Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterised by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data.

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With new technologies (e.g. IoT, big data) embraced in smart manufacturing, smart facilities focus on creating manufacturing intelligence that can have a positive impact across the entire organisation. The manufacturing today is experiencing an unprecedented increase in available sensory data comprised of different formats, semantics, and structures. Sensory data was collected from different aspects across the manufacturing enterprise, including product line, manufacturing equipment, manufacturing process, labour activity, and environmental conditions. Data modelling and analysis are the essential part of smart manufacturing to handling increased high volume data, as well as supporting real-time data processing

From sensory data to manufacturing intelligence, deep learning has attracted much attention as a breakthrough of computational intelligence. By mining knowledge from aggregated data, deep learning techniques play a key role in automatically learning from data, identifying patterns, and making decisions. Different levels of data analytics can be produced including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics aims to summarise what happens by capturing the product’s conditions, environment and operational parameters. When the product performance is reduced or the equipment failure happens, diagnostic analytics examine the root cause and report the reason it happens. Predictive analytics utilises statistical models to make predictions about the possibility of future production or equipment degradation with available historical data. Prescriptive analytics goes beyond by recommending one or more courses of action. Measures can be identified to improve production outcomes or correct the problems, showing the likely outcome of each decision.

With the advanced analytics provided by deep learning, manufacturing is transformed into highly optimised smart facilities. The benefits include reducing operating costs, keeping up with changing consumer demand, improving productivity and reducing downtime, gaining better visibility and extracting more value from the operations for globally competitiveness.


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Applications to smart manufacturing

Computational intelligence is an essential part of smart manufacturing to enable accurate insights for better decision making. Machine learning has been widely investigated in different stages of manufacturing lifecycle covering concept, design, evaluation, production, operation, and sustainment. The applications of data mining in manufacturing engineering are reviewed in, covering different categories of production processes, operations, fault detection, maintenance, decision support, and product quality improvement. The evolution and future of manufacturing are reviewed in, emphasising the importance of data modelling and analysis in manufacturing intelligence. The application schemes of machine learning in manufacturing are identified as summarised in. Smart manufacturing also requires prognostics and health management (PHM) capabilities to meet the current and future needs for efficient and reconfigurable production.

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  • How does smart manufacturing help companies?
  • How will smart manufacturing evolve in the future?