✦ LIBER ✦

Feature signature prediction of a boring process using neural network modeling with confidence bounds

✍ Scribed by Gang Yu; Hai Qiu; Dragan Djurdjanovic; Jay Lee

Publisher: Springer
Year: 2005
Tongue: English
Weight: 316 KB
Volume: 30
Category: Article
ISSN: 0268-3768
DOI: 10.1007/s00170-005-0114-x

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Artificial neural network modeling of we

Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals

✍ Sukhomay Pal; Surjya K. Pal; Arun K. Samantaray 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 646 KB

This paper addresses the weld joint strength monitoring in pulsed metal inert gas welding (PMIGW) process. Response surface methodology is applied to perform welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six p

The use of design of experiments to impr

The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process

✍ F. Sánchez Lasheras; J.A. Vilán Vilán; P.J. García Nieto; J.J. del Coz Díaz 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 374 KB

The hard chromium plating process aims at creating a coating of hard and wear-resistant chromium with a thickness of some micrometres directly on the metal part without the insertion of copper or nickel layers. Chromium plating features high levels of hardness and resistance to wear and it is due to