Augmented reality smart glasses (ARSG) are increasingly popular and have been identified as a vital technology supporting shop-floor operators in the smart factories of the future. By improving our knowledge of how to efficiently evaluate and select the ARSG for the shop-floor context, this paper aims to facilitate and accelerate the adoption of the ARSG by the manufacturing industry. The market for ARSG has exploded in recent years, and the large variety of products to select from makes it not only difficult but also time consuming to identify the best alternative. To address this problem, this paper presents an efficient step-by-step process for evaluating the ARSG, including concrete guidelines as to what parameters to consider and their recommended minimum values. Using the suggested evaluation process, manufacturing companies can quickly make optimal decisions about what products to implement on their shop floors. This paper demonstrates the evaluation process in practice, presenting a comprehensive review of currently available products along with a recommended best buy. This paper also identifies and discusses topics meriting research attention to ensure that the ARSG are successfully implemented on the industrial shop floor.

This paper describes a study of using the concept of augmented reality for supporting assembly line workers in carrying out their task optimally. By overlaying virtual information on real world objects – and thereby enhance the human’s perception of reality – augmented reality makes it possible to improve the visual guidance to the workers. In the study, a prototype system is developed based on the Oculus Rift platform and evaluated using a simulated assembling task. The main aim is to investigate user acceptance and how this can possible be improved.

With augmented reality, virtual information can be overlaid on the real world in order to enhance a human’s perception of reality. In this study, we aim to deepen the knowledge of augmented reality in the shop-floor context and analyze its role within smart factories of the future. The study evaluates a number of approaches for realizing augmented reality and discusses advantages and disadvantages of different solutions from a shop-floor operator’s perspective. The evaluation is done in collaboration with industrial companies, including Volvo Cars and Volvo GTO amongst others. The study also identifies important future research directions for utilizing the full potential of the technology and successfully implement it on industrial shop-floors

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.