Architettura della soluzione: Quality control for manufacturing processes

Without a manufacturing-control system that’s capable of identifying slowdowns or potential failures to improve the overall process, manufacturing companies can lose money and productivity on scrap and rework. Plus, wide-scale recalls can shake consumer confidence, further affecting your bottom line.

This solution introduces a quality-control process that helps predict failures in manufacturing pipelines (assembly lines), so your company can produce more while wasting less and saving money. It uses test systems that are already in place and failure data, specifically looking at returns and functional failures at the end of an assembly line. By combining these with domain knowledge and root-cause analysis within a modular design that encapsulates main processing steps, it provides an advanced-analytics solution that uses machine learning to predict failures before they happen.

Catching future failures early allows for less expensive repairs or even discarding, which are usually more cost efficient than going through recall and warranty cost.

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Quality control for manufacturing processes Diagram showing six connected icons. On the far left is the icon for ALS test measurements, which gathers the telemetry data. Connected by a one-way arrow to the right is Event Hubs, which logs the data. Further to the right and also connected by a one-way arrow is Stream Analytics, which processes the data. Above that, connected by a mutual arrow, is Machine Learning, which makes predictions based on the data. Below Stream Analytics, connected by a one-way arrow, is Azure SQL Data Warehouse, which stores the data. To the far right, connected by one-way arrows from both Azure SQL Data Warehouse and Stream Analytics, is Power BI, which visualizes the data in an interactive dashboard. Azure SQL DW Machine Learning(Real time predictions) Power BI ALS test measurements (Telemetry) Event Hub Stram Analytics(Real time analytics) Dashboard of predictions/alerts Realtime data stats, Anomaliesand aggregates Realtime event and predictions

Linee guida di implementazione

Prodotti Documentazione

Analisi di flusso

Stream Analytics provides near real-time analytics on the input stream from the Azure Event Hub. Input data is filtered and passed to a Machine Learning endpoint, finally sending the results to the Power BI dashboard.

Hub eventi

Event Hubs ingests raw assembly-line data and passes it on to Stream Analytics.

Machine Learning

Machine Learning predicts potential failures based on real-time assembly-line data from Stream Analytics.

SQL Data Warehouse

SQL Data Warehouse stores assembly-line data along with failure predictions.

Power BI

Power BI visualizes real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.

Architetture delle soluzioni correlate

Anomaly detection with machine learning

Microsoft Azure’s IT Anomaly Insights can help automate and scale anomaly detection for IT departments to quickly detect and fix issues.

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