Streamlining the quality assessment process for quartz glass production, using a hybrid solution of signal processing and machine learning, increasing efficiency and accuracy in identifying the correct zero-order line.
noun /ˈbreɪk.θruː/
Reduced repetitive testing, saving time and energy by determining quartz glass quality in one attempt.
Refined data gathering process, ensuring quality labeled data for the algorithm.
Combined signal processing and machine learning, respecting existing research and enhancing the identification of the correct zero-order line.
Heraeus, a globally renowned technology company specializing in special metals, sensors, and quartz glass, faced challenges in assessing the quality of their quartz glass. Utilizing a test called the Zero-order test was a complex and time-consuming process, sometimes requiring up to seven repetitions before confirming the glass quality. Heraeus sought a solution to determine the quality of their quartz glass efficiently and accurately in a single attempt.
minutes of working time saved per measurement
Addressing the complex issue required the input data to be in the right format. We assisted Heraeus stakeholders in labeling images and revamping their data collection process to ensure high-quality, well-labeled data suitable for the algorithm. By developing a hybrid solution combining signal processing and machine learning, we respected existing research and successfully enhanced the process of identifying the correct zero-order line, revolutionizing quartz glass quality assessment.
However here are a few common pain points that we often see, which can be solved through our programs and will lead to an AI breakthrough.
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