Process curves are multivariate finite time series data coming from manufacturing processes. This paper studies machine learning that detect drifts in process curve datasets. A theoretic framework to synthetically generate process curves in a controlled way is introduced in order to benchmark...

We report on the evaluation of a beam stabilization system mounted on a six-axis articulated ultrashort pulsed laser robot system for real 3D micromachining. The system integrates a discrete beam guiding system along the robot links and an optical scanner mounted on the final robot axis,...

This study presents a comprehensive evaluation of initial sampling techniques within the context of Bayesian Optimization (BO), a machine learning technique intended for the optimization of intricate and expensive functions. We assessed its efficacy in optimizing both theoretical benchmark...

Research indicates the effectiveness of machine learning for condition monitoring in sheet metal shearing. However, existing studies primarily focused on model accuracy while neglecting model explainability. In consequence, potential biases and novel insights captured by the models remained...

In semiconductor wafer dicing, one particular area of interest is the process of visual inspection to detect manufacturing defects that occur throughout the manufacturing process. Emerging defect patterns are typically in the micrometer range, translating to barely visible defects in pixel size...

Deep learning-based classification models show high potential for automating optical quality monitoring tasks. However, their performance strongly depends on the availability of comprehensive training datasets. If changes in the manufacturing process or the environment lead to defect patterns...

Dislocation density-based models offer a physically grounded approach to modeling strain hardening in metal forming. Since these models are typically defined by Ordinary Differential Equations (ODEs), their accuracy is constrained by both, the model formulation and the parameter identification...

Semiconductor manufacturing systems are highly complex due to intricate processes and material flows. Operating these systems efficiently remains a significant challenge, particularly under the growing demands for operational excellence and cost reduction. Current approaches often rely on...

Improving the accuracy of sheet metal localization in industrial machines is of great interest to many automated manufacturing systems. Current vision-based systems typically rely on traditional image processing algorithms to locate the position of sheets in images. However, these algorithms...

The consistently growing demand for robust automated Photonic Integrated Circuits assembly, testing and packaging, is increasingly oriented towards high volume and continuously sets newer challenges to overcome concerning throughput and cost effectiveness. Production processes’ intrinsic...