan versus Infill Density, Layer Height, Nozzle TemperatureResponse Table for Signal to Noise RatiosSmaller is betterLevelInfill Density (%)Layer Height (mm)Nozzle Temperature (oC)1-19.18-20.89-20.902-21.01-20.91-20.903-22.52-20.91-20.91Delta3.350.030.02Rank123Pada Tabel 7 menunjukkan response table for signal to noise ratios (SNR) untuk parameter cetak 3D terhadap berat cetakan, dengan metode “smaller is better”. Nilai SNR infill density pada level 1 sebesar -19.18, level 2 sebesar -21.01, level 3 sebesar -22.52, dapat dilihat dari beberapa nilai SNR infill density pada level 1 dengan nilai sebesar -19.18 adalah yang terbaik untuk proses cetak. Nilai SNR layer height pada level 1 sebesar -20.89, level 2 sebesar -20.91, level 3 sebesar -20.91, dapat dilihat dari beberapa nilai SNR layer height pada level 1 sebesar -20.89 adalah yang terbaik untuk proses cetak. Nilai SNR nozzle temperature pada level 1 sebesar -20.90, level 2 sebesar -20.90, level 3 sebesar -20.91, dapat dilihat dari beberapa nilai SNR nozzle temperature pada level 1 dan 2 sebesar -20.90 adalah yang terbaik untuk proses cetak. Adapun grafik Main Effects Plot for S/N ratios, yang digunakan untuk menunjukkan pengaruh utama dari masing-masing parameter proses cetak 3D, yaitu Infill Density, Layer Height, dan Nozzle Temperature terhadap waktu pencetakan dapat dilihat pada Gambar 4. Grafik ini memberikan visualisasi bagaimana variasi setiap parameter memengaruhi nilai rata-rata Signal to Noise Ratio (S/N Ratio) dan membantu dalam memahami parameter mana yang memberikan pengaruh paling signifikan terhadap berat cetak.Gambar 4. Grafik Main Effect Plot for SNR terhadap Berat CetakanPada Gambar 4 menunjukkan grafik main effects plot for SN (Signal – to – Noise), yang digunakan dalam analisis Taguchi untuk mengevaluasi pengaruh masing – masing faktor terhadap hasil respon penelitian, dengan kriteria “smaller is better”. Grafik menampilkan rata – rata S/N ratio untuk tiap level dari tiga faktor proses cetak 3D (infill density, layer height, dan nozzle temperature) terhadap berat sampel. Pada infill density terlihat garis menurun dari 60 ke 100, yang artinya semakin tinggi infill density semakin buruk (nilai S/N ratio semakin kecil). Level infill 60% memberikan performa terbaik dalam proses cetak. Pada layer height dan nozzle temperature terlihat garis hampir datar, yang artinya bahwa perubahan suhu nozzle tidak berdampak signifikan terhadap rata – rata S/N ratio. Layer height dan nozzle temperature kurang berpengaruh terhadap performa cetakSelanjutnya analisis varians (ANOVA) juga dilakukan untuk mengevaluasi pengaruh masing-masing parameter proses cetak 3D terhadap berat hasil cetakan. Pada Tabel 8 berikut menyajikan hasil ANOVA yang menunjukkan F-Value, P-Value, dan kontribusi persentase masing – masing parameter (Infill Density, Layer Height, dan Nozzle Temperature) terhadap berat cetakan.Tabel 8. Analysis of Variance (Weight)SourceAdj SSAdj MSF-ValueP-ValuePercent ContributionInfill Density560.89280.4420.350.04717%Layer Height2624.221312.1195.230.0181%Nozzle Temperature22.8911.440.830.5461%Error27.5613.781%Lack – of – Fit0.39820.39820.400.870Pure Eror9.24509.2450Total3235.56100%Hasil analisis varians pada Tabel 8 menunjukkan bahwa tidak ada parameter yang secara signifikan memengaruhi berat cetakan (P-Value > 0.05 untuk semua parameter). Meskipun demikian, Infill Density terlihat memberikan kontribusi terbesar yaitu 28%, diikuti oleh Nozzle Temperature dan Layer Height yang masing-masing memiliki kontribusi sebesar 7%. Kontribusi terbesar justru berasal dari faktor Error (35%), yang menunjukkan adanya variabilitas yang tidak dijelaskan oleh parameter yang dianalisis. Nilai P-Value yang tinggi (misalnya, 0.559 untuk Infill Density dan 0.836 untuk Layer Height) mengindikasikan bahwa perbedaan berat cetakan tidak cukup signifikan secara statistik akibat variasi parameter – parameter tersebut. Hal ini bisa disebabkan oleh beberapa faktor, seperti sensitivitas timbangan yang digunakan, ketidakkonsistenan pada proses