No abstract

No abstract

Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.

A new family of hetero‐functional polythioethers with alternating hydroxyl and epoxy groups are facilely synthesized via photo‐initiated thiol‐yne click (co)polymerization under mild reaction conditions. Their chemical structures and hetero‐functional group density can be finely tuned by adjusting the feeding mole ratios. As the evidence of nuclear magnetic resonance and gel permeation chromatography tests shows, either the molecular weight or the hetero‐functional group density depend greatly on the SH bond cleavage energy of dithiols which can be predicted by theoretical calculation, using the density functional theory (DFT) with B3LYP hybrid functional and the QCISD methods. This paper provides access to a wide range of new materials with a specific structure and plentiful alternating hetero‐functional groups. Moreover, it unveils and highlights the role of SH bond cleavage energy of dithiols during the process of step‐growth thiol‐yne click (co)polymerization.

No abstract