Singapore Institute of Manufacturing Technology (SIMTech), A*STAR – Engineering / Analytics Intern

Engineering and/or Analytics Intern

We are looking for highly motivated interns to contribute to cutting-edge research in our additive manufacturing (AM) department. Interns can choose from multiple project topics depending on their interests and skills, including engineering, data analytics, software, and automation. These projects vary throughout the year depending on our ongoing needs. The current topics are:

Available Internship Topics
1. Predicting Printed Parts’ Quality from Monitoring Data
2. Metal Matrix Composites Fabrication using Laser Powder Bed Fusion
3. Mechatronics and Sensor Integration for Metal 3D Printing
4. Software, Data Analytics, or Automation for Advanced Manufacturing

1. Predicting Printed Parts’ Quality from Monitoring Data (supervisor: WP)

Current research on quality control of 3D printed parts is mainly from an experimental aspect, therefore, is slow and costly. We will focus on building a relationship between the monitored data and the porosity of printed part. Therefore, design of experiments and their printing, high-resolution X-ray CT, image analysis and detection algorithm, and self-supervised deep learning strategy will be adopted to realize the target. The aim of this project is to finally give a cost-effective and fast solution for the qualification of printed parts for better industrial adoption.

 * The student should intend to explore new knowledge. Experience in metallurgy and/or 3D printing and/or machine learning would be a plus point(s).
* The student also allow the use high-performance computing resource in the National supercomputing center (NSCC) in Singapore.

2. Metal Matrix Composites Fabrication using Laser Powder Bed Fusion (supervisor: LDN)

We are seeking a motivated and meticulous student to lead a project focusing on exploring the potential of using laser powder bed fusion (LPBF) technology to fabricate metal parts reinforced with hard ceramics. The unique set of material properties enables metal matrix composites (MMCs) to be used in many specialized and high-performance applications such as advanced tooling, brake discs, turbine blades, etc. This project aims to identify the optimum compositions of the composite and the possible mechanisms behind the enhanced properties.

Learning Outcomes for Students
* An understanding of LPBF technology and its applications.
* Expertise in materials science and/or mechanical engineering.
* Experience in designing and executing experiments.
* Experience in using material characterisation and testing techniques.
* Proficiency in data collection, analysis, and interpretation.
* Effective communication and collaboration within a research team.
* Presentation and reporting skills to convey research findings.

Roles and Responsibilities of Student
* Literature Review: Conduct a thorough review of existing research and developments in the field of metal matrix composite. Identify key trends, challenges, and opportunities.
* Experimental Setup: Plan and set up experiments to print MMCs parts using LPBF equipment, including ceramic compositional percentage, printing preparation, and process optimisation.
* Part Preparation and Testing: Prepare printed parts in a suitable manner for mechanical testing.
* Data Collection: Collecting, compiling, and comparing data for each type of printed MMCs.
* Microstructural Analysis: Analyse the interaction between the ceramics and the metal matrix and identify the mechanisms for any observed mechanical property enhancements.
* Data Analysis: Analyse the data collected during experiments, identify trends and insights, and use these to guide recommendations for further development.
* Reporting: Document your research findings comprehensively and create presentations and reports for the research team and stakeholders.

Job Description for Student
The student will be given opportunities to explore and study the entire workflow of the LPBF process such as powder feedstock preparation and reuse, pre and post-printing processes, printed samples preparation and characterisation, and collecting, analysing, and presenting data.

3. Mechatronics and Sensor Integration for Metal 3D Printing (supervisor: ZJT)

We are looking for highly-motivated and competent interns with a strong background with digital skills to help with the continued digitalisation of our R&D processes in our additive manufacturing department.

The intern will be responsible for developing sensor/IoT devices that will provide visibility into various scientific and operational blind spots in our department. These devices will feed data into an existing larger digital framework that can accelerate R&D efforts by improving the efficiency of our scientists and engineers. In parallel, this data-rich digital approach to R&D will be showcased to local and international industry partners as part of A*STAR's mission to drive innovation.

Various job scopes are available depending on the skill sets of the intern and whether he/she wishes to contribute towards options of R&D, logistic functions, or business analytics. These can be in areas of app development, PCB/electronics design, system integration, etc. Candidates should have good hands-on & digital skills and a strong problem solving mindset.

4. Software, Data Analytics, or Automation for Advanced Manufacturing (supervisor: ZJT)

The intern will be involved for example in building robust scripts that can run 24/7 beyond months. These tools will be built upon our existing modern codebase and network infrastructure. Opportunities are also available to enhance the resilience and scope of our network architecture. A typical project will for example involve adding to our microservice architecture by providing monitoring or analytics APIs that other future services can call upon.

Specific roles can be chosen by interns based on their skills and interests in areas such as frontend development, data/image/sensor analytics, database management, automation, security, or DevOps. Familiarity with technologies like Python, Flask, C++, React.js, Git, Docker, various SQL databases, and AI tools is essential.

The intern will be tasked with assisting in developing technologies that complement our core engineering expertise, working within our existing digital framework to deploy new projects using a microservice architecture. The goal is to provide senior scientists with tools to expedite insights acquisition. This ongoing digital transformation is also designed to serve as a model for local engineering firms, demonstrating how modern digital tools can enhance engineering capabilities.

Interns will gain practical experience in additive manufacturing, data-driven R&D, process optimisation, software and analytics for manufacturing, and materials characterisation depending on the chosen topic.