Autonomous driving paper index

Smart Manufacturing: Integrating IoT and AI for Learner Operations

2026-08-14 · Journal of Intelligent Decision Making and Information Science

autonomous driving

One-line summary

This paper proposes an integrated AI-IoT framework for smart manufacturing that continuously acquires machine data, performs real-time analytics, predicts equipment failures, optimizes production scheduling, and supports data-driven decision-making.

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充:本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Smart manufacturing is transforming industrial production through the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), enabling intelligent decision-making, predictive maintenance, real-time monitoring, and autonomous process optimization. Conventional lean manufacturing techniques primarily rely on human expertise and periodic inspections, limiting their ability to respond dynamically to changing production environments. The convergence of IoT-enabled sensing technologies with AI-driven analytics introduces a new generation of intelligent lean operations capable of minimizing waste, improving productivity, reducing operational costs, and enhancing overall equipment effectiveness (OEE). This paper proposes an integrated AI-IoT framework for smart manufacturing that continuously acquires machine data, performs real-time analytics, predicts equipment failures, optimizes production scheduling, and supports data-driven decision-making. The proposed architecture employs interconnected sensors, edge computing, cloud analytics, machine learning models, and digital dashboards to improve operational efficiency while maintaining product quality and resource sustainability. Performance evaluation demonstrates improvements in production throughput, energy efficiency, machine utilization, defect reduction, predictive maintenance accuracy, and manufacturing flexibility compared with conventional manufacturing systems. The proposed framework provides an intelligent, scalable, and sustainable solution aligned with Industry 4.0 principles and offers practical guidance for implementing AI-enabled lean manufacturing across modern industrial enterprises.

5.0Engineering value
7.0Research novelty
6.0Business relevance

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