Alan Tan: Ethical AI in Automotive Manufacturing
The ethics of AI depend on the intent of the humans applying it. What does this mean for automotive manufacturing applications?
AI & automotive insights
The ethics of AI depend on the intent of the humans applying it. What does this mean for automotive manufacturing applications?
Detecting anomalies can help automotive manufacturers improve critical metrics like OEE, scrap rates, and first time through yield (FTT)
Today’s manufacturers are leveraging smart Industry 4.0 technologies, such as artificial intelligence and machine learning, the Industrial Internet of Things, cloud and edge computing, virtual reality and augmented reality.
When analyzing msnufacturing data, anomaly detection is the process of identifying and observing rare items, events, patterns, and outliers that differ significantly from a dataset’s normal behavior.
Statistical process control is a long-standing method of manufacturing data analysis. Can it be improved by machine learning?
The global Industry 4.0 market is expected to grow from US$65 billion in 2021 to US$166 billion by 2026, reflecting a healthy CAGR of 20.6%.
Accurate predictions can have a huge impact on manufacturing. They can help precision manufacturers increase throughput, reduce machine downtime, improve product quality, develop data-driven action
A machine learning (ML) model is created by running training data through a number of different methods or algorithms so they can learn to handle
Digital transformation (DX) may seem daunting, but 95% of manufacturers agree it is essential to their future success. DX is about using new technologies to
Many automotive part manufacturers use data from the production floor to guide decision-making. They often use statistical process control (SPC) and end-of-line (EOL) testing to
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