Smart manufacturing - Revision history

Admin at 16:11, 28 October 2026

2026-10-28T16:11:30Z

Admin at 16:11, 28 October 2026

2026-10-28T16:11:10Z

Admin: /* TEAM MEMBERS */

2026-10-05T19:31:11Z

‎TEAM MEMBERS

Chathurangi: /* Project-6:CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing */

2025-12-18T00:11:06Z

‎Project-6:CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing

Chathurangi: /* Project-6:CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing */

2025-12-18T00:10:25Z

‎Project-6:CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing

Chathurangi: /* Seamless Coordination and AI Safety */

2025-12-18T00:10:10Z

‎Seamless Coordination and AI Safety

Chathurangi: /* Project-6:CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing */

2025-12-18T00:10:00Z

‎Project-6:CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing

Chathurangi: /* Project-8:AI Empowered Personalized Education */

2025-12-18T00:05:31Z

‎Project-8:AI Empowered Personalized Education

Chathurangi: /* Project-7:Explaining the root cause of an event through causal knowledge */

2025-12-18T00:05:23Z

‎Project-7:Explaining the root cause of an event through causal knowledge

Chathurangi: /* Project-6:Process Knowledge Workflow using PDDL */

2025-12-18T00:05:00Z

‎Project-6:Process Knowledge Workflow using PDDL

← Older revision		Revision as of 16:11, 28 October 2026
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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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← Older revision		Revision as of 16:11, 28 October 2026
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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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		+	= DISSERTATIONS =
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		+	== Chathurangi Shyalika ==
		+	* PhD Dissertation: [https://drive.google.com/file/d/1IYgWFyVkbIAp9Bt_bs1x5P9saOYR1h3s/view Neurosymbolic AI for Multi-Agent AI for Robust Intelligent, Trustworthy Industrial Systems]
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		+	<iframe src="https://drive.google.com/file/d/1IYgWFyVkbIAp9Bt_bs1x5P9saOYR1h3s/view" frameborder="0" width="600" height="375" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
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 #[https://www.linkedin.com/in/kaushik-roy-b8a323ab/ Kaushik Roy]
 #[https://www.linkedin.com/in/renjithprasadk/ Renjith Prasad]
 '''External Collaborators:'''

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← Older revision		Revision as of 00:10, 18 December 2025
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	<iframe src="https://docs.google.com/presentation/d/e/2PACX-1vT3bsHSuk6eV23UdPk3Xhu86VSCub26Ayr9wsI0CgLiKoam-9Mwepwdv1p35x9nxw/pubembed?start=false&loop=false&delayms=3000" frameborder="0" width="3178" height="4522" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>		<iframe src="https://docs.google.com/presentation/d/e/2PACX-1vT3bsHSuk6eV23UdPk3Xhu86VSCub26Ayr9wsI0CgLiKoam-9Mwepwdv1p35x9nxw/pubembed?start=false&loop=false&delayms=3000" frameborder="0" width="3178" height="4522" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
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	== '''Project-7:'''Process Knowledge Workflow using PDDL ==		== '''Project-7:'''Process Knowledge Workflow using PDDL ==

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 <iframe src="https://docs.google.com/presentation/d/e/2PACX-1vSBU9h7w3ompjVh_l5OWsoJ6D2PhhlxVGZ6GJ3YRb7SuULMHgA1aHTqjF91hPAy39gwJnQIAzxCH46Z/pubembed?start=false&loop=false&delayms=3000" frameborder="0" width="960" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
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 == '''Project-6:'''CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing==

