{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:56:37Z","timestamp":1763664997840,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031184604"},{"type":"electronic","value":"9783031184611"}],"license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-18461-1_12","type":"book-chapter","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T07:15:14Z","timestamp":1665558914000},"page":"185-197","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pre-trained CNN Based SVM Classifier for Weld Joint Type Recognition"],"prefix":"10.1007","author":[{"given":"Satish","family":"Sonwane","sequence":"first","affiliation":[]},{"given":"Shital","family":"Chiddarwar","sequence":"additional","affiliation":[]},{"given":"M. R.","family":"Rahul","sequence":"additional","affiliation":[]},{"given":"Mohsin","family":"Dalvi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"issue":"4","key":"12_CR1","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1007\/s40194-019-00723-2","volume":"63","author":"U Reisgen","year":"2019","unstructured":"Reisgen, U., Mann, S., Middeldorf, K., Sharma, R., Buchholz, G., Willms, K.: Connected, digitalized welding production\u2014industrie 4.0 in gas metal arc welding. Welding in the World 63(4), 1121\u20131131 (2019). https:\/\/doi.org\/10.1007\/s40194-019-00723-2","journal-title":"Welding in the World"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Mahadevan, R., Jagan, A., Pavithran, L., Shrivastava, A., Selvaraj, S.K.: Intelligent welding by using machine learning techniques. Mater. Today Proc. 46, 74027410 (2021). https:\/\/doi.org\/10.1016\/j.matpr.2020.12.1149","DOI":"10.1016\/j.matpr.2020.12.1149"},{"key":"12_CR3","unstructured":"Xiuping, W., Fan, X., Ying, F.: Recognition of the Type of Welding Joint Based on Line Structured Light Vision, pp. 4403\u20134406 (2015)"},{"issue":"7\u20138","key":"12_CR4","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s00170-005-0104-z","volume":"30","author":"X Chen","year":"2006","unstructured":"Chen, X., Chen, S., Lin, T., Lei, Y.: Practical method to locate the initial weld position using visual technology. Int. J. Adv. Manuf. Technol. 30(7\u20138), 663\u2013668 (2006). https:\/\/doi.org\/10.1007\/s00170-005-0104-z","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Hong, T.S., Ghobakhloo, M., Khaksar, W.: Robotic Welding Technology 6. Elsevier (2014). https:\/\/doi.org\/10.1016\/B978-0-08-096532-1.00604-X","DOI":"10.1016\/B978-0-08-096532-1.00604-X"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Zhang, Y.M., Feng, Z., Chen, S.: Trends in intelligentizing robotic welding processes. J. Manuf. Process. 63, 1 (2021). https:\/\/doi.org\/10.1016\/j.jmapro.2020.11.012","DOI":"10.1016\/j.jmapro.2020.11.012"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Zeng, J., Cao, G.Z., Peng, Y.P., Huang, S.D.: A weld joint type identification method for visual sensor based on image features and SVM. Sensors (Switzerland) 20(2), 471 (2020). https:\/\/doi.org\/10.3390\/s20020471","DOI":"10.3390\/s20020471"},{"issue":"1-4","key":"12_CR8","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1007\/s00170-017-0202-8","volume":"92","author":"J Fan","year":"2017","unstructured":"Fan, J., Jing, F., Fang, Z., Tan, M.: Automatic recognition system of welding seam type based on SVM method. Int. J. Advanced Manufacturing Technol. 92(1\u20134), 989\u2013999 (2017). https:\/\/doi.org\/10.1007\/s00170-017-0202-8","journal-title":"Int. J. Advanced Manufacturing Technol."},{"issue":"4","key":"12_CR9","doi-asserted-by":"publisher","first-page":"5402","DOI":"10.1109\/JSEN.2020.3034382","volume":"21","author":"Y Tian","year":"2021","unstructured":"Tian, Y., et al.: Automatic identification of multi-type weld seam based on vision sensor with silhouette-mapping. IEEE Sens. J. 21(4), 5402\u20135412 (2021). https:\/\/doi.org\/10.1109\/JSEN.2020.3034382","journal-title":"IEEE Sens. J."},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Wang, Z., Jing, F., Fan, J.: Weld seam type recognition system based on structured light vision and ensemble learning. In: Proceedings 2018 IEEE International Conference Mechatronics Autom. ICMA 2018, no. 61573358, pp. 866\u2013871 (2018). https:\/\/doi.org\/10.1109\/ICMA.2018.8484570","DOI":"10.1109\/ICMA.2018.8484570"},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.rcim.2017.12.007","volume":"51","author":"HNM Shah","year":"2018","unstructured":"Shah, H.N.M., Sulaiman, M., Shukor, A.Z., Kamis, Z., Rahman, A.A.: \u201cButt welding joints recognition and location identification by using local thresholding,\u201d robot. Comput. Integr. Manuf. 51, 181\u2013188 (2018). https:\/\/doi.org\/10.1016\/j.rcim.2017.12.007","journal-title":"Comput. Integr. Manuf."