Implementation of Machine Vision based Quality Inspection in Production: An Approach for the Accelerated Execution of Case Studies

Reichenstein, Tobias; Raffin, Tim; Sand, Christian; Franke, Jörg

doi:10.1016/j.procir.2022.09.058

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The field has noticed some types of robots being added, such as ground robots that are being coupled with certain vision techniques to ease the burden of human personnel [29]. Recent advances in Deep Learning (DL) have brought into the spotlight a variety of algorithms for object detection, semantic segmentation or image classification which significantly enhance vision systems in terms of quality inspection capabilities [30]. Such approaches are investigating the implementation of new systems by utilizing already existing or new and developing CNN (Convolutional Neural Network) architectures showing promising results [31].…”

Section: Human-robot Collaboration (Hrc) In Quality Inspection Processesmentioning

confidence: 99%

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Papavasileiou,

Nikoladakis,

Basamakis

et al. 2024

Applied Sciences

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Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.

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Section: Human-robot Collaboration (Hrc) In Quality Inspection Processesmentioning

confidence: 99%

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Papavasileiou,

Nikoladakis,

Basamakis

et al. 2024

Applied Sciences

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“…Another related work based on an adaptable CAVI system can be found in [44]. In their work, the authors propose a flexible hardware solution comprising standardized (e.g., lights, robots) and reconfigurable (i.e., handling system) components, integrated with flexible image processing algorithms using CNN and Explainable Artificial Intelligence (XAI), along with a knowledge transfer system for software parametrization support.…”

Section: Computer-aided Vision Inspection (Cavi)mentioning

confidence: 99%

“…Despite the close relationship with the general framework proposed in [44], we aim to provide a more general model not tied to a specific HW/SW architecture. To validate the proposed framework and present an industrial case study, a successful real-world implementation in the automotive industry is showcased.…”

Section: Computer-aided Vision Inspection (Cavi)mentioning

confidence: 99%

A framework for flexible and reconfigurable vision inspection systems

Lupi,

Biancalana,

Rossi

et al. 2023

Int J Adv Manuf Technol

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Reconfiguration activities remain a significant challenge for automated Vision Inspection Systems (VIS), which are characterized by hardware rigidity and time-consuming software programming tasks. This work contributes to overcoming the current gap in VIS reconfigurability by proposing a novel framework based on the design of Flexible Vision Inspection Systems (FVIS), enabling a Reconfiguration Support System (RSS). FVIS is achieved using reprogrammable hardware components that allow for easy setup based on software commands. The RSS facilitates offline software programming by extracting parameters from real images, Computer-Aided Design (CAD) data, and rendered images using Automatic Feature Recognition (AFR). The RSS offers a user-friendly interface that guides non-expert users through the reconfiguration process for new part types, eliminating the need for low-level coding. The proposed framework has been practically validated during a 4-year collaboration with a global leading automotive half shaft manufacturer. A fully automated FVIS and the related RSS have been designed following the proposed framework and are currently implemented in 7 plants of GKN global automotive supplier, checking 60 defect types on thousands of parts per day, covering more than 200 individual part types and 12 part families.

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