E-Book, Englisch, 124 Seiten
Nedelcu Advanced Engineering Forum Vol. 16
Erscheinungsjahr 2016
ISBN: 978-3-0357-3067-8
Verlag: Trans Tech Publications
Format: PDF
Kopierschutz: 0 - No protection
E-Book, Englisch, 124 Seiten
ISBN: 978-3-0357-3067-8
Verlag: Trans Tech Publications
Format: PDF
Kopierschutz: 0 - No protection
We are glad to present the next 16th volume of journal "Advanced Engineering Forum". In this volume are collected articles which describe the results of engineering solutions of actual problems in applied materials, processing technologies, researching and designing of parts of modern machines mechanisms. Published articles will be useful for professionals from field of mechanical engineering, students and academic teachers.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde
- Technische Wissenschaften Technik Allgemein Technik: Allgemeines
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik
Weitere Infos & Material
Analysis of Mechanical Properties on Roselle Fibre with Polymer Matrix Reinforced Composite
Modelling and Optimization of Tool Chip Interface Temperature and Surface Roughness in CNC Dry Turning of EN 24 Steel
An Experimental Investigation to Optimize Multi-Response Characteristics of Ni-Hard Material Using Hot Machining
Finite Element Method and Experimental Investigation of Hot Turning of Inconel 718
Elements and Materials Improve the FDM Products: A Review
Relation between Cantabro Loss and Surface Abrasion Resistance of Fly Ash Roller Compacted Concrete (FRCC)
Evaluation of Permanent Deformation of BRA Modified Asphalt Paving Mixtures Based on Dynamic Creep Test Analysis
Exit Geometry Modifications of Double Volute Centrifugal Pump for Vane Passing Frequency Vibration Resolution: A Case Study
Investigation of Variation in the Performance of an Electro Thermal Thruster with Aerospike Nozzle
Financial Predictions Using Cost Sensitive Neural Networks for Multi-Class Learning