},{"issue":"11","key":"12_CR12","first-page":"1129","volume":"16","author":"Y Li","year":"2006","unstructured":"Li, Y., Xu, D., Tan, M.: Welding joints recognition based on Hausdorff distance. Gaojishu Tongxin\/Chinese High Technol. Lett. 16(11), 1129\u20131133 (2006)","journal-title":"Gaojishu Tongxin\/Chinese High Technol. Lett."},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.optlastec.2018.08.047","volume":"109","author":"J Fan","year":"2019","unstructured":"Fan, J., Jing, F., Yang, L., Long, T., Tan, M.: A precise seam tracking method for narrow butt seams based on structured light vision sensor. Opt. Laser Technol. 109, 616\u2013626 (2019). https:\/\/doi.org\/10.1016\/j.optlastec.2018.08.047","journal-title":"Opt. Laser Technol."},{"issue":"January","key":"12_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.optlaseng.2018.01.008","volume":"105","author":"Y Zou","year":"2018","unstructured":"Zou, Y., Chen, T.: Laser vision seam tracking system based on image processing and continuous convolution operator tracker. Opt. Lasers Eng. 105(January), 141\u2013149 (2018). https:\/\/doi.org\/10.1016\/j.optlaseng.2018.01.008","journal-title":"Opt. Lasers Eng."},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Chen, S., Liu, J., Chen, B., Suo, X.: Universal fillet weld joint recognition and positioning for robot welding using structured light. Robot. Comput. Integr. Manuf., 74, 102279 (2021). https:\/\/doi.org\/10.1016\/j.rcim.2021.102279","DOI":"10.1016\/j.rcim.2021.102279"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Tang, Y.: Deep Learning using Linear Support Vector Machines (2013). https:\/\/doi.org\/10.48550\/ARXIV.1306.0239","DOI":"10.48550\/ARXIV.1306.0239"},{"key":"12_CR17","unstructured":"Agarap, A.F.: An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification, pp. 5\u20138 (2017). http:\/\/arxiv.org\/abs\/1712.03541"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Jiang, S., Hartley, R., Fernando, B.: Kernel support vector machines and convolutional neural networks. 2018 Int. Conf. Digit. Image Comput. Tech. Appl. DICTA, pp. 1\u20137 (2019). https:\/\/doi.org\/10.1109\/DICTA.2018.8615840","DOI":"10.1109\/DICTA.2018.8615840"},{"issue":"2019","key":"12_CR19","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1016\/j.procs.2020.03.309","volume":"167","author":"S Ahlawat","year":"2020","unstructured":"Ahlawat, S., Choudhary, A.: Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Comput. Sci. 167(2019), 2554\u20132560 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.03.309","journal-title":"Procedia Comput. Sci."},{"key":"12_CR20","unstructured":"Kaggle: Rectified Linear Units (ReLU) in Deep Learning. https:\/\/www.kaggle.com\/dansbecker\/rectified-linear-units-relu-in-deep-learning"},{"issue":"1-2","key":"12_CR21","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10472-014-9440-8","volume":"75","author":"N Hantos","year":"2014","unstructured":"Hantos, N., Iv\u00e1n, S., Bal\u00e1zs, P., Pal\u00e1gyi, K.: Binary image reconstruction from a small number of projections and the morphological skeleton. Ann. Math. Artif. Intell. 75(1\u20132), 195\u2013216 (2014). https:\/\/doi.org\/10.1007\/s10472-014-9440-8","journal-title":"Ann. Math. Artif. Intell."},{"key":"12_CR22","unstructured":"MathWorks: ResNet-18 convolutional neural network - MATLAB resnet18 - MathWorks India. https:\/\/in.mathworks.com\/help\/deeplearning\/ref\/resnet18.html"},{"issue":"5","key":"12_CR23","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/64.163674","volume":"7","author":"C Cortes","year":"1992","unstructured":"Cortes, C., Vapnik, V.: Support-vector network. IEEE Expert. Syst. their Appl. 7(5), 63\u201372 (1992). https:\/\/doi.org\/10.1109\/64.163674","journal-title":"IEEE Expert. Syst. their Appl."},{"key":"12_CR24","unstructured":"MathWorks: ClassificationECOC. https:\/\/in.mathworks.com\/help\/stats\/classificationecoc.html"},{"key":"12_CR25","unstructured":"\"Weld-Joint-Segments | Kaggle. https:\/\/www.kaggle.com\/datasets\/derikmunoz\/weld-joint-segments Accessed 1 Apr 2022"},{"key":"12_CR26","unstructured":"MathWorks Inc.: Fit multi-class models for support vector machines or other classifiers (2018). https:\/\/in.mathworks.com\/help\/stats\/fitcecoc.html Accessed 1 Apr 2022"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18461-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T07:21:14Z","timestamp":1665559274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18461-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"ISBN":["9783031184604","9783031184611"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18461-1_12","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022,10,13]]},"assertion":[{"value":"13 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FTC 2022","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of the Future Technologies Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ftc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/FTC","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}