Decision support for augmented reality-based assistance systems deployment in industrial settings

Area of Science:

• Industrial Engineering
• Human-Computer Interaction
• Manufacturing Systems

Background:

• Augmented Reality (AR) offers potential for on-the-job guidance in industries.
• Deployment success hinges on factors like expertise, digital content, and task characteristics (error-proneness, complexity).
• Applicability in unique manufacturing settings with diverse products is often unclear for decision-makers.

Purpose of the Study:

• To develop a decision support tool for customized AR-based assistance system deployment strategies.
• To address the challenge of gauging AR's applicability in complex and idiosyncratic manufacturing scenarios.
• To provide a framework for preparing tailored AR deployment strategies, using manual assembly as a primary example.

Main Methods:

• Development of a decision support tool for AR deployment strategy preparation.
• Conducting an interview study with sixteen domain experts in manufacturing and AR.
• Analysis of expert knowledge regarding criteria weighting for AR system implementation.

Main Results:

• Significant differences in criteria weighting were identified based on task complexity.
• Expert knowledge analysis revealed varying importance of factors like data acquisition effort.
• The study provides insights into the nuanced considerations for AR adoption in manual assembly.

Conclusions:

• A decision support tool can facilitate customized AR deployment strategies.
• Task complexity and data availability are critical factors influencing AR implementation decisions.
• Understanding expert criteria weighting is essential for effective AR integration in manufacturing.