Design of Products
Product Design Optimization: Generate alternative design variations for remanufacturable products.

Virtual prototyping & Testing for products.

Defect Identification

Defect Classification:
  - Robust defect classification system capable of identifying various defects, such as cracks, corrosion, and wear.

Anomaly Detection:
  - Uncover subtle defects with our anomaly detection algorithms, ensuring a comprehensive inspection of automotive cores viz a viz standard cores and parts.

Multiple Defect Identification:
  - Identify and categorize multiple defects within a single image, providing a thorough assessment of the core's condition.

Multi-modal data collaboration
 - Variety of data from images to videos, text specifications, reports, etc., for easy  and error-free defect identification

Adaptive Learning:
  - Continuous learning algorithms adapt and improve accuracy over time, learning from new data and evolving defect scenarios.

Synthetic Data Generation with GANs:
   - Utilize GANs for synthetic data generation, augmenting the training dataset and improving model generalization.

Data Augmentation with GANs:
   - Enhance training data with GANs, creating variations of defect images for improved model robustness.

Addressing Limited Data Issues:
   - Overcome limited labeled data challenges with GANs, generating additional synthetic data to enhance training.

Domain Adaptation with GANs:
   - Handle variations in defect appearance with GANs, ensuring adaptability to different manufacturing processes or materials.

GAN-based Super-Resolution:
   - Improve image quality using GAN-based super-resolution techniques, particularly beneficial for low-resolution or degraded images.

Unsupervised Anomaly Detection with GANs:
   - Employ GANs for unsupervised anomaly detection, identifying defects without relying on predefined categories.

Adversarial Training for Robustness:
   - Enhance model robustness against input variations with GANs through adversarial training.