Computer Architecture for Industrial Training Evaluation

Abstract

Companies have tried to innovate in their training processes to increase their productivity indicators, reduce equipment maintenance costs, and improve the work environment. The use of Augmented Reality (AR) has been one of the implemented strategies to upgrade training processes, since it optimizes, through User Interface (UI) Design, experiences designed for users (UX) that are focused on education and training contexts. This research describes the definition and implementation of an IT architecture based on the ISO/IEC/IEEE 42010 standard using the Zachman and Kruchten frameworks. The methodological proposal presents an architecture seen from a business perspective, taking into account the strategic and technological components of the organization under a strategic alignment approach. The result is a six-layer architecture: The Government Strategy Layer (1) that accounts for the strategic component; the Business Layer (2) that presents the business management perspective; the Information Layer (4) that defines the metrics system: efficiency through task time, effectiveness through tasks completed, and satisfaction with overall satisfaction. In the Data Layer (4), the data collected with the metrics are structured in an industrial scenario with a cylinder turning process on a Winston Lathe. The experiment was carried out with two groups of 272 participants. In the Systems and Applications Layer (5), two applications were designed: a web client and a mobile application with augmented reality, and finally, the Networks and Infrastructure Layer (6), which delivers the two functional applications. The architecture validation was carried out using the mobile application. The analysis of the results showed a significance value of less than 0.001 in the three indicators: efficiency, effectiveness, and satisfaction in the Levene test and Student’s t-test. To corroborate the results, a test of equality of means with the Mann–Whitney U was carried out, showing that the three indicators presented significantly different values in the two experimental groups of this study. Thus, the group trained with the application obtained better results in the three indicators. The proposed architecture is adaptable to other training contexts. Information, data, and systems and application layers allowed for the exchange of training processes so that the augmented reality application is updated according to the new requirements.