|Title:||Big data analysis of multi-parameters of continuous casting process|
|Subject:||Computer science, Embedded systems, Robotics|
ABB AB/Metallurgy is world leading in developing and delivering electromagnetic devices for steel and aluminum industry. The electromagnetic devices are employed to control the fluid flow and solidification of liquid metal, and as a result, improve the process productivity and quality. ABB has long experience in working with industrial partners in collecting the industrial data with the purpose to evaluate and improve its product performance.
The main content of the thesis work is to apply new methods in Big Data analysis/machine learning to analyze a data set which is collected from industrial application, and as a further step, to build a predictive model. The data set covers the operation data, sensor data and final product quality data of slab continuous casting process.
The detailed description of the work is as follows
- Introduction to ABB AB/Metallurgy products and slab continuous casting process
- Test of Big Data/machine learning methods for the analysis of the extracted data set
- Comparison among different analysis methods
- Build-up of a predictive model
|Prerequisites:||Driven M.Sc. student with solid background in mathematics, numerical analysis, statistics and data science, socially skilled to collaborate with colleagues in the analysis of big data. Structured and analytical person well trained in describing and quantifying parameter relationships. Experience in artificial intelligence, big data analysis methods and machine learning. Good programming skills in Matlab, Python etc. Technical University educational programs: Computer Science, Embedded systems, Engineering Physics, Material Science or similar.|
|IDT supervisors:||Ning Xiong|
|Company contact:||ABB Metallurgy Hongliang Yang, R&D team Leader, Hongliang.email@example.com, 070 380 9610|