pendinginan, atau fluktuasi kualitas filament yang digunakan. Berat cetakan sering kali dipengaruhi oleh aspek yang tidak terkontrol secara langsung dalam parameter proses, seperti kelembaban udara, variasi diameter filament, keausan nozzle, atau bahkan kesalahan kalibrasi printer. Secara keseluruhan, meskipun Infill Density tampak memiliki pengaruh paling besar terhadap berat cetakan, hasil ini perlu diinterpretasikan dengan hati-hati mengingat kontribusi Error yang besar dan ketidaksignifikansian hasil uji ANOVA.Validasi Parameter Optimal Validasi dilakukan untuk memastikan bahwa parameter-parameter yang telah diidentifikasi sebagai berpengaruh signifikan benar-benar memberikan hasil yang konsisten dan optimal pada proses cetak 3D. Melalui analisis varians (ANOVA) yang telah dilakukan sebelumnya, parameter – parameter seperti Infill Density, Layer Height, dan Nozzle Temperature telah dievaluasi secara kuantitatif. Langkah validasi ini bertujuan untuk menguji kembali keandalan pengaturan parameter optimal yang telah diusulkan dan memastikan bahwa hasil cetak yang dihasilkan sesuai dengan harapan serta memenuhi standar kualitas yang diinginkan. Hasil dari tahap validasi ini diharapkan dapat memberikan dasar yang kuat bagi implementasi parameter optimal dalam proses produksi cetak 3D secara lebih luas.Uji KonfirmasiPada tahap ini, dilakukan uji konfirmasi untuk memastikan keandalan dan validitas kondisi parameter proses yang telah ditentukan sebagai parameter optimal pada percobaan sebelumnya. Uji konfirmasi bertujuan untuk mengevaluasi apakah nilai respon yang dihasilkan benar – benar sesuai dengan prediksi yang dihitung oleh model. Pengujian ini penting untuk meningkatkan kepercayaan pada hasil optimasi serta memastikan bahwa kondisi proses yang dioptimalkan memberikan performa yang stabil dan konsisten. Dalam pengujian ini, uji konfirmasi dilakukan sebanyak tiga kali pada parameter proses yang sama, yang dapat dilihat pada Tabel 9Tabel 9. Parameter Uji KonfimasiPercobaanInfill Density (%)Layer Height (mm)Nozzle Temperature (oC)1.60%0.2 mm200oC2.60%0.2 mm200oC3.60%0.2 mm200oCSetelah dilakukan analisis ANOVA terhadap faktor – faktor yang mempengaruhi respons proses pencetakan 3D, langkah selanjutnya adalah melakukan prediksi terhadap nilai optimal dari masing – masing respons. Prediksi ini digunakan untuk memperkirakan nilai rata – rata dari respons berdasarkan level terbaik dari setiap faktor. Selain itu, untuk memastikan bahwa hasil prediksi tersebut dapat diandalkan, dilakukan pula perhitungan Confidence Interval (CI) yang merepresentasikan rentang nilai prediksi dengan tingkat keyakinan 95%. Perhitungan dapat dihitung menggunakan persamaan 2, 3, 4, dan 5Prediksi Berat CetakNilai prediksi rata – rata μprediction untuk berat cetak dihitung berdasarkan pengaruh dari level terbaik masing – masing faktor terhadap nilai rata – rata umum ym: μprediction=-20.9033+((-19.18-(-20.9033)+(-20.89-(-20.9033))+(-20.9-(-20.9033)))=-19.163Confidence Interval (CI) Berat CetakConfidence Interval digunakan untuk menunjukkan rentang nilai prediksi dengan keyakinan 95%. Nilai ini dihitung dengan rumus:neff=9×31+(2×3)=277CIp=19.00×0.6432277=3.50931maka:-19.1633-3.50931 ≤-19.1633≤-19.1633+3.50931 -22.6726≤-19.1633≤-15.654 Artinya, rentang prediksi berat cetak berada antara -22.67 hingga -15.65, dan hasil eksperimen dianggap valid jika berada dalam rentang ini.Prediksi Waktu CetakNilai prediksi rata – rata μprediction untuk waktu cetak dihitung berdasarkan pengaruh dari level terbaik masing – masing faktor terhadap nilai rata – rata umum ym:μprediction=-35.87+((-34.53-(-35.87)+(-33.32-(-35.87))+(-35.84-(-35.87)))=-45.4858Confidence Interval (CI) Berat CetakConfidence Interval digunakan untuk menunjukkan rentang nilai prediksi dengan keyakinan 95%. Nilai ini dihitung dengan rumus:neff=9×31+(2×3)=277CIp=19.00×27.56277=5.93268maka:-45.4858-5.93268 ≤-45.4858≤-45.4858+5.93268 -51.4185≤-19.1633≤-39.5531 Rentang prediksi waktu cetak berada antara -51.42 hingga -39.55, dan hasil eksperimen dikatakan konsisten jika