The analysis and evaluation of the influence of haptic-enabled virtual assembly training on real assembly performance. Issue 1 (March 2017)

• Record Type: Journal Article
• Authors: Gallegos-Nieto, Enrique; Medellín-Castillo, Hugo; González-Badillo, Germánico; Lim, Theodore; Ritchie, James
• Is Part Of: International journal of advanced manufacturing technology. Volume 89:Issue 1/4(2017)
• Page Start: 581
• Page End: 598
• Publication Date: 2017-03
• Subjects: Virtual assembly (VA) -- Virtual assembly training -- Assembly task -- Task completion time (TCT) -- Effectiveness -- Real assembly performance; Manufacturing processes -- Periodicals; Production engineering -- Periodicals; 670.427
• DOI: 10.1007/s00170-016-9120-4
• Languages: English
• ISSNs: 0268-3768

Abstract

Abstract This paper reports the results of an investigation carried out to analyse and evaluate the influence of haptic-enabled virtual assembly (VA) training with respect to real assembly tasks. The aim was to determine how well virtual assembly training transfers knowledge and skills to the trainee in order to improve their real-world assembly performance. To demonstrate this, a comprehensive analysis and evaluation of the influence of VA training on the real assembly performance is presented. This influence is assessed in terms of the effectiveness and efficiency when performing the real assembly task after undergoing VA training. The study considers the use of three training modes and several assembly tasks with increasing complexity and number of parts. The results indicate a significant improvement (of up to 80 %) in the real assembly performance of subjects who undertook VA training first when compared to those trained conventionally. Moreover, haptic-enabled VA training led to greater levels of effectiveness than without haptics. The results also revealed that the effectiveness of VA training depended on assembly task complexity, i.e. the greater the task complexity, the greater the effectiveness. Consequently, maximum VA training effectiveness was obtained with a combination of haptic-enabled VA training and high-complex assembly tasks.