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Monitoring Keausan Pahat Menggunakan Artificial Neural Networks pada Proses Turning

dc.contributor.authorWidiantoro, Heri
dc.contributor.authorFikri, Ahmad Atif
dc.contributor.authorMahardika, Muslim
dc.date.accessioned2014-07-14T04:26:09Z
dc.date.available2014-07-14T04:26:09Z
dc.date.issued2014-03-27
dc.identifier.citationA. Siddhpura & R. Paurobally, A review of flank wear prediction methods for tool condition monitoring in a turning process, International Journal Advance Manufacture Technology 65:371–393, 2013. Dimla Snr. D.E., Multivariate tool condition monitoring in a metal cutting operation using neural networks. Ph.D. thesis, School of Engineering and the Built Environment, The University of Wolverhampton, UK, 1998. Dimla E. Dimla Snr., Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods, International Journal of Machine Tools & Manufacture Vol 40. pp 1073– 1098, 2000. Dimla Snr D.E., Tool wear monitoring using cutting force measurements, in: 15th NCMR: Advances in Manufacturing Technology XIII, University of Bath, 6–8 September, 1999, pp. 33–37. Dimla Snr D.E., P.M. Lister, On-line metal cutting tool condition monitoring—I: Force and vibration analyses, International Journal of Machine Tools and Manufacture, Vol 40 (5) 739–768, 2000. D.R. Salgado, F.J. Alonso, An approach based on current and sound signals for n-process tool wear monitoring, International Journal of Machine Tools & Manufacture 47 (2007) 2140– 2152 F.J. Alonso, D.R. Salgado,Application of singular spectrum analysis to tool wear detection using sound signals, Proceedings of the IMechE Journal of Engineering Manufacture 219 (9) (2005) 703–710 Hongli Gao, Mingheng Xu, Intelligent Tool Condition Monitoring System for Turning Operations, School Of Mechanical Engineering, Southwest Jiaotong University Chengdu, Sichuan 610031, China, 2005. ISO 3685, International standard second edition, 1993. Jong Jek Siang, Jaringan syaraf tiruan, Andi, 2005. Matlab, 2010. Help File Muslim Mahardika, Neural Networks Prediction of Cutting Tool Wear During Turning Operation. Master Thesis, University of malaya, 2005. Puhar J., 1st Seminar on Manufacturing Technologies, University of Ljubljana, Slovenia, pp. 1–19, 1999. Rao P, N. 2000. Manufacturing Technology, Metal Cutting and Machine Tools, Singapore : McGraw Hill Higher Education,. Taylor F.W., Trans. ASME, 28:31-279, 1907. Tien-I Liu, Shin-Da Song, George Liu, Zhang Wu, Online monitoring and measurements of tool wear for precision turning of stainless steel parts, International journal advance manufacture technology, vol.65 pp.1397-1407, 2013. Tizit Maxiflex Universal Tooling System Catalogue, 2002 Usha Nair, Bindu M. Krishna, V. N. N. Namboothiri and V. P. N. Nampoori, Permutation entropy based real-time chatter detection using audio signal in turning process,International Journal Advance Manufacture Technology vol 46, pp 61–68. 2010.en_US
dc.identifier.issn2337-4349
dc.identifier.urihttp://hdl.handle.net/11617/4561
dc.description.abstractDalam penelitian ini dikembangkan sebuah sistem monitoring dengan menggunakan Artificial Neural Networks (ANN) Backpropagation untuk memprediksi keausan cutting tool (pahat) sehingga diharapkan dapat meningkatkan produktifitas dan mencegah lebih dini kerugian akibat keausan pahat seperti permukaan komponen tidak rata, pahat rusak (chipping) dan perawatan mesin tidak terjadwal yang dapat berdampak pada membengkaknya biaya produksi. Sinyal suara selama proses pemotongan akan ditangkap oleh microphone dan diproses menggunakan software LabVIEW berupa time domain dan frequency domain. Sinyal yang diterima oleh LabVIEW kemudian difilter sehingga nilai yang muncul merupakan sinyal dari proses pemotongan dan bukan noise dari luar. Sinyal tersebut digunakan sebagai informasi untuk menentukan pola atau karakteristik ketika pahat aus dan digunakan untuk membangun jaringan ANN Backpropagation. Arsitektur ANN Backpropagation dengan menggunakan fungsi aktivasi sigmoid biner (logsig), 2 neuron pada input layer, 500 neuron pada hidden layer dan 2 neuron pada output layer (2 × 500 × 2) mampu mengenali kondisi pahat selama proses pemotongan dengan memberikan hasil kinerja sebesar 92%en_US
dc.publisherUniversitas Muhammadiyah Surakartaen_US
dc.subjectANNen_US
dc.subjectbackpropagationen_US
dc.subjectonline monitoringen_US
dc.subjectturningen_US
dc.titleMonitoring Keausan Pahat Menggunakan Artificial Neural Networks pada Proses Turningen_US
dc.typeArticleen_US


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