What Is Manufacturing Analytics?
Using MATLAB for Manufacturing Analytics
Detect defects in manufacturing analytics using visual inspection systems based on deep learning technologies and private data from industrial cameras, SEM, X-ray images, and other sources. MATLAB can help engineers with the whole workflow including data preparation, AI modeling, and deployment.
Classifying defects on wafer maps using deep learning in manufacturing analytics.
Access operation and test data via databases (SQL, NoSQL) in manufacturing analytics, specific file format (STDF), or industrial IoT communication systems (OPC) from manufacturing equipment. You can also connect to cloud data using MATLAB Cloud interfaces to popular services such as Amazon S3™, Azure® Data Lake, and Google Cloud Storage™.
Access big data in manufacturing analytics.
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 AutoML technology includes feature selection, model selection, and hyperparameter tuning.
Classification Learner app, in Statistics and Machine Learning Toolbox, guides you through the classification process while minimizing the coding required to create models.
Deploy data analytics functions to manufacturing production systems on embedded edge hardware or enterprise IT systems. MathWorks helps IT and engineering work together to deliver tangible business results by using your chosen IT infrastructure without recoding into other languages.
Cloud (enterprise), edge, and embedded manufacturing analytics applications.
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.
Modeling methods for manufacturing analytics digital twins: physicals-based, data-driven, AI-based.
For more information about machine learning with MATLAB, see Statistics and Machine Learning Toolbox™.
Examples and How To
- Classify Defects on Wafer Maps Using Deep Learning – Example
- Optimization of Shared Resources in a Batch Production Process – Example
- Improve an Engine Cooling Fan Using Design for Six Sigma Techniques – Example
- Chemical Process Fault Detection Using Deep Learning – Example
- GSK Uses Historical Data to Improve Manufacturing Processes and Make Better Products – Example
- 8 Steps for Analyzing Manufacturing Data for Better AI Outcomes – White Paper
Videos
- Building Diagnostic Models in the Processing Industries with Machine Learning (51:50) – Video
- Digital Twins for Smart Manufacturing (36:49) – Video
- Fault Detection and Diagnosis in Chemical and Petrochemical Processes (3 videos) – Video Series
- Simulate Biopharmaceutical Manufacturing for Improved Plant Performance (40:10) – Video
- Process Control with Reinforcement Learning (15:34) – Video
Software Reference
pca– Principal component analysis of raw data – Functioncontrolchart– display measurements of process samples over time – Function