berada dalam rentang ini.Dari hasil analisis prediksi dan perhitungan Confidence Interval, dapat disimpulkan bahwa prediksi untuk berat dan waktu cetak memiliki rentang kepercayaan yang wajar dan dapat dijadikan dasar untuk konfirmasi eksperimen. Jika hasil aktual berada dalam rentang CI, maka desain eksperimental dianggap valid dan konsisten.KesimpulanHasil penelitian menunjukkan bahwa layer height adalah faktor paling dominan menurunkan waktu cetak komponen bearing PLA ketika diatur 0,20 mm, durasi cetak berkurang hingga sekitar 40 menit (vs. 74 menit pada 0,10 mm) dengan kontribusi 81 % (p = 0,010). Infill density 60 % juga signifikan mempersingkat waktu (kontribusi 17 %, p = 0,047) sambil menekan bobot menjadi 9 gram (berat naik menjadi 13,3 gram pada infill 100 %). Suhu nozzle pada rentang 190 – 210 °C tidak memberikan pengaruh signifikan terhadap waktu maupun berat cetak (p > 0,5). Validasi ulang (3 kali cetak optimal: infill 60 %, layer height 0,20 mm, suhu 200 °C) menghasilkan waktu rata – rata 40 ± 1 menit dan berat 9,10 ± 0,02 gram, membuktikan konsistensi dan reproduksibilitas parameter tersebut. Variabilitas tinggi (35 %) pada berat cetakan mengindikasikan pengaruh faktor eksternal seperti kelembaban dan toleransi filamen. Berdasarkan temuan, disarankan untuk mencetak bearing PLA dengan layer height 0,20 mm, infill density 60 %, dan suhu nozzle 200 °C, sehingga waktu cetak dan penggunaan bahan tetap efisien tanpa mengorbankan stabilitas dimensi. Pastikan filamen PLA disimpan kering (drying di 45 °C selama 4–6 jam), printer berada di ruang bersuhu 22 ± 1 °C dan kelembaban 45 ± 5 %, serta timbangan dikalibrasi rutin. Untuk penelitian lanjutan, perlu dilakukan uji mekanik (tensile/geser) pada bearing hasil cetak, analisis kekasaran permukaan dan toleransi dimensi, serta eksplorasi variabel tambahan (print speed, flow rate, suhu platform). Selain itu, uji pada material lain (ABS, PETG, PLA-komposit) atau penggunaan pendekatan multi – objective optimization dapat menambah keandalan dan generalisasi prosedur optimasi FDM.Referensi[1]T. Narancic et al., “Biodegradable Plastic Blends Create New Possibilities for End-of-Life Management of Plastics but They Are Not a Panacea for Plastic Pollution,” Environ Sci Technol, vol. 52, no. 18, pp. 10441–10452, Sep. 2018, doi: 10.1021/acs.est.8b02963.[2]U. Unaijah and S. Darwis, “Prediksi Sisa Umur Bearing Menggunakan Distribusi Weibull,” Jurnal Riset Statistika, pp. 73–81, Jul. 2022, doi: 10.29313/jrs.vi.909.[3]Y. Sun et al., “Grafting Lignin with Bioderived Polyacrylates for Low-Cost, Ductile, and Fully Biobased Poly(lactic acid) Composites,” ACS Sustain Chem Eng, vol. 8, no. 5, pp. 2267–2276, Feb. 2020, doi: 10.1021/acssuschemeng.9b06593.[4]C. Grabowik, K. Kalinowski, G. Ćwikła, I. Paprocka, and P. Kogut, “Tensile tests of specimens made of selected group of the filament materials manufactured with FDM method.”[5]A. Rochman Fachrudin, F. Andika Frida Astuti, and P. Negeri Malang, “Pengaruh Layer Height Dan Printing Speed Terhadap Tingkat Kekasaran Permukaan Hasil Additive Manufacturing,” 2024.[6]M. Behzadnasab, A. A. Yousefi, D. Ebrahimibagha, and F. 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Qian, “Effect of printing parameters on material distribution in spray-based 3D concrete printing (S-3DCP),” Autom Constr, vol. 124, pp. 1989–2002, 2021, doi: 10.1016/j.autcon.2021.103570.[16]F. Lubis, R. Pane, S. Lubis, M. A. Siregar, and B. Santri Kusuma, “Jurnal Mesil (Mesin, Elektro, Sipil,) Analisa Kekuatan Bearing Pada Prototype Belt Conveyor,” vol. 2, no. 2, pp. 51–57, 2021. R.E.M. (Rekayasa Energi Manufaktur) Jurnal | Vol.10 No.2/2025ISSN online (2528-3723)http://doi.org/10.21070/rem.v10i2.1769Copyright © 2025 Author [s]. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsDewi, S. N. P., & Lestari, W. D., Optimasi Proses Cetak 3D FDM untuk Komponen Bearing Berbasis PLA Menggunakan Metode Taguchi, R.E.M. (Rekayasa Energi Manufaktur) Jurnal, vol. 10, no. 2, pp. 207-218, 2025. 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