Data-Driven Framework for Anomaly Identification and Process Optimization in Continuous Casting

Thermal Data-Driven Quality Prediction in Wire Arc Additive Manufacturing via Machine Learning and Anomaly Detection

Maintaining consistent quality control is a significant challenge in Wire Arc Additive Manufacturing (WAAM). However, real-time monitoring remains difficult due to the impracticality of manual labelling of complex, continuous thermal data during production. This paper proposes a framework utilizing temperature data and...

A Review of Steel Surface Defect Detection for Industrial Applications: Evaluation Frameworks, Data Challenges, and Model Mechanisms

Steel surface defect detection is a critical component of industrial quality inspection based on artificial intelligence (AI). In recent years, deep learning has substantially improved the level of automation in this task. However, model performance in real production lines remains jointly affected by evaluation metric...

Timeseries Foundation Model for Anomaly Detection in Manufacturing Sensor Data

A time-series foundation model specifically designed for anomaly detection in large-scale industrial datasets is proposed, incorporating both reconstruction error and embedding vector-based anomaly scores, leveraging the effectiveness of embedding-based anomaly detection in simple time-series data.

Results in Engineering

Prediction of Key State Parameters in Blast Furnace Ironmaking: Mechanism–Data Fusion and Cross-Condition Generalization

Accurate prediction of blast furnace state parameters is complicated by partial observability, heterogeneous sampling frequencies, delayed laboratory measurements, and changes in operating conditions. This critical review synthesizes recent journal and conference literature on the prediction of hot-metal silicon conten...

Unsupervised online anomaly detection in laser powder bed fusion via statistical time-series methods: An experimental assessment and feasibility study

Metal additive manufacturing (AM) processes are susceptible to in-process faults that compromise part quality or interrupt printing. Early defect detection enables process intervention, reducing lead time and material waste. This work presents a statistical time-series framework for online anomaly detection in laser...