Abstract
Investigators publish new report on Machine Learning. According to news reporting out of Opole, Poland, by NewsRx editors, research stated, “Tool wear is intimately related to intelligent operation and maintenance of automated production, workpiece surface quality, dimension accuracy, and tool life. Therefore, it is necessary to improve the production efficiency and quality by predicting tool wear values.” Financial support for this research came from Opole University of Technology. Our news journalists obtained a quote from the research from the Opole University of Technology, “The purpose of this research is to use machine learning and image processing methods in estimating the tool wear values when turning AA7075 aluminum alloy. In addition, the indepth analysis of cutting tools at different parameters were examined with SEM and EDS analysis. The results shown that there was a 44.40% increase in tool wear when the cutting speed was raised by 100% while feed rates were maintained at the same level. On the other hand, there was a 22.78% increase in tool wear seen when the cutting speed was maintained at the same level.”
NETL
NSTL
Elsevier