Advances in CAD/CAM/CAE Technologies

• Autore: Kyratsis Panagiotis
• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (116 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: adaptive neuro-fuzzy inference system; Al6082-T6; analysis of variance (ANOVA); ball burnishing; CAD teaching; CNC machining; computer-aided manufacturing (CAM); cutting torque; disk to disk test; drilling; fatigue; finite element analysis; friction; friction behavior; gear reducer housings; graphite; Grey analysis; induction hardening; licensed CAD; mechanical post-treatment; milling; molybdenum disulfide; multi-layer perceptron; multiple regression; numerical simulation; open-source CAD software; pattern design; polyester coating; power transmission; radial basis function neural network; radial impeller; solid lubricants particles; SOLIDWORKS; surface topography; Taguchi method; teaching/learning strategies; thrust force; topology management optimization; wear
• Persona (resp. second.): MarkopoulosAngelos P; KakoulisKonstantinos
• Sommario/riassunto: CAD/CAM/CAE technologies find more and more applications in today's industries, e.g., in the automotive, aerospace, and naval sectors. These technologies increase the productivity of engineers and researchers to a great extent, while at the same time allowing their research activities to achieve higher levels of performance. A number of difficult-to-perform design and manufacturing processes can be simulated using more methodologies available, i.e., experimental work combined with statistical tools (regression analysis, analysis of variance, Taguchi methodology, deep learning), finite element analysis applied early enough at the design cycle, CAD-based tools for design optimizations, CAM-based tools for machining optimizations.
• Titolo autorizzato: Advances in CAD
• ISBN: 3-03928-741-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910404078703321
• Lo trovi qui: Univ. Federico II