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

arXiv: https://arxiv.org/abs/2008.12987 · HTML: https://mlsanity.com/paper/2008.12987/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis](https://mlsanity.com/paper/2104.09876/) (2021)
- [Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification](https://mlsanity.com/paper/2102.06029/) (2021)
- [IoT-based Route Recommendation for an Intelligent Waste Management System](https://mlsanity.com/paper/2201.00180/) (2022)
- [A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems](https://mlsanity.com/paper/2512.05971/) (2025)
- [A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering](https://mlsanity.com/paper/2209.06429/) (2022)
- [From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making](https://mlsanity.com/paper/2504.04244/) (2025)
- [CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing](https://mlsanity.com/paper/2603.06361/) (2026)
- [Reputation Bootstrapping for Composite Services using CP-nets](https://mlsanity.com/paper/2105.15135/) (2021)
- [Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling](https://mlsanity.com/paper/2607.18515/) (2026)

## Abstract (arXiv)

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.