Project Info
Machine Learning to Optimize Additive Manufacturing Parameters
Hua Wang
huawang@mines.edu
Project Goals and Description:
One fundamental theory underpins materials science: process dictates structure and structure dictates properties. However, predicting this process—structure—process or PSP relationship is elusive, because it requires knowledge of the orientation of every grain, the location and distribution of defects, and every processing condition that leads to this structure, down to the smallest fluctuations in temperature, pressure, and composition. From an informatics perspective, this creates an extremely large input space. Weak and redundant variables and variables that cannot be measured convolute important, measurable values. However, machine learning provides a number of robust techniques to extract PSP relationships from these convoluted data streams. Approximately 6000 samples have been printed to characterize the build parameters for 3D printed metal samples. The tested samples connect the microstructure and mechanical properties to laser power, speed, spot size, powder size, shape, and part orientation. These data serve as the basis for the development of machine learning (ML) algorithms — including decision trees, scalable vector regression, and random forest networks —that focus on two-way modeling of process-property and process-structure relationships. Our results show how these parameters affect mechanical performance through microstructure, particularly keyhole and lack-of-fusion porosity defects. This project will focus on the development of a data collection, processing, validation, and distribution framework; on ML performance, accuracy, and validation procedures.
More Information:
Grand Challenge: Reverse-engineer the brain.
Some reference papers are provided below:
https://arxiv.org/pdf/1710.10324.pdf?utm_source=Data-Driven+Materials+Science+Newsletter&utm_campaign=bd68f0f906-EMAIL_CAMPAIGN_2017_11_05&utm_medium=email&utm_term=0_60e6d7fee0-bd68f0f906-297887237
https://arxiv.org/pdf/1711.00118.pdf?utm_source=Data-Driven+Materials+Science+Newsletter&utm_campaign=bd68f0f906-EMAIL_CAMPAIGN_2017_11_05&utm_medium=email&utm_term=0_60e6d7fee0-bd68f0f906-297887237
https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.7b03500
https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.7b02333
https://arxiv.org/pdf/1710.03319.pdf?utm_source=Data-Driven+Materials+Science+Newsletter&utm_campaign=4611562463-EMAIL_CAMPAIGN_2017_11_05&utm_medium=email&utm_term=0_60e6d7fee0-4611562463-297887237
Primary Contacts:
Hua Wang, huawang@mines.edu
Student Preparation
Qualifications
The applicant student is expected to have already taken CSCI 261 and 262. It would be beneficial if the student has already taken CSCI 470.
TIME COMMITMENT (HRS/WK)
4-8 hours/week
SKILLS/TECHNIQUES GAINED
1. The students will learn the skills to perform data processing and management.
2. The students will be involved in my research team to perform research on machine learning and data mining.
3. The students will be involved in scientific paper writing for the results of this project.
4. The students will have a chance to work together with my collaborators in medical schools.
5. The students will gain fundamental knowledge on medical image computing, as well as how to use machine learning, as well as computational algorithms, to deal with problems in medical image computing.
In a word, after the training in this project by successfully completing the assigned research tasks, the student is expected to be ready for pursuing a graduate degree in the area of machine learning, data mining, artificial intelligence, or a broader area of computer science.
MENTORING PLAN
1. One orientation meeting is planned at the beginning of the project, in which the undergraduate students will be introduced to the research team. The project and research culture of the faculty’s research team will be introduced to the undergraduate students in the meeting.
2. Technical seminars within the research team are planned, once per week. In every meeting, the undergraduate students will present a research paper relevant to the project and lead discussions on it with the faculty and the graduate students in the research team.
3. Professional development sessions within the research team are planned, once per week. In every meeting, the faculty or the graduate students in the research team will examine the progress of the project and the recent research results, exchange ideas with the undergraduate students, and help them develop research skills, including algorithm development, experimental design, scientific results evaluation, paper writing, and so on.
4. A poster session will be conducted at the end of the project in which the results of this project will be presented to the research teams of the faculty, the Computer Science Department, and the collaborators of the faculty.
PREFERRED STUDENT STATUS
Sophomore
Junior
Senior