Implementing Marked-Based Augmented Reality in Discrete industry - A Case Study

Nithyanandam, Ganesh Kumar; Kothandaraman, Praveen Kumar; Munguia, Javier; Marimuthu, Muruthanayagam

doi:10.1109/hydcon48903.2020.9242696

Cited by 4 publications

(1 citation statement)

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“…In [25], the authors developed a glove capable of monitoring the force and vibrations resulting from connecting two electrical terminals to understand whether the task was successful or not. AR technology has been also applied by [26]. In this article, AR was selected as a solution in order to facilitate new workers in carrying out activities requiring high skills, relying less on specialised training and, at the same time, reducing production scraps generated by human errors.…”

Section: Process Controlmentioning

confidence: 99%

Poka Yoke Meets Deep Learning: A Proof of Concept for an Assembly Line Application

2022

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In this paper, we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The “as-is” line was highly inefficient due to several issues, including the age of the machines, a non-optimal arrangement of the shop floor, and the absence of process standards. The assembly line issues were analysed with Lean Manufacturing tools, identifying irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the “to-be” line included actions to update the department layout, modify the assembly process, and design the line feeding system in compliance with the concepts of Golden Zone (i.e., the horizontal space more ergonomically and easily accessible by the operator) and Strike Zone (i.e., the vertical workspace setup in accordance to ergonomics specifications). The re-engineering process identified a critical problem in the incorrect assembly of the oil seals, mainly caused by the difficulty in visually identifying the correct side of the component, due to different reasons. Convolutional neural networks were used to address this issue. The proposed solution resulted to be a Poka Yoke. The whole re-engineering process induced a productivity increase that is estimated from 46% to 80%. The study demonstrates how Lean Manufacturing tools together with deep learning technologies can be effective in the development of smart manufacturing lines.

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Section: Process Controlmentioning

confidence: 99%