TCM Broach Inspector

General information

Multifunctional system for the automatic inspection of flat broaching tools, featuring:

• 2-stage AI-based image processing system (detection of tooth application surface followed by failure segmentation)
• 3D analysis system with laser profilometer (measurement of tooth height and detection of surface defects)
• Module for cost optimisation of the tool reconditioning process (selection of a regeneration method that maximizes overall profit over the tool's life cycle)
• Dash based interactive application to view results, select the optimum regeneration method and generate a tool scan report
• SQL database for storing the results of the performed scans

Machine learning solutions

• Detectron2 models based on the ResNET architecture
• Supervised learning using custom datasets
• Annotations with artifacts saved in .json format, compatible with LabelMe software
• System for semi-automatic datasets creation
• Jupiter notebooks with scripts for training and evaluation of AI models
• Proprietary evaluation methods with modified F1 metric
• Model comparison and performance tracking using Neptune.AI
• System for AI errors reporting (MLOps)

Technologies used

• Python 3.10
• Detectron2 v0.6
• PyTorch 1.12
• NVIDIA CUDA 11.7

Features

Visualization application

Broach wear map.

Wear analysis for broach rows.

Preview of apposition surface and detected artifacts.

TKinter based host application with console output

ML solutions

MLOPs platform - Neptune AI for experiments tracking pourposes.

Automatic labels in .json lableme compatible format, generated using detectron2 model inference.

Organisations involved in the project development

Wrocław University of Science and Technology	Machine Vision Laboratory	TCM International