Page 68 - IJAMD-2-2

International Journal of AI for
Materials and Design
A unified industrial AI foundation framework

generalization refers to the model’s ability to maintain 4.4. Industrial AI methodology platform
accurate predictions when exposed to previously unseen The industrial AI methodology platform provides
data or tasks. They are essential to ensure that models structured approaches, architectures, and guiding
remain robust, reliable, and effective in real-world industrial principles to ensure AI applications are systematically
settings. Several key techniques support model adaptation integrated into industrial systems. This platform includes
and generalization. Transfer learning and domain multiple well-established methodologies that shape AI
adaptation allow models trained in one context or domain integration in industrial settings. Examples include:
to be fine-tuned or adjusted for use in another, reducing (i) ILKM: ILKM bridges LLMs with domain-specific
the need for large amounts of newly labeled data. 57,58 industrial knowledge to support reasoning, explanation,
Federated learning enables decentralized model training and contextual decision-making in industrial AI; (ii) 5C
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across multiple industrial sites, allowing collaborative CPS: CPS provides a structured approach for integrating
improvement of models while preserving data privacy and AI with industrial systems through five levels (connection,
security. In addition, few-shot and zero-shot learning conversion, cyber, cognition, and configuration), enabled
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techniques allow models to make accurate predictions by operation technology, analytic technology, data
in new domains with limited (few-shot) or no (zero- technology, and platform technology; (iii) stream-of-
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shot) task-specific training data. 60,61 These are especially quality and stream-of-X: This shows a paradigm that defines
valuable when collecting and labeling new industrial a structured methodology for continuous monitoring,
data is time-consuming or expensive. Online learning optimization, and decision-making in industrial systems,
allows models to continuously update as new data streams particularly for multi-stage manufacturing processes; and
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become available, maintaining relevance and accuracy
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in real-time applications. Likewise, continual learning (iv) digital twin: It integrates data, simulation, and services
techniques enable models to incrementally incorporate to create a virtual representation of physical entities,
achieving industrial intelligence by utilizing AI, IoT, and
knowledge from new data without catastrophic forgetting ML techniques. 19
of previously learned information, supporting long-term
model evolution in dynamic environments. 63 While existing methodologies have played an important
role in advancing industrial AI, their implementation
4.3.4 Model optimization and deployment readiness can be further enhanced by the proposed industrial AI

Model optimization is essential for improving the foundation framework. Rather than redefining the core
performance, efficiency, and reliability of AI models methodologies, the knowledge, data, and model modules
in industrial applications. It involves tuning model offer comprehensive and systematic views that support
parameters, selecting appropriate architectures, and both existing and emerging industrial AI approaches. By
improving computational efficiency. Common techniques promoting more structured development processes, they
include hyperparameter optimization methods, such as ensure that the design, implementation, and deployment
grid search, random search, and Bayesian optimization, of industrial AI solutions are more effective, reliable,
which systematically explore the parameter space to find and scalable. A concrete example is using ILKM to
optimal configurations. In addition to hyperparameter enable intelligent question-answering (QA) systems for
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tuning, model optimization also addresses structural maintenance personnel, who frequently need to query
design and computational efficiency to meet industrial complex maintenance manuals and troubleshooting guides
requirements. Neural architecture search (NAS) methods, during equipment servicing. The knowledge module
including differentiable NAS and evolutionary algorithms, enables the structured extraction of key procedures, fault
can automate the discovery of architectures that achieve descriptions, and parameter settings from maintenance
optimal trade-offs between accuracy, latency, and memory manuals and technical documentation. This content is
consumption. Model compression techniques, such as organized into knowledge graphs and indexed repositories
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pruning (magnitude-based or structured pruning), weight that can be efficiently queried by LLMs. The data module
quantization, and low-rank factorization, are commonly ensures that domain-specific datasets – including historical
used to reduce inference time and deployment costs maintenance logs and annotated QA pairs – are properly
without significant loss of performance. Knowledge documented, versioned, and linked to the knowledge base.
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distillation further enhances deployment readiness by Standardized metadata (e.g., machine type, operating
transferring knowledge from large teacher models to conditions, and fault categories) allows the LLM to retrieve
smaller student models. Furthermore, multi-condition relevant answers aligned with specific equipment and
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testing could be performed to ensure that models perform scenarios. The model module guides the fine-tuning of
reliably across different operating scenarios. 68 LLMs on domain-specific QA datasets. It also defines best

Volume 2 Issue 2 (2025) 62 doi: 10.36922/IJAMD025080006