AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes (2008.12987v1)

Abstract: Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

• The paper proposes a novel AI-based methodology integrating Genetic Algorithms and Neural Networks for intelligent feature selection and evaluation in smart manufacturing processes.
• The hybrid GA-NN model uses a binary GA for dynamic feature selection and a multilayer perceptron for evaluation, improving process control and predictive capabilities.
• Experimental results show the proposed model achieves over 93% accuracy, significantly outperforming conventional methods like PCA and Lasso Regression, proving its practical effectiveness.

AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes

The paper "AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes" presents a comprehensive approach toward optimizing semiconductor manufacturing processes using advanced AI techniques. The authors, Mohammadhossein Ghahramani et al., propose a dynamic algorithm leveraging Evolutionary Computing and Deep Learning to enhance the efficiency and intelligence of these processes.

The crux of the paper lies in the deployment of a Genetic Algorithm (GA) coupled with a Neural Network (NN) to facilitate intelligent feature selection. This hybrid model aims to address key challenges inherent in semiconductor manufacturing, such as cost reduction, quality enhancement, and sustainable production practices. By employing this AI-based methodology, the authors attempt not only to control manufacturing processes more effectively but also to enable manufacturers to access robust predictive technologies.

For robust feature selection, the authors utilize a binary Genetic Algorithm to dynamically identify optimal features, thereby minimizing redundancies and maximizing relevance to the target feature. The resulting selected features are fed into a multilayer perceptron, which serves as the function for evaluating objectives, particularly minimizing classification error rates. The GA is adeptly integrated to adaptively control the exploration and exploitation phases, mitigating premature convergence through strategic adjustments in crossover operations.

The methodology set forth in the paper confirms the superiority of combining ML, in the form of neural networks, with evolutionary algorithms over traditional methods. Through experimental validation, the authors demonstrate that their proposed model outperforms conventional techniques, such as Principal Component Analysis (PCA) and Lasso Regression, thereby affirming the practicality of their approach in real-world applications.

In terms of numerical results, the accuracy of the proposed model surpasses 93%, a stark improvement over the performance of conventional methods, which typically range from 60% to 80%. The meticulous handling of imbalanced data classification issues, employing techniques such as density-based SMOTE, further strengthens the robustness of the AI-driven approach toward feature selection.

The implications of this research are multifaceted. Practically, it promises substantial enhancements in the efficiency and cost-effectiveness of semiconductor manufacturing. Theoretically, it paves the way for future applications using AI and ML algorithms to drive further advancements in smart manufacturing. The framework laid out in this paper is scalable and could potentially be adapted for similar industrial applications, underscoring its relevance to ongoing developments in AI.

Looking forward, the authors propose expanding the model to explore other Multiobjective Evolutionary Algorithms (MOEA) that may offer simultaneous optimization of various feature selection objectives. This future direction is poised to explore the intricacies of evolving manufacturing analytics and broaden the spectrum of AI applications in this domain.

In conclusion, the paper sets a strong precedent for AI-driven approaches in manufacturing, offering a detailed examination of how dynamic modeling and data-driven evaluation can contribute to smarter and more sustainable production processes. It serves as a valuable resource for researchers and industry professionals seeking to integrate AI advancements into manufacturing strategies.