AIM-Ready: Quality Assurance
About This Course
This intermediate-level course equips manufacturing professionals to assess and plan the adoption of Artificial Intelligence Applications for quality assurance.
Participants will learn to identify suitable AI-enabled visual inspection use cases, define measurable quality objectives, assess image-data and imaging-system readiness, evaluate AI vision approaches, determine production-system integration requirements, and assess implementation feasibility and business value.
Through manufacturing case studies, structured readiness assessments and project-based learning, participants will develop an individual AI Vision Inspection Implementation Roadmap and Adoption Proposal for a workplace-relevant quality assurance use case.
Advanced AI vision techniques such as object detection, segmentation and anomaly detection are introduced as solution options for evaluation. Participants are not required to develop or train AI models.
No prior coding or AI model-development experience is required. Participants should have a basic understanding of manufacturing, quality assurance, inspection or production processes.
What You'll Learn
- identify suitable AI-enabled quality assurance opportunities and their expected operational and business impact;
- define an AI-ready visual inspection use case with clear defect classes, baselines, targets, KPIs and success criteria;
- assess image-data quality, defect diversity, annotation readiness and data gaps;
- determine camera, optics, lighting and image-acquisition requirements;
- assess AI deployment architecture, production-system integration, data flow and traceability requirements; and
- compare AI vision approaches, evaluate business value and feasibility, and develop an implementation roadmap.
Entry Requirements
Applicants should possess a degree in any discipline or a Diploma with a minimum of 3 years of related working experience.
Applicants who do not have the required academic qualifications are still welcome to apply, but shortlisted candidates may be required to attend an interview for special approval.
Proficiency in written and spoken English.