What is AI Noise Inspection?
AI noise inspection is an image-based inspection method that leverages deep learning technology.
Specifically, it converts extracted sound data into images and automatically optimizes the system so that defective areas are flagged as NG.
AI automatically captures the features of defective areas to build inspection models, achieving high accuracy in inspection.
The "3 Objectives" of AI Noise Inspection
Noise inspection is a critical process that verifies whether product quality conforms to specifications and defined standards.
- Quality Assurance
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In manufacturing, it is essential to provide "quality assurance to consumers" and, as a supplier, to "comply with the quality standards of customers."
If defective products reach consumers, the risks go beyond functionality and safety — they can lead to recalls and reputational damage, causing serious business losses.
There is also a risk of escalating into serious issues such as customer complaints or suspension of business relationships. AI noise inspection therefore checks whether products meet quality standards, playing a critically important role. - Quality Maintenance
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Continuously checking for inconsistencies in product quality is essential from the perspective of maintaining quality and ensuring stable supply.
Conducting noise inspection with consistent standards leads to stable quality maintenance. - Quality Improvement
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While product inspection is essential, it is equally important to improve manufacturing processes and equipment to prevent defects from occurring in the first place.
Through noise inspection, identifying and addressing the root causes of defects becomes the fundamental objective.
Strengths of AI Noise Inspection
Standardization of Inspection Criteria
AI (deep learning) can learn from "human sensory judgments" and "subtle defects detected by skilled inspectors," enabling high-accuracy inspection under unified standards.
Inspection Automation Based on Expert Inspector Standards
In conventional noise inspection systems, operators manually set thresholds on an individual basis, leading to widespread inconsistency in inspection standards across lines and operators. AI noise inspection, by contrast, recognizes the characteristics of detection targets for stable inspection, eliminating the need for frequent threshold adjustments and enabling "standardization of inspection criteria," as well as "reduction of adjustment workload."
AI Noise Inspection vs.
Conventional Noise Inspection Methods
Sensory Inspection
This method involves generating sound from the inspection target and judging its condition from that sound.
While skilled technicians can achieve high accuracy, inspections based on human senses tend to have ambiguous criteria and results that vary depending on each inspector's skill and physical condition.
This makes missed detections and false positives common, posing a challenge to ensuring consistent quality.
With an AI noise inspection system, the model learns from historical data to build a system that automatically detects defects based on consistent criteria.
As a result, missed detections due to human error and ambiguous judgments on difficult-to-inspect products can be detected accurately.
Inspection Examples with AI Noise Inspection
Finished Product InspectionAutomobiles, vessels, etc.
Analyzes the sounds produced when parts or products are in operation to classify them as good or defective.
Predictive Maintenance of Production EquipmentFactory machinery, etc.
Monitors time-series data from production equipment to detect early signs of failure and prevent unexpected downtime.
Monitoring of Production Equipment and FacilitiesFactory machinery, etc.
The primary purpose is to confirm that equipment is operating stably. Continuous monitoring for accident prevention in chemical plants is also possible.
Structural InspectionBridges, utility poles,
tunnel concrete, etc.
Records and analyzes the sounds generated by impact to inspect structures for abnormalities such as loosening or deterioration.
AI Noise Inspection Case Studies
Do any of these challenges sound familiar?
- Missed detections occur in sensory inspection and you want to standardize criteria and improve accuracy
- Recruiting and training personnel takes too long, so you are pursuing workforce reduction
- Conventional waveform analysis requires significant effort to define rules, and there are limits to its accuracy Feel Free to Contact Us
Two Implementation Patterns
Automation of Sensory Inspection
Challenges of Sensory Inspection
- Inconsistent inspection standards
- Inspection time limits production volume
- Difficulty hiring inspectors / high training costs
workforce reduction
Improving Accuracy of Existing Inspection Systems
Improving Accuracy of Existing Inspection Systems
- Frequent false judgments increase operational and disposal costs
- Existing inspection systems cannot detect certain types of defects
- Daily threshold adjustments cause inconsistent inspection standards
your existing system to achieve
higher inspection accuracy
Free Verification Testing Before Implementation
To ensure even first-time AI adopters can explore the technology with confidence, we offer free initial testing and report preparation. Testing is conducted using the latest algorithms, with comparison across multiple models.
On-Site Assessment
Reviewing imaging conditions suited to the on-site environment
Work Test
Product imaging and AI inspection model verification
Report Preparation
Preparing a report and explaining the verification results
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