Manufacturing is vital to a country’s economic growth and ability to innovate. Manufacturing is changing from large monolithic production floors to geographically distributed, internet-connected, medium scale, smart factories. Engineering education needs to be updated in order to train engineers with the needed knowledge and skills.
Manufacturing accounts for a quarter of worldwide employment. Employees of this sector earn higher than the median income of a country.1 Manufacturing contributes up to 20% of Gross Domestic Product, GDP of countries. Moreover it has a multiplier effect on business services jobs. Manufacturing is vital to a country’s economic growth and ability to innovate. Hence countries such as Singapore, China, USA, Germany, India, UK, Korea, and Japan have embarked on substantially funded national programs to strengthen manufacturing. Singapore’s $19 billion Research, Innovation and Enterprise plan known as RIE2020 has a major emphasis on Advanced Manufacturing and Engineering, AME. Automation and robots have been part of manufacturing innovation.
The terms “digital manufacturing” or “smart manufacturing” or “intelligent manufacturing” refer to communication and computing technologies which enable all players in the value chain of products at the supply chain, enterprise and shop floor levels to be digitally connected and data analytics-driven, thus achieving intelligent coordination for demand and supply matching, faster time to market, mass customisation and cost benefits. Engineers are developing Manufacturing Control Tower (MCT) for this purpose. Maintenance of machinery is an area of focus. For example, General Electric (GE), reported that at one of its plants, the use of Predix software platform connected to sensors led to the detection of gas leakage and preventive measures led to savings of $350,000 per year. Another example, consider cutter tool wear in a CNC milling machine. After each cut, photos from a high fidelity stereomicroscope can be taken to measure the wear of the cutting tool. Measurements from sensors such as vibration sensor and force dynamometer are sampled using data acquisition cards and the data are stored on computers. After data acquisition, the sensor readings are used for feature extraction and selection. These selected features can be used to train and cross-validate advanced neural network (NN) or Hidden Markov Model (HMM) models of the tool wear, e.g. via an offline computing platform away from the machine. When the models are trained and cross-validated, they can be used for: (a) diagnostics where the degree of tool wear in the current time step can be determined from the model given the current and past sensor readings, and (b) prognostics so that the tool wear is predicted. These predictions enable maintenance to be scheduled when necessary, thus reducing downtime while increasing reliability.