Data aspects in the reinforcement of polymer gears manufactured by injection moulding

This study is data driven research, focusing on injection moulded polymer gears. With the help of big data, python scripts were created to summarise academic papers automatically, and bibliometrics research was conducted to discover the main challenges in polymer gears. Challenges were found in the whole life cycle of polymer gear manufacturer including design, manufacturing, and testing. As a practical research topic, the study on polymer gears crossed with many disciplines including mechanical engineering, material science, computer science, electrical engineering and statistics. Following the literature based review, a targeted experimental programme was undertaken using injection moulding. Mould designs were examined and improved to create optimum mouldings and high quality gears were produced. A study on the effect of injection moulding parameters revealed the relationship between the injection moulding parameter settings and the polymer gear performance for material reinforcement in gears with optimisation in the injection moulding process. Further research was undertaken on widely applied POM gears and it was concluded that the homopolymer and copolymer POM gears perform in service differently. It was a novel finding for this commonly applied material. Further, a new effective polymer gear analysis procedure was introduced, putting more emphasis on the material science aspects. This procedure successfully explained differences in performance between POM types was due to the inherent crystallinity. Finally, a new cloud-based Internet of Things (IoT) solution for polymer gears was explored. This offers a potential future ability to collect data from real polymer gear applications and to help secure applications with real-time gear status monitoring