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REFERENCES
Ayuningtias, L. P., Irfan, M. and Jumadi, J. (2017) ‘Analisa Perbandingan Logic Fuzzy Metode Tsukamoto, Sugeno, Dan Mamdani (Studi Kasus : Prediksi Jumlah Pendaftar Mahasiswa Baru Fakultas Sains Dan Teknologi
Universitas Islam Negeri Sunan Gunung Djati Bandung)’, Jurnal Teknik Informatika, 10(1). doi: 10.15408/jti.v10i1.5610.
Azizah, N., Adi, K. and Widodo, A. (2016) ‘Metode Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Prediksi Tingkat Layanan Jalan’, Jurnal Sistem Informasi Bisnis, 3(3), pp. 127–131. doi: 10.21456/vol3iss3pp.
Chan, K. Y., Kwong, C. K. and Fogarty, T. C. (2010) ‘Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers’, Information Sciences, 180(4), pp. 506–518. doi:
10.1016/j.ins.2009.10.007.
Chaves, M. L. et al. (2020) ‘Experimental assessment of quality in injection parts using a fuzzy system with adaptive membership functions’,
Neurocomputing, 391, pp. 334–344. doi: 10.1016/j.neucom.2019.06.108.
Chiang, K. T. (2007) ‘The optimal process conditions of an injection-molded thermoplastic part with a thin shell feature using grey-fuzzy logic: A case study on machining the PC/ABS cell phone shell’, Materials and Design, 28(6), pp. 1851–1860. doi: 10.1016/j.matdes.2006.04.008.
Chiang, K. T. and Chang, F. P. (2006) ‘Application of grey-fuzzy logic on the optimal process design of an injection- molded part with a thin shell
feature’, International Communications in Heat and Mass Transfer, 33(1), pp. 94–101. doi: 10.1016/j.icheatmasstransfer.2005.08.006.
Dang, X. P. (2014) ‘General frameworks for optimization of plastic injection molding process parameters’, Simulation Modelling Practice and Theory.
Elsevier B.V., 41, pp. 15–27. doi: 10.1016/j.simpat.2013.11.003.
Dawoud, M., Taha, I. and Ebeid, S. J. (2016) ‘Mechanical behaviour of ABS: An experimental study using FDM and injection moulding techniques’, Journal of Manufacturing Processes. The Society of Manufacturing
Page 96 of 99
Neilinda Novita Aisa The Effect of Injection Parameter on Acrylonitrile Butadiene
Styrene (ABS) Products Using Fuzzy Logic System
Engineers, 21, pp. 39–45. doi: 10.1016/j.jmapro.2015.11.002.
Debasis Samanta (1965) ‘Chapter 3: Fuzzy Membership Function Formulation and Parameterization’, in.
Despa, M. S., Kelly, K. W. and Collier, J. R. (1999) ‘Injection molding of polymeric LIGA HARMs’, Microsystem Technologies, 6(2), pp. 60–66.
doi: 10.1007/s005420050176.
Devalia, P. T. et al. (2019) ‘Analisis dan Optimasi Parameter Proses Injeksi Plastik Multi Cavity untuk Meminimalkan Cacat Short Mold’, pp. 553–
560.
Devillez, A. et al. (2015) ‘Use of the fuzzy pattern matching method for diagnosis of a plastic injection moulding process’, European Control Conference, ECC 1999 - Conference Proceedings, pp. 2114–2119. doi:
10.23919/ecc.1999.7099631.
Dimas, E. et al. (2016) ‘Pengaruh Setting Parameter Mesin Injeksi Terhadap Mechanical Properties Produk Material Acrylonitril Butadiene Styrene ( Abs ) Dengan Menggunakan Metode 3k’, (Snttm Xv), pp. 5–7.
Kitayama, S. and Natsume, S. (2014) ‘Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization’, Simulation Modelling Practice and Theory.
Elsevier B.V., 48, pp. 35–44. doi: 10.1016/j.simpat.2014.07.004.
Kramar, D. and Cica, D. (2017) ‘Predictive model and optimization of processing parameters for plastic injection moulding’, Materiali in Tehnologije, 51(4), pp. 597–602. doi: 10.17222/mit.2016.129.
Moayyedian, M., Abhary, K. and Marian, R. (2018) ‘Optimization of injection molding process based on fuzzy quality evaluation and Taguchi
experimental design’, CIRP Journal of Manufacturing Science and Technology, 21, pp. 150–160. doi: 10.1016/j.cirpj.2017.12.001.
Mohammed, H. S., Elangovan, K. and Subrahmanian, V. (2016) ‘Indian Journal of Advances in Chemical Science Studies on Aramid Short Fibers Reinforced Acrylonitrile Butadiene Rubber Composites’, pp. 458–463.
Page 97 of 99
Neilinda Novita Aisa The Effect of Injection Parameter on Acrylonitrile Butadiene
Styrene (ABS) Products Using Fuzzy Logic System
Mu, A. (2019) ‘済無No Title No Title’, Journal of Chemical Information and Modeling, 53(9), pp. 1689–1699. doi: 10.1017/CBO9781107415324.004.
Muhajirah, A. et al. (2019) ‘Analisis Tingkat Akurasi Metode Neuro Fuzzy dalam Prediksi Data IPM di NTB’, JTAM | Jurnal Teori dan Aplikasi
Matematika, 3(1), p. 58. doi: 10.31764/jtam.v3i1.769.
Nasution, H. (2007) Energy Analysis of an Air Conditioning System Using PID and Fuzzy Logic Controllers.
Strong, A. B. (2006) Plastics Materials and Processing : Third Edition. Third.
Columbus, Ohio.
Toshiba (1988) Instruction Manual for IS_EPN Injection Molding Machine. 1st edn. Tokyo.
Vagelatos, G. A., Rigatos, G. G. and Tzafestas, S. G. (2001) ‘Incremental fuzzy supervisory controller design for optimizing the injection molding process’, Expert Systems with Applications, 20(2), pp. 207–216. doi:
10.1016/S0957-4174(00)00060-9.
Vishnuvaradhan, S. et al. (2013) ‘Intelligent modeling using adaptive neuro fuzzy inference system (ANFIS) for predicting weld bead shape parameters during A-TIG welding of reduced activation ferritic-martensitic (RAFM) steel’, Transactions of the Indian Institute of Metals, 66(1), pp. 57–63. doi:
10.1007/s12666-012-0178-x.
Vishwakarma, S. K., Pandey, P. and Gupta, N. K. (2017) ‘Characterization of ABS Material: A Review’, Journal of Research in Mechanical
Engineering, 3(5), pp. 13–16.
Wang, C. (2015) ‘A Study of Membership Functions on Mamdani-Type Fuzzy Inference System for Industrial Decision-Making’, Theses and
Dissertations.
Wijaya, H. (2017) Teknologi Pengolahan Plastik : Injection Molding.
Yang, D. et al. (2014) ‘Computer determination of weld lines in injection molding based on filling simulation with surface model’, Journal of Reinforced
Page 98 of 99
Neilinda Novita Aisa The Effect of Injection Parameter on Acrylonitrile Butadiene
Styrene (ABS) Products Using Fuzzy Logic System
Plastics and Composites, 33(15), pp. 1403–1415. doi:
10.1177/0731684414535277.
Yen, C. et al. (2006) ‘An abductive neural network approach to the design of runner dimensions for the minimization of warpage in injection
mouldings’, Journal of Materials Processing Technology, 174(1–3), pp.
22–28. doi: 10.1016/j.jmatprotec.2005.02.233.