Recommender systems for Personalized Work Instructions

The introduction of digital systems in manufacturing has increased the number of ways that assistant information reaches the human operators in assembly work spaces. Such digital systems can accustom to different degrees of support, based on the level of detail of the information and the used medium. As the production environment becomes increasingly flexible and the tasks are changing in a faster pace, a more agile way for recommendations of support or instructions is needed. This work will present and compare two ways to represent instructional support, namely, on-screen digital work instructions (DWI) and Augmented Reality (AR)- based instructions. Furthermore, in order to assist an operator with the appropriate instructional support, a recommender system (RS) is developed, which matches an operator’s proficiency, a task’s requirement and a level of instruction. A purely data-driven RS using Bayesian Probabilistic Matrix Factorization (BPMF) is compared with a skill-driven approach. The latter has the advantage that the recommended level of instruction is directly relatable to a skill-based operator proficiency metric. For the evaluation of the two instruction delivery approaches and for training and testing the RS, a group of 13 users were called to complete two industrial setups, alternating between the different instruction types. Both RS were able to outperform a naive baseline.