← Older revision		Revision as of 00:10, 18 December 2025
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	The work presents a comprehensive evaluation of CausalTrace using an academic rocket assembly dataset from the Future Factories Lab. Results show strong performance in root cause identification—achieving MAP@3 of 94%, Precision@2 of 97%, MRR of 0.92, and Jaccard similarity of 0.92. CausalTrace also significantly outperforms correlation-based baselines and ontology-ablated variants, demonstrating the decisive role of neurosymbolic grounding. In human and LLM-based evaluations using the C3AN framework (Robustness, Intelligence, Trustworthiness), CausalTrace received an overall rating of 4.59/5, indicating high reliability and interpretability.		The work presents a comprehensive evaluation of CausalTrace using an academic rocket assembly dataset from the Future Factories Lab. Results show strong performance in root cause identification—achieving MAP@3 of 94%, Precision@2 of 97%, MRR of 0.92, and Jaccard similarity of 0.92. CausalTrace also significantly outperforms correlation-based baselines and ontology-ablated variants, demonstrating the decisive role of neurosymbolic grounding. In human and LLM-based evaluations using the C3AN framework (Robustness, Intelligence, Trustworthiness), CausalTrace received an overall rating of 4.59/5, indicating high reliability and interpretability.
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	== '''Project-7:'''Process Knowledge Workflow using PDDL ==		== '''Project-7:'''Process Knowledge Workflow using PDDL ==

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-== '''Project-8:'''AI Empowered Personalized Education ==
+== '''Project-9:'''AI Empowered Personalized Education ==
 We have surveyed the latest AI technologies for personalized education and have identified synergies with some active projects on using AI for education at AIISC. There is growing use of KGs along with statistical AI (i.e., neural network based) techniques for EduTech solutions using AI for Education. One use of KG is for capturing domain knowledge. For example, the EduTech company Embibe.com has developed comprehensive KG for all subjects and courses it supports. Another use of KG is for creating personalized KG to capture the history of student’s interaction with an AI based education application (e.g., what are all the topics students has learned and how well so that the AI based application can guide the next learning objective and help teachers and the students with personalized assessment). At AIISC we are using AI for education through gaming (e.g., teach strategies for solving Rubrics cube) and support analogy-based pedagogy to improve learning about complex topics with the aid of analogy with more familiar concepts. We identify Dynamic KG to be another interesting application for personalized education. Specifically, the use of a Dynamic KG would allow us to keep the course materials up to date with the latest developments while catering to the emerging demands of students’ learning goals.

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-== '''Project-7:'''Explaining the root cause of an event through causal knowledge ==
+== '''Project-8:'''Explaining the root cause of an event through causal knowledge ==
 Our goal is to utilize the process knowledge workflow to construct a sequential causal Bayesian network (Figure 4) that captures both the input-output sequence of the process and the agent information. Specifically, we aim to create a model that can track the cause-and-effect relationships between the input commands, robot actions, and pipeline outputs in the assembly process. By doing so, we will be able to predict the impact of a given input/output and action combination on the assembly pipeline. If an anomaly occurs, we will use the causal Bayesian network to identify the possible root cause and explain the event.

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 The work presents a comprehensive evaluation of CausalTrace using an academic rocket assembly dataset from the Future Factories Lab. Results show strong performance in root cause identification—achieving MAP@3 of 94%, Precision@2 of 97%, MRR of 0.92, and Jaccard similarity of 0.92. CausalTrace also significantly outperforms correlation-based baselines and ontology-ablated variants, demonstrating the decisive role of neurosymbolic grounding. In human and LLM-based evaluations using the C3AN framework (Robustness, Intelligence, Trustworthiness), CausalTrace received an overall rating of 4.59/5, indicating high reliability and interpretability.
-== '''Project-6:'''Process Knowledge Workflow using PDDL ==
+== '''Project-7:'''Process Knowledge Workflow using PDDL ==
 We proposed Process Knowledge Workflow (PKW) using Planning Domain Definition Language (PDDL) to detect anomalies in the assembly line (Figure 2&3). Specifically, the PKW uses multimodal data from robot sensors, conveyor belt sensors and camera to detect anomalies. The images from the camera will be processed using state-of-the-art object detection techniques to identify the objects of interest and the interactions among them. The data from the sensors will be utilized to detect the movement of the desired objects. Using this information, PKW will be constructed for the expected process. Then the PKW can serve as an orchestrator to identify anomalies in the sensor values. Based on the sensor values, the PKW will also have the ability to define and name the anomalies. We conducted an extensive survey of the list of sensors in the assembly line from neXt Future Factories lab and studied the type and range of data being generated. Using this, the first version of the PKW ontology will be constructed. A demonstration of PKW can be found in Figure 2.