Machine Learning and Low Cost Ultrasonic Sensors for the Optimisation of Industrial Mixing Processes

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The world is undergoing the fourth industrial revolution where digital technologies such as artificial intelligence, robotics, and the Internet of Things are used to improve the productivity, efficiency and sustainability of manufacturing processes. Industry 4.0 is underpinned by the acquisition and intelligent use of data. Therefore, sensors are a key technology for this manufacturing transformation. Mixing is one of the most common processes across manufacturing as it is not only used for combining materials, but also for suspending solids, increasing heat and mass transfer, providing aeration, and modifying material structure. Although several sensing techniques are available for monitoring mixing process, each have their own applications and limitations. Ultrasonic sensors are low-cost, real-time, in-line, able to be non-invasive, and capable of operating in opaque systems. There is little previous literature using ultrasonic sensors to monitor mixing. The industrially applicable, non-invasive, reflection-mode sensing technique employed in this research, along with the extensive data post-processing investigated, separates this work from the existing literature. The student will develop several model mixing systems in a laboratory setting and acquire sensor data during the mixing processes. Supervised Machine Learning (ML) trains by mapping input data to output values to then be able to predict output values from new input data. Classification ML models will be trained to predict whether the system is non-mixed or fully mixed. Regression ML models will be developed to predict the time remaining until mixing completion. Determination of whether a system is mixed or non-mixed would provide industrial processes benefits such as less off-specification product and less resource consumption caused by over-mixing. Prediction of the mixing time remaining would allow for better batch scheduling and therefore process productivity. The quality of the input data for ML models effects the prediction performance. Often, some specialist sensor or process knowledge is needed to engineer useful features from the data. Therefore, another aspect of this research is to use Convolution Neural Networks (CNN) which require no manual feature engineering from the sensor data. By using CNNs, the burden on operators deploying ultrasonic sensors in industrial processes can be reduced. Multi-sensor data fusion, combining outputs from multiple sensors to produce greater ML performance over that which could be achieved using a single sensor, will also be explored. Further research avenues of this work will focus on industrial application. For example, working with industrial partners to monitor their mixing processes. In addition, focus can be on overcoming the problem of limited output values available for training ML models in industrial settings. This is because a reference measurement for the mixture's state is often difficult, expensive, or time-consuming to obtain. Two methods for overcoming this difficulty are transfer learning and semi-supervised learning. Transfer learning involves training a ML model on a similar system where it is easier to obtain reference measurements, and then using the model to aid in prediction of the target system. Semi-supervised learning uses information from the sensor data with no output values available as well as those with output values provided.