Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-020-01624-8
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
- Changqing Liu & Yingguang Li & Guanyan Zhou & Weiming Shen, 2018. "A sensor fusion and support vector machine based approach for recognition of complex machining conditions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1739-1752, December.
- Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
- Zhonglei Liu & Xuekun Li & Dingzhu Wu & Zhiqiang Qian & Pingfa Feng & Yiming Rong, 2019. "The development of a hybrid firefly algorithm for multi-pass grinding process optimization," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2457-2472, August.
- Guiqian Liu & Xiangdong Gao & Deyong You & Nanfeng Zhang, 2019. "Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 821-832, February.
- Chinmaya P. Mohanty & Siba Sankar Mahapatra & Manas Ranjan Singh, 2016. "A particle swarm approach for multi-objective optimization of electrical discharge machining process," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1171-1190, December.
- Kalipada Maity & Himanshu Mishra, 2018. "ANN modelling and Elitist teaching learning approach for multi-objective optimization of $$\upmu $$ μ -EDM," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1599-1616, October.
- Feng Zhang & Taotao Zhou, 2019. "Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regression," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2217-2230, June.
- Shujie Liu & Yawei Hu & Chao Li & Huitian Lu & Hongchao Zhang, 2017. "Machinery condition prediction based on wavelet and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 1045-1055, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Roham Sadeghi Tabar & Kristina Wärmefjord & Rikard Söderberg & Lars Lindkvist, 2021. "Critical joint identification for efficient sequencing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 769-780, March.
- Sangho Lee & Youngdoo Son, 2021. "Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-21, June.
- Xiang Zhu & Yunqiu Zhang, 2020. "Co-word analysis method based on meta-path of subject knowledge network," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 753-766, May.
- Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
- Andhi Indira Kusuma & Yi-Mei Huang, 2023. "Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1683-1699, April.
- Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
- Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
- Lenin Nagarajan & Siva Kumar Mahalingam & Jayakrishna Kandasamy & Selvakumar Gurusamy, 2022. "A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1337-1354, June.
- Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
- Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
- Sujata Dash & Ajith Abraham & Ashish Kr Luhach & Jolanta Mizera-Pietraszko & Joel JPC Rodrigues, 2020. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.
- Runquan Xiao & Yanling Xu & Zhen Hou & Chao Chen & Shanben Chen, 2022. "An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1419-1432, June.
- Wenchao Yi & Liang Gao & Zhi Pei & Jiansha Lu & Yong Chen, 2021. "ε Constrained differential evolution using halfspace partition for optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 157-178, January.
- Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
- Matteo Bugatti & Bianca Maria Colosimo, 2022. "Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 293-309, January.
- Zhen Zhang & Zenan Yang & Chenchong Wang & Wei Xu, 2024. "Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 449-465, January.
- Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
- Wei Qin & Dongye Zha & Jie Zhang, 2020. "An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1661-1671, October.
- Alon Ratner & Michael Wood & Maximilian Chowanietz & Nikhil Kumar & Rashik Patel & Paul Hadlum & Abhishek Das & Iain Masters, 2022. "Laser Doppler Vibrometry for Evaluating the Quality of Welds in Lithium-Ion Supercells," Energies, MDPI, vol. 15(12), pages 1-20, June.
- Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
More about this item
Keywords
Electrical discharge machining (EDM); Selective laser sintering (SLS); Least square support vector machine (LSSVM); Desirability grey relational analysis (DGRA); Firefly algorithm (FA);All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-020-01624-8. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.