STTR Phase I: Intelligent Instruction Systems using Augmented Reality

Juxtopia, Llc, Baltimore MD

Investigators

Abstract

This Small Business Technology Transfer Phase I project seeks to enhance the current state of-the-art in production line manufacturing training processes with an initial focus on the automotive industry. Human-directed training by other skilled employees and supervisors does not take advantage of recent technologies that can make training routines more autonomous, thereby mitigating the impact on the quality of training in periods where resources are constrained. Empirical training results (i.e., quantitative training feedback that is not currently available) can assist planners in optimizing manufacturing processes. The proposed innovation lies in the creation of intelligent instruction systems that exploit adaptive software mechanisms (i.e., intelligent software agents) and augmented reality (AR) techniques, thus enhancing training techniques while promoting continuous process standardization, assessment, optimization and resource management. Since it is common that production-line employees are required to wear goggles, intelligent agents could transfer their instruction via goggle-like wearable computers (i.e., AR) that overlay the actual visual field with text and computer graphics. A major aspect of this research is the architecture, framework, and feasibility analysis of the insertion of these technologies into real manufacturing environments. Initial analysis will be with two facilities, General Motors and BMW. The proposed techniques will enable real-time assessment of employees during training routines and enable the software agents to automatically and proactively reinforce weaker areas based on these assessments. An overall assessment model of all employees can characterize the entire workforce for a particular facility. This overall assessment can be used to enhance resource management required by the on-going problem of absenteeism. Probably the greatest innovation would be a framework to allow planners to exploit the assessments and optimize manufacturing processes by refactoring traditional, perhaps obsolete, production processes. If intelligent agents could manage and direct the cross training of employees in any industry, a major pay-off would be the efficient use of resources. In addition, the use of such agents would enhance the following as expressed by the common claim: "Standardization of processes results in the predictability of the final product". If all employees were trained to perform similar tasks consistently and intelligent agents and AR could enforce that standardization, quality would then also become predictable and measurable. Manufacturing environments beyond automotive will have the models, frameworks, tools, and techniques to standardize their processes. As a result, the quality of products delivered to external customers in any industry can be improved, while at the same time production costs can be reduced.