##### CASE STUDY

## Manufacturing

As precision parts manufacturers are continually embracing new ways and methods to squeeze out last mile efficiency gains that will positively impact EBITDA as well as building a set of AI brain libraries that are fully battle tested for enterprise specific use cases.

Mindtrace’s Brain-Sense™ solution is not only fulfilling performance thresholds set by the enterprises, but also exceeding expectations in terms of accuracy, consistency, and scalability.

**Challenges**

- 1. Bottleneck in manufacturing throughput – slow finished parts inspection process
- 2. Associated labor costs
- 3. Unreliable operator quality inspection process
- 4. Lack of audit trails on pass versus fail inspection results

### Duration - 3 months from start to finish

**Mindtrace Weld AI Brains Deployed**

### Objectives

1. Above **95% defect detection accuracy**  
2. **Consistency in detection** across multiple QC lines  
3. **24x7 Availability** – eliminate defect detection variability  
4. Reduction in inspection cycle time – **up to 80% speed up**

### Duration - 3 months from start to finish

**Mindtrace Weld AI Brains Deployed**

### Objectives

## Outcome

By leveraging Mindtrace’s few-short learning capabilities, the solution can surpass the defect detection accuracy of human operators with around 10 samples, while classical AI often requires thousands to reach that accuracy benchmark.

**AI Brains Used**

1

##### Classification

Object identification & classification.

1

##### Detection

Identifies defect types, severity & location.

1

##### Analytics

Generates insights & recommendations.

1
F1 score achieved using proprietary AI & ML

1%
Reduction in time to inspect parts for defects

1
Average # of images with defects present needed to match human QA inspector performance