A Wireless Angle and Position Tracking Concept for Live Data Control of Advanced, Semi-Automated Manufacturing Processes

Lipka, Melanie; Meinel, David; Müller, Stefan A.; Sippel, Erik; Franke, Jörg; Vossiek, Martin

doi:10.3390/s20092589

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…There are relatively few published studies that have attempted to understand manual operations in electrical machine manufacturing process. Research by Lipka et al 34 and Fischer et al 35 proposed using a nearfield localization system for assembly operations in order to detect the position and orientation of assembly tools or assembler's hands. However, this mechanism has a limitation that it may not be able to capture cause-effect link between worker actions and their effect on the state of workpiece.…”

Section: State Of the Artmentioning

confidence: 99%

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

Tiwari

Farnsworth

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, several manufacturing operations continue to involve manual intervention because of the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances of the final machine, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in in-process monitoring particularly for error prone manual operations. This paper presents a literature review of the state-of-the-art available techniques and limitations in process monitoring within the context of electrical machine manufacturing. To quantify the degree of manual activities in process monitoring within electrical machine manufacture, a structured survey of UK based companies was conducted, identifying specific error prone manual processes to target, and gaps in inspection. The survey identified that a significant proportion of activities in electrical machine manufacture are manual, or semi-automated with manual interventions. However, literature review revealed only a limited research in in-process monitoring of manual operations in this area. Finally, two case studies are presented where case study 1 presents a framework for digitisation of a variety of manual manufacturing tasks, and case study 2 demonstrates real-time capture, modelling and analysis of deformable linear objects in electrical machine manufacturing.

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Section: State Of the Artmentioning

confidence: 99%

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

Tiwari

Farnsworth

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation

O’Donovan

Иван

Todeschini

et al. 2023

Int J Adv Manuf Technol

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Deep learning in computer vision is becoming increasingly popular and useful for tracking object movement in many application areas, due to data collection burgeoning from the rise of the Internet of Things (IoT) and Big Data. So far, computer vision has been used in industry predominantly for quality inspection purposes such as surface defect detection; however, an emergent research area is the application for process monitoring involving tracking moving machinery in real time. In steelmaking, the deployment of computer vision for process monitoring is hindered by harsh environments, poor lighting conditions and fume presence. Therefore, application of computer vision remains unplumbed. This paper proposes a novel method for tracking hot metal ladles during pouring in poor lighting. The proposed method uses contrast-limited adaptive histogram equalisation (CLAHE) for contrast enhancement, Mask R-CNN for segmentation prediction and Kalman filters for improving predictions. Pixel-level tracking enables pouring height and rotation angle estimation which are controllable parameters. Flame severity is also estimated to indicate process quality. The method has been validated with real data collected from ladle pours. Currently, no publications presenting a method for tracking ladle pours exist. The model achieved a mean average precision (mAP) of 0.61 by the Microsoft Common Objects in Context (MSCOCO) standard. It measures key process parameters and process quality in processes with high variability, which significantly contributes to process enhancement through root-cause analysis, process optimisation and predictive maintenance. With real-time tracking, predictions could automate ladle controls for closed-loop control to minimise emissions and eliminate variability from human error.

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A Wireless Angle and Position Tracking Concept for Live Data Control of Advanced, Semi-Automated Manufacturing Processes

Cited by 2 publications

References 28 publications

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation

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