Difference between revisions of "Smart manufacturing"
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The utilization of AI in manufacturing processes has ushered in a new era of data-driven optimization and problem-solving. AI is a pivotal cornerstone, especially in Industry 4.0 and 5.0 applications, yielding advanced technologies to guide a new era of heightened efficiency, productivity, and refined decision-making. In this capacity, AI emerges as an indispensable force driving transformative advancements within the manufacturing domain, where data-driven insights and smart automation catalyze innovation and shape the future of industrial processes. The assessment of SoA approaches in the manufacturing realm helped us identify three concrete use cases for further investigation: (1) Rare event prediction, (2) Anomaly detection using process knowledge workflow, and (3) explaining the root cause of an event through causal knowledge. | The utilization of AI in manufacturing processes has ushered in a new era of data-driven optimization and problem-solving. AI is a pivotal cornerstone, especially in Industry 4.0 and 5.0 applications, yielding advanced technologies to guide a new era of heightened efficiency, productivity, and refined decision-making. In this capacity, AI emerges as an indispensable force driving transformative advancements within the manufacturing domain, where data-driven insights and smart automation catalyze innovation and shape the future of industrial processes. The assessment of SoA approaches in the manufacturing realm helped us identify three concrete use cases for further investigation: (1) Rare event prediction, (2) Anomaly detection using process knowledge workflow, and (3) explaining the root cause of an event through causal knowledge. | ||
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| + | = PROJECTS = | ||
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| + | == '''Project-1:''' Explainable Recommendation: A neuro-symbolic food recommendation system with deep learning models and knowledge graphs == | ||
| + | In this work, we propose a food recommendation system (Figure-1) that employs an analyser to recommend whether the food is advisable to the user and a reasonser that provides an explanation for the decisions made by the analyser. The recommendation system harnesses generalization power of deep learning models and abstraction power of the knowledge graphs to analyze recipes. The several knowledge graphs involved in this work are | ||
| + | # Personalized health knowledge graph | ||
| + | # Disease specific knowledge graph | ||
| + | # Nutrition retention knowledge graph | ||
| + | # Cooking effects knowledge graph | ||
Revision as of 19:50, 14 September 2023
The utilization of AI in manufacturing processes has ushered in a new era of data-driven optimization and problem-solving. AI is a pivotal cornerstone, especially in Industry 4.0 and 5.0 applications, yielding advanced technologies to guide a new era of heightened efficiency, productivity, and refined decision-making. In this capacity, AI emerges as an indispensable force driving transformative advancements within the manufacturing domain, where data-driven insights and smart automation catalyze innovation and shape the future of industrial processes. The assessment of SoA approaches in the manufacturing realm helped us identify three concrete use cases for further investigation: (1) Rare event prediction, (2) Anomaly detection using process knowledge workflow, and (3) explaining the root cause of an event through causal knowledge.
PROJECTS
Project-1: Explainable Recommendation: A neuro-symbolic food recommendation system with deep learning models and knowledge graphs
In this work, we propose a food recommendation system (Figure-1) that employs an analyser to recommend whether the food is advisable to the user and a reasonser that provides an explanation for the decisions made by the analyser. The recommendation system harnesses generalization power of deep learning models and abstraction power of the knowledge graphs to analyze recipes. The several knowledge graphs involved in this work are
- Personalized health knowledge graph
- Disease specific knowledge graph
- Nutrition retention knowledge graph
- Cooking effects knowledge graph
