AI-Powered Automated Image Analysis for Materials Manufacturing and Research

Struggling to extract reliable, quantitative data from complex images?

Advances in artificial intelligence and machine learning are transforming the way images are analysed in materials research and manufacturing. Features such as grains, particles, phases and layer thickness can provide critical information about material performance and manufacturing quality, yet traditional image analysis methods are often manual, time-consuming and difficult to apply to complex, low-contrast or ambiguous images.

In this webinar, MIPAR Image Analysis will explore how AI-powered image analysis can automate the detection, segmentation and measurement of features, enabling researchers and engineers to move from images to accurate, reproducible and statistically robust data more quickly.

Through real-world examples, discover practical workflows for grain, particle and phase analysis, layer thickness and dendrite arm spacing measurement, as well as automated critical dimension and pass/fail analysis of welds and gears.

The webinar will also introduce modern AI capabilities such as zero-shot analysis, which can identify features without requiring users to build and train models from scratch, alongside automated annotation tools that can significantly reduce the manual effort involved in deep learning model development.

See how automated batch processing and reporting can eliminate operator error, save time and make image-based analysis more scalable and consistent, with built-in templates helping to streamline the creation of reports.

What You Will Learn

During this webinar, you will discover:

• Scalable workflows for automated grain, particle, and phase analysis, layer thickness and dendrite spacing measurement, critical dimension and pass/fail analysis of welds and gears
• How zero-shot AI tools can detect features without requiring training on every new application
• How automated annotation can dramatically reduce the manual effort required to train deep learning models
• How to extract reliable, quantitative data from complex, noisy, or low-contrast images
• How automated batch processing and reporting can save time, reduce variation, and deliver more consistent results
• How real-world applications demonstrate the ability of MIPAR to turn raw image data into fast, quantitative, and actionable results

Who Should Attend?

This webinar is ideal for:

• Materials researchers and scientists using microscopy and image analysis to characterise materials
• R&D professionals investigating material structure, phases, particles or microstructural features
• Manufacturing and quality professionals looking to automate inspection and measurement workflows
• Welding and inspection professionals undertaking image-based assessment and critical dimension analysis
• Engineers and technicians seeking to improve the speed, consistency and reproducibility of image analysis
• Anyone interested in applying AI and machine learning to materials characterisation and image-based measurement