๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Statistical process control applied to gas metal arc welding

โœ Scribed by Gary P. Maul; Richard Richardson; Brett Jones

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
339 KB
Volume
31
Category
Article
ISSN
0360-8352

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Multi-variable control of robotic gas me
Multi-variable control of robotic gas metal arc welding
โœ J.P. Huissoon; D.L. Strauss; J.N. Rempel; S. Bedi; H.W. Kerr ๐Ÿ“‚ Article ๐Ÿ“… 1994 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 577 KB

The current generation of robotic gas metal arc welding systems tend to separate control of the robot from control of the weld process, although the two are intimately related. This paper presents some results of the research of the authors into multi-variable control of an integrated robotic weldin

A neural network applied to pattern reco
A neural network applied to pattern recognition in statistical process control
โœ A.S. Anagun ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 363 KB

In processes, the causes of variations may be categorized as chance (unassignable) causes and special (assignable) causes. The variations due to chance causes are inevitable, and difficult to detect and identify. On the other hand, the variations due to special causes prevent the process being a sta

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
โœ Sukhomay Pal; Surjya K. Pal; Arun K. Samantaray ๐Ÿ“‚ Article ๐Ÿ“… 2008 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 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