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manufacturing analytics is the use of operations and events data and technologies to ensure quality, increase performance and yield, and reduce costs. manufacturing analytics is particularly important for the process manufacturing industry including semiconductor, chemistry, energy production, and biopharmaceutics.

manufacturing analytics is a part of industry 4.0, and is closely related to ai and machine learning, iot, digitization, ar/vr, electrification, clean energy, and other new technologies. matlab^® enables data engineers and process engineers to develop defect detection and advanced process control algorithms, and deploy them into applications for industrial systems.

using matlab for manufacturing analytics

detect defects in manufacturing analytics using visual inspection system based on deep learning technologies and private data from industrial camera, sem, x-ray images, and other sources. matlab can help engineers with the whole workflow including data preparation, ai modeling, and deployment.

access operation and test data via databases (sql, nosql) in manufacturing analytics, specific file format (stdf), or industrial iot communication system (opc) from manufacturing equipment. you can also connect to cloud data using matlab cloud interfaces to popular services like amazon s3, azure data lake, and google storage. 

apply machine learning or multiobjective optimization technology in manufacturing analytics to multiple-variate data to implement advanced process control, monitor the process, predict drift and default, identify root causes, and optimize manufacturing recipes. you can choose from the most popular classification, clustering, and regression algorithms using interactive apps, such as classification and regression learner apps. automate the process of building optimized machine learning models using technology includes feature selection, model selection, and hyperparameter tuning.

deploy data analytics functions to manufacturing production systems on embedded edge hardware or. mathworks helps it and engineering work together to deliver tangible business results by using your chosen it infrastructure without recoding into other languages.

a digital twin model helps overcome typical manufacturing analytics difficulties: expensive hardware test costs, difficulty to obtain failure data, time alignment between many sensors, and complex design space. digital twin models can include physics-based approaches using simscape™, statistical data-driven approaches, or ai-based approaches. the models reflect the operating asset’s current environment, age, and configuration, which typically involves direct streaming of asset data into tuning algorithms.

for more information about machine learning with matlab, see statistics and machine learning toolbox™.