Special Issue on “Machining Dynamics and Parameters Process Optimization”
Source abstract
Abstract
In 1907, F.W Taylor—the father of production engineering—exposed the fundamentals of modern machining and defined chatter as the most obscure and delicate of all problems facing the machinist [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Advanced machining processes and optimization · Manufacturing Process and Optimization · Engineering Technology and Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, University of the Basque Country, Alameda de Urquijo s/n, 48013 Bilbao, Bizkaia, Spain
Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Eugenio Garza Sada 2501, Monterrey, Nuevo León 64849, Mexico
References (10)
Show source-provided references
1. Alvarez, J., Zatarain, M., Barrenetxea, D., Marquinez, J.I., and Izquierdo, B. (2020). Implicit Subspace Iteration to Improve the Stability Analysis in Grinding Processes. Appl. Sci., 10.10.3390/app10228203 ↗
2. Sosa, J.L., Olvera-Trejo, D., Urbikain, G., Martinez-Romero, O., Elías-Zúñiga, A., and Lacalle, L.N.L. (2020). Uncharted Stable Peninsula for Multivariable Milling Tools by High-Order Homotopy Perturbation Method. Appl. Sci., 10.10.3390/app10217869 ↗
3. Sun, J., Zhang, W., and Dong, X. (2020). Natural Frequency Prediction Method for 6R Machining Industrial Robot. Appl. Sci., 10.10.3390/app10228138 ↗
4. Puma-Araujo, S.D., Olvera-Trejo, D., Martínez-Romero, O., Urbikain, G., Elías-Zúñiga, A., and López de Lacalle, L.N. (2020). Semi-Active Magnetorheological Damper Device for Chatter Mitigation during Milling of Thin-Floor Components. Appl. Sci., 10.10.3390/app10155313 ↗
5. Gao, X., Zhang, S., Qiu, L., Liu, X., Wang, Z., and Wang, Y. (2020). Double B-Spline Curve-Fitting and Synchronization-Integrated Feedrate Scheduling Method for Five-Axis Linear-Segment Toolpath. Appl. Sci., 10.10.3390/app10093158 ↗
6. Yu, B.-F., and Chen, J.-S. (2020). Development of an Analyzing and Tuning Methodology for the CNC Parameters Based on Machining Performance. Appl. Sci., 10.10.3390/app10082702 ↗
7. Fang, Y., Wang, L., Yang, J., and Li, J. (2020). An Accurate and Efficient Approach to Calculating the Wheel Location and Orientation for CNC Flute-Grinding. Appl. Sci., 10.10.3390/app10124223 ↗
8. Grossi, N., Scippa, A., Venturini, G., and Campatelli, G. (2020). Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts. Appl. Sci., 10.10.3390/app10217575 ↗
9. Mamledesai, H., Soriano, M.A., and Ahmad, R. (2020). A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning. Appl. Sci., 10.10.3390/app10207298 ↗
10. Zhang, X., Han, C., Luo, M., and Zhang, D. (2020). Tool Wear Monitoring for Complex Part Milling Based on Deep Learning. Appl. Sci., 10.10.3390/app10196916 ↗