Turun yliopiston Moodle

Objectives

After having passed the course, the student will have advanced knowledge of current technologies of additive manufacturing (AM, 3D printing) and their role in manufacturing industries, know fundamentally product design in AM, be able to comprehensively compare different AM processes with conventional manufacturing and select suitable processes for different applications. Student will also learn fundamental phenomena behind AM technologies, be able to understand simulation and optimization tools used in AM, know AM material properties, understand deeply post processing of AM and also gather deep knowledge of process control of AM. Students are also familiar with cost evaluation, business aspects and market potential of AM. Students will learn how industrial cases are executed and which factor affect them.

Embark on a journey into Machine Learning in Digital Manufacturing, where data-driven strategies converge with advanced manufacturing technologies. This course provides MSc students with insight into the expanding role of ML in process monitoring and optimization across diverse domains, including Additive Manufacturing (AM), 3D printing, laser-assisted manufacturing processes, and surface engineering.

Upon completion of this course, students will have:

• Explored in-situ process monitoring and sensorization techniques for real-time defect detection.
• Investigated neural network and sensor data fundamentals to elevate manufacturing workflows.
• Examined how ML-driven strategies boost sustainability and data-informed decision-making in industrial contexts

Tavoitteet

Opiskelija tutustuu kestävän kehityksen filosofiseen ja kasvatusfilosofiseen näkökulmaan. Opiskelija tutustuu muutamiin ekologisen kasvatusfilosofian perusteemoihin ja osaa soveltaa filosofista näkökulmaa ekologisiin kysymyksiin.