Hands-on AI for the human-centred factory of Industry 5.0.
Industry 5.0: Hands-on AI in Manufacturing moves beyond the automation focus of Industry 4.0 to its human-centred successor. You learn what distinguishes Industry 5.0 β collaboration between people and intelligent systems, mass personalisation, and sustainability and resilience as first-class goals β and get hands-on with the AI that enables it: collaborative robots (cobots), adaptive quality and process control, and human-in-the-loop decision systems. The course connects technology to the values reshaping manufacturing. You finish able to apply AI within a human-centred, sustainable production vision. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
This hands-on course covers Industry 5.0 and AI in manufacturing β human-AI collaboration, cobots, mass personalisation and sustainable, resilient production.
1. Explain how Industry 5.0 differs from Industry 4.0.
2. Design human-AI collaboration and cobot workflows.
3. Apply AI to adaptive quality and process control.
4. Support mass personalisation with data.
5. Build resilience and sustainability into production.
β’ Manufacturing and automation engineers
β’ Industrial data scientists
β’ Operations and transformation leaders
β’ Students of advanced manufacturing
β’ A hands-on grasp of Industry 5.0 AI.
β’ A human-centred manufacturing perspective.
β’ An applied production-AI project.
β’ A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
β’ Human-centricity, resilience and sustainability as the three stated pillars
β’ Automation that augments an operator rather than replacing them
β’ Where the label is genuinely new and where it is rebranding
β’ Cobot operating modes and the safety requirements of ISO/TS 15066
β’ Speed and separation monitoring, power and force limiting in practice
β’ Task allocation between human and machine on capability, not cost alone
β’ Vision-based quality inspection and the defect classes with too few examples
β’ Operator assistance: guidance, augmented reality and adaptive instructions
β’ Explainability, because an operator will not act on an unexplained alert
β’ Batch-size-one production and the changeover cost it demands solving
β’ Reconfigurable lines, digital thread and traceability per unit
β’ Scheduling and planning under high product variety
β’ Energy and material monitoring, and emissions accounting on the line
β’ Supply chain disruption, redundancy and the cost of resilience
β’ Workforce skills, acceptance and change management as the real constraint
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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