Manual repair tasks in the industry of maintenance, repair, and overhaul require experience and object-specific information. Today, many of these repair tasks are still performed and documented with inefficient paper documents. Cognitive assistance systems have the potential to reduce costs, errors, and mental workload by providing all required information digitally. In this case study, we present an assistance system for object-specific repair tasks for turbine blades. The assistance system provides digital work instructions and uses augmented reality to display spatial information. In a user study with ten experienced metalworkers performing a familiar repair task, we compare time to task completion, subjective workload, and system usability of the new assistance system to their established paper-based workflow. All participants stated that they preferred the assistance system over the paper documents. The results of the study show that the manual repair task can be completed 21 % faster and with a 26 % lower perceived workload using the assistance system.

翻译：手动修复任务在维护、修理和大修行业中需要经验及特定对象信息。目前，许多此类修复任务仍依赖低效的纸质文档进行执行与记录。认知辅助系统通过数字化方式提供所有必要信息，具有降低成本、减少错误及减轻认知负荷的潜力。在本案例研究中，我们提出了一种针对涡轮叶片特定对象修复任务的辅助系统。该系统提供数字化工作指导，并利用增强现实技术展示空间信息。通过一项涉及十名经验丰富金属工人执行熟悉修复任务的用户研究，我们将新辅助系统与既有纸质工作流程在任务完成时间、主观工作负荷及系统可用性方面进行了对比。所有参与者均表示更倾向于使用辅助系统而非纸质文档。研究结果表明，使用辅助系统可使手动修复任务完成速度提高21%，且感知工作负荷降低26%。