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[Abnormal Sound Inspection Case] Onkyo

[Abnormal Sound Inspection Case] Onkyo

Quantifying abnormal sound inspections relying on the ears of skilled inspectors using smartphones and AI! A case study that achieved standardization of inspection quality.

We would like to introduce a case study on the quantification of abnormal sound inspection at Onkyo. Traditionally, subjective sensory inspections have relied solely on the experience of the inspectors, leading to challenges in skill transfer, sharing of judgment criteria, and standardization of inspection quality. By using a smartphone and microphone to collect and store operational sounds and abnormal noises from the products under inspection, we conducted analysis and abnormal sound judgment using AI. We established an abnormal sound judgment flow utilizing data from normal and abnormal sounds, and by combining sound data with the judgments of skilled inspectors, we built an inspection system that does not rely solely on the experience of the inspectors. The transition to data-driven inspections has achieved standardization of inspection quality and improved quality traceability. [Challenges] ■ Subjective sensory inspections relied on the ears of skilled inspectors. ■ There was a need to establish an inspection system that does not depend solely on the experience of the inspectors and to facilitate skill transfer. *For more details, please refer to the related links or feel free to contact us.

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[Engineering Audio AI Judgment Case] Onkyo Corporation

[Engineering Audio AI Judgment Case] Onkyo Corporation

Automatic determination of work completion and abnormalities using 'engineering sound'! A case study on reducing defects in the production lines of precision equipment and home appliances.

We will introduce a case where automatic judgment of work completion and abnormalities was achieved through process sound analysis, leading to a reduction in defects. During the launch of mass production lines for new products and the transfer of production to overseas factories, variability in judgment and the occurrence of defects within the process were significant challenges. By implementing a system that analyzes sounds during the process using AI, we were able to utilize AI judgment for event sounds such as parts fitting and screw tightening completion as a mistake-proofing measure. This stabilized work quality, reduced variability in judgment, contributed to defect reduction, and improved work efficiency and quality on the production line through standardized rules. [Challenges] - Variability in judgment and occurrence of defects within the process - Promotion of standardization during the launch of mass production lines for new products and the transfer of production to overseas factories *For more details, please refer to the related links or feel free to contact us.

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  • Other inspection equipment and devices
  • Sound Anomaly Detection System

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