Imagine this: a tiny flaw buried deep within a metal component, invisible to the human eye and missed by routine inspections. Months later, that flaw turns into a product failure, a costly recall, or a safety incident.

For manufacturers, quality is where engineering meets reputation and every defect carries a price tag. Get it right, and you unlock efficiency, customer trust, and long-term profit. Get it wrong, and the fallout can be catastrophic.

The Hard Math of Quality

Studies show the Cost of Poor Quality (COPQ) in automotive and aerospace manufacturing can range from 5% to 30% of gross sales. A single undetected defect can lead to production downtime, safety incidents, regulatory penalties, customer dissatisfaction and Brand erosion. Device Tech: The True Cost of Poor Quality

Case Study 1: Cracked Steel Axles

In 2019, Ford recalled over 200,000 vehicles due to cracked steel rear axles. The defect originated from microscopic internal inclusions in the forged metal, invisible to the naked eye and missed during conventional inspections. Traditional ultrasonic testing was too slow for high-speed production lines, meaning only a few samples were checked, allowing defective parts to slip through.  Cars.com Report on Ford Axle Recall

Case Study 2: Recurring Recalls in Rapid Growth

Between 2023 and 2024, Tesla faced multiple large-scale recalls affecting hundreds of thousands of vehicles. While some were software-related, others stemmed from material and assembly flaws, including cracked steering knuckles, loose bolts, and weld defects. These cases highlight the urgent need for faster, smarter defect detection across modern production systems.  Car and Driver: Tesla Recall Summary

Case Study 3: Jet Engine Recall

In 2024, Pratt & Whitney launched an emergency recall of aircraft engines after finding microscopic cracks in turbine disks. The root cause? Contaminated metal powders that compromised material integrity. In industries where precision is life-critical, even microscopic defects can have multimillion-dollar consequences. CT Insider: Jet Engine Recall Report

The Core Challenge

Traditional inspection technologies, like ultrasonic, X-ray, and visual methods, often fall short due to sampling instead of 100% inspection, slow or manual testing processes, operator variability and difficulty detecting internal or micro-structural defects. Manufacturers need an inspection method that’s fast, non-contact, and capable of hearing what others can’t see.

How RESONIKS Redefines Non-Destructive Testing

At RESONIKS, we combine AI-driven acoustic resonance testing with machine learning to revolutionise industrial quality control.

Here’s how it works. Each component is gently tapped or vibrated. Its unique acoustic signature is captured using precision microphones. AI algorithms compare the resonance data against reference models to instantly detect cracks, porosity, welding flaws, or material inconsistencies. This process delivers 100% inspection coverage, real-time detection, and zero operator bias, all at production-line speed.

Some global OEMs have already validated RESONIKS’ technology for its speed, scalability, and precision. At RESONIKS, we listen where others look, detecting flaws before they become failures and turning every vibration into a voice for quality.