| Joining of dissimilar materials K Martinsen, SJ Hu, BE Carlson Cirp Annals 64 (2), 679-699, 2015 | 644 | 2015 |
| Trends and research challenges in remanufacturing M Matsumoto, S Yang, K Martinsen, Y Kainuma International journal of precision engineering and manufacturing-green …, 2016 | 296 | 2016 |
| Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: A conceptual framework I Baturynska, O Semeniuta, K Martinsen Procedia Cirp 67, 227-232, 2018 | 165 | 2018 |
| Integration of digital learning in industry 4.0 N Tvenge, K Martinsen Procedia manufacturing 23, 261-266, 2018 | 146 | 2018 |
| Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms I Baturynska, K Martinsen Journal of Intelligent Manufacturing 32 (1), 179-200, 2021 | 98 | 2021 |
| Monitoring and control for thermoplastics injection molding a review O Ogorodnyk, K Martinsen Procedia Cirp 67, 380-385, 2018 | 84 | 2018 |
| Vectorial tolerancing for all types of surfaces K Martinsen International Design Engineering Technical Conferences and Computers and …, 1993 | 74 | 1993 |
| Combining learning factories and ICT-based situated learning N Tvenge, K Martinsen, SSVK Kolla Procedia CIRP 54, 101-106, 2016 | 46 | 2016 |
| Application of machine learning methods for prediction of parts quality in thermoplastics injection molding O Ogorodnyk, OV Lyngstad, M Larsen, K Wang, K Martinsen Advanced Manufacturing and Automation VIII 8, 237-244, 2019 | 42 | 2019 |
| Towards increased intelligence and automatic improvement in industrial vision systems O Semeniuta, S Dransfeld, K Martinsen, P Falkman Procedia cirp 67, 256-261, 2018 | 40 | 2018 |
| Added value of a virtual approach to simulation-based learning in a manufacturing learning factory N Tvenge, O Ogorodnyk, NP Østbø, K Martinsen Procedia CIRP 88, 36-41, 2020 | 39 | 2020 |
| Beyond lean and six sigma; cross-collaborative improvement of tolerances and process variations-a case study L Krogstie, K Martinsen Procedia Cirp 7, 610-615, 2013 | 39 | 2013 |
| Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms T Ghosh, K Martinsen Engineering Science and Technology, an International Journal 23 (3), 650-663, 2020 | 30 | 2020 |
| Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance D Bhattacharjee, T Ghosh, P Bhola, K Martinsen, PK Dan Energy 183, 235-248, 2019 | 26 | 2019 |
| Evolutionary algorithms in additive manufacturing systems: Discussion of future prospects TS Leirmo, K Martinsen Procedia CIRP 81, 671-676, 2019 | 22 | 2019 |
| CDIO design education collaboration using 3D-desktop printers T Haavi, N Tvenge, K Martinsen Procedia CIRP 70, 325-330, 2018 | 21 | 2018 |
| Human-machine interface for artificial neural network based machine tool process monitoring K Martinsen, J Downey, I Baturynska Procedia CIRP 41, 933-938, 2016 | 21 | 2016 |
| Application of feature selection methods for defining critical parameters in thermoplastics injection molding O Ogorodnyk, OV Lyngstad, M Larsen, K Martinsen Procedia CIRP 81, 110-114, 2019 | 19 | 2019 |
| A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105 T Ghosh, Y Wang, K Martinsen, K Wang The International Journal of Advanced Manufacturing Technology 111, 2419-2439, 2020 | 17 | 2020 |
| Use of post-consumer scrap in aluminium wrought alloy structural components for the transportation sector K Martinsen, S Gulbrandsen-Dahl Procedia CIRP 29, 686-691, 2015 | 17 | 2015 |
