Purpose

This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone.

Design/methodology/approach

This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis.

Findings

The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online.

Originality/value

This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.

中文翻译：

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基于投影的增强现实系统，用于装配指导和监视

目的

这项研究旨在监视和指导组装过程。在批量定制生产的手动组装过程中，操作员需要根据产品规格更改组装过程。传统的信息查询和显示方法（例如，手动查找装配图或电子手册）效率低下且容易出错。

设计/方法/方法

本文提出了一种基于投影的增强现实系统（PBARS），用于装配指导和监视。该系统包括基于视点跟踪的投影方法，其中跟踪操作者头部的位置，并相应地更改投影图像。装配监视阶段应用一种零件识别方法。首先，通过将经典的LBP运算符与像素分类过程合并来实现像素局部二进制模式（PX-LBP）运算符。然后，提取深度图像的PX-LBP特征，并使用随机决策森林分类器获得像素分类预测图像（PCPI）。零件识别和装配监控通过PCPI分析执行。

发现

投影图像随着人体的视点而变化，因此操作者总是从不同的视点感知三维引导场景，从而改善了人机交互性。通过比较PCPI来实现零件识别和装配监控，可以在线检测丢失和错误的装配。

创意/价值

本文设计了PBARS，以同时监视和指导组装过程，并在大规模定制生产中具有潜在的应用。基于像素分类的零件识别和装配监控提供了一种新颖的装配监控方法。