Knowledge based process planning using manufacturing big data

In the era of the fourth industrial revolution, dubbed as Industry 4.0, manufacturing resources will be digitised and integrated into a digital ecosystem of manufacturing network. The data generation in such a system will be enormous, and data and analytics are becoming increasingly important to decision making. Manufacturing stores more data than any other sector – close to two exabytes (two quintillion bytes) in 2010. From the big data to useful knowledge to support decision making, manufacturing industry will benefit greatly. Focusing on the big data of Computer Numerical Controller (CNC) machines at the shop floor, this project will investigate the mechanism on knowledge capture from the machining data (part programs) and reuse to support new product development. At present, the data has been isolated at the shop floor and not used for improving operations.

Based on the big data, manufacturing process knowledge can be refined and filtered. A closed knowledge loop can be established from process planning to manufacturing and feedback to process planning [1]. Advanced analytics can be used with historical process data to identify patterns, relationship among process parameters and then optimise the factors that prove to have greatest effect on yield. It will be particularly important in the era of Industry 4.0 to support the decision making in the future cyber-physical manufacturing system. The project will benefit the enterprise to keep the product quality and consistency, reduce the leading time for new products, boost innovation and accumulate knowledge to gain competitive advantages.