Micro-credential: Digital printing and automation
Part of pathway / module: Color management in eco-design / Innovative textile printing
EQF level: 6
ECTS credits: 1
Learning methods and duration:
- Lectures – 5 hours; Exercises/Laboratory works Individual study/ Project Works (seminar) – 17 hours; video experience – 2 hours; final exam – 1 hours. Total: 25 hours
Assessment methods:
- Initial assesment (quiz): to check the starting level in AI/automation and basic digital printing workflow.
- Formative assessment: Short online quizzes on predictive maintenance, pattern recognition, robotics. Cheking the familiarity with the terms like digital twin, cobot, closed-loo control, and similar. Some group works and activities (online or hybrid) can be included.
- Summative assessment: mini project + small final quiz: the description of the current state, integration of AI tools, integration of automation and robotics, analysis of benefits and limitation (expected improvments in productivity, cost efficiency, reduction of reprints, and similar). The deliverables is written report.
General objective: The learner will be introduced to:
- Recent advancements in AI and automation, transforming the digital textile printing, with focus on improved efficiency, quality and sustainability.
- Automatized colour matching, AI-based pattern defect detection, AI upscaling of images for printing and maintenance of digital printing equipment.
- Automation solutions in and after the digital printing process, such as robotic system for fabric handling and automated post-printing woekflow, so they can asses their impact on productivity, cost efficiency and sustainable production
Enrollment:
Timing:
Responsible institution: University of Zagreb (Croatia)
Contact person, email:
Learning Outcomes
Knowledge
After the completion of this module, learners will:
• Understand the current state and main trends in AI and automation in digital textile printing, and their impact on efficiency, quality and sustainability
• Know the basic principles of machine learning, deep learning and neural networks as they relate to colour matching, defect detection and predictive maintenance in printing.
• Understand future directions of AI and automation in digital printing
Skills
After the completion of this module, learners will be able to:
• Apply an AI-powered tools for automatized colour matching and explain where they are integrated in the digital printing workflow
• Upscale an image for printing using AI, evaluate the visual result (sharpness, artefacts, colour changes) and judge its suitability for production.
• Analyse the role of automation in post-printing peocesses (faixation, cutting, folding, sorting, packaging) and identify where automation yields the greatest benefits
Competences
After the completion of this module, learners will:
• Propose and explain AI and automation solutions for given digital textile printing scenario, linking them to improvements in quality, productivity and sustainability
• Communicate and collaborate with digital designers, technologists and automation specialists when planning or optimising AI-enhanced digital printing workflows within innovative textile printing environments.
• Critically assess opportunities and limitations of AI and automation in digital printing (technical, economic, organisational), supporting informed decisions in industrial or SME contexts.
