Analysis and development of augmented reality (AR) technology for the maintenance of manufacturing equipment

Abstract

Industry 4.0 has introduced several modern technologies within the manufacturing industry, which have been utilised in various different sectors to improve and enhance the processes. This has increased the complexity of the machinery which has also increased the complexity of their maintenance processes. Maintenance is a particularly important aspect within manufacturing plants. This allows for the machinery to keep running smoothly with minimal downtime, therefore, maintaining productivity of the manufacturing plants. Currently, most manufacturing companies follow a preventive maintenance strategy. This type of strategy consists of planned maintenance tasks that need to be as efficient and effective as possible. Augmented Reality (AR) is an Industry 4.0 technology that enhances the efficiency and effectiveness of maintenance processes. AR brings about benefits that allow for the increased knowledge transfer of tacit knowledge, through the use of three-dimensional (3D) virtual models and animations. This is the primary knowledge required for maintenance tasks, that is difficult to transfer through the use of written instructions. The aim of this dissertation is to investigate how AR technology can be potentially adopted within manufacturing companies, specifically for the maintenance of their equipment. By using the Design Thinking methodology, an AR application was designed and developed to improve the way in which knowledge of a maintenance task is transferred. This AR solution is the digitalisation of a task, that is originally presented in a paper manual that describes the steps to be taken while replacing batteries and calibrating an industrial robot arm. Using a Microsoft Hololens 2 headset, 3D virtual models of the components were used together with simplified instructions to create a step-by-step AR application. After conducting tests that compared the content on the AR application created against the use of a paper manual, results showed that AR has several advantages. One important result that emerged from the test conducted was the ease with which the maintenance instructions were understood, along with the rate of comprehension. This dissertation successfully proved the potential that AR technologies can bring to the manufacturing industry for applications such as maintenance tasks.