QUALITY CONTROL
Making audio actionable:
Quality Control for the optimization of production up-time and product quality.
Can you hear repeating failure in your production line or process facility? What about the production equipment you use? If you can tell in an instant when something is off in the sound signature, where the sound is a key indicator of faults that might not meet the standards of the finished product, we can digitalize and automate that human sense and experience – even in extremely noisy environments.
We have developed and delivered numerous machine learning models for fault detection. Combining these models with our equipment that listens beyond the noise and zooms into the components we are most interested in, you can listen for specific fault indicators. This enables an automatic quality control procedure without exposing staff to possible hazardous environments. Additionally this can remove the individual bias of quality control staff. Together, these capabilities ensure as much up-time as possible, a more continuous production, less unscheduled down-time, and fewer ruined finished products and batches.
Striving for perfection? Elevate your quality control procedures by adding sound measurement sensors and machine learning to your routines.
How it works
Audio deviations are a common sign of equipment, product, and process failure, yet these deviations are hard to detect in highly noise-filled areas typically found in production facilities. Our arrays can listen beyond the noise to a specific part of a machinery. We can automatically record examples of a specific fault signature as well as normal operating sounds, label the data correctly, and use the labeled data to train a machine learning model. The trained model will then automatically trigger on the specific fault signature, while ignoring normal operating sounds, and alarm the PLC or control system. In this way, we combine sound measurement sensors and machine learning to automate the quality control procedure.
USE CASE SCENARIOS
Acoustic blister detector during aluminium extrusion
In metal forming processes like extrusion, rolling, and pressing, the physical processes themselves are repetitive. Due to the nature of the process and the material, some faults and errors may be induced in the end product. These faults sometimes emit a characteristic sound source signature that can be hard or even impossible to detect manually or automatically by sensors like optical or infrared. In addition, the manufacturing process is often subject to harsh environments with a lot of ambient noise, heat, and dust.
Given the repetitive nature of the process, the sound source characteristic of the emitted fault signature is often repetitive as well. This means that the combination of harsh environments and the nature of the fault signature fits excellently for our ruggedized microphone arrays and our wide range of pre-trained machine learning tools.
R&D sound testing during product development
During research and development of a new product there are certain noise criteria that must be met. When combining individual components to a final product, the overall emitted noise spectrum may be unexpected or even unwanted. In addition, running the product at different speeds may produce different audio patterns at different frequencies, all of which need to be characterized. During this development process it’s important to pin-point what components are responsible for which emitted noise, and where they are located.
Using Squarehead’s intuitive analytics software, you can either be on site or sit remotely and do a full sound quality analysis of the product under inspection, both live and in post-processing. With our state-of-the-art superhearing ability, and color plotting of individual sound sources, pinpointing the source you’re looking for is a breeze.
Fault detection in serial production
A standard routine before shipment of rotating equipment such as pumps, fans and motors are various quality checks. One of the checks to pass is that the emitted sound signature doesn’t deviate from a predefined golden sample. This is often based on manual inspection and operator experience.
Squarehead’s microphone arrays and advanced signal processing techniques automates and standardizes this quality assurance check to ensure that the results are the same and correct every time, even in noisy environments.
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