A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process

Choi, Taihun; Seo, Yoonho

doi:10.3390/s20185386

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…The evaluation indicator that can represent the performance of the model most intuitively is accuracy [ 39 ] and will be considered for the final CNN evaluation.…”

Section: Methodsmentioning

confidence: 99%

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Brili

Ficko

Klančnik

2021

Sensors

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This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9,000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.

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“…The evaluation indicator that can represent the performance of the model most intuitively is accuracy [ 39 ] and will be considered for the final CNN evaluation.…”

Section: Methodsmentioning

confidence: 99%

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Brili

Ficko

Klančnik

2021

Sensors

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“…The performance of the model is represented most intuitively by accuracy [ 34 ], so all four classifications were compared by this evaluation indicator ( Figure 6 ).…”

Section: Resultsmentioning

confidence: 99%

Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography

Brili

Ficko

Klančnik

2021

Sensors

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In turning, the wear control of a cutting tool benefits product quality enhancement, tool-related costs‘ optimisation, and assists in avoiding undesired events. In small series and individual production, the machine operator is the one who determines when to change a cutting tool, based upon their experience. Bad decisions can often lead to greater costs, production downtime, and scrap. In this paper, a Tool Condition Monitoring (TCM) system is presented that automatically classifies tool wear of turning tools into four classes (no, low, medium, high wear). A cutting tool was monitored with infrared (IR) camera immediately after the cut and in the following 60 s. The Convolutional Neural Network Inception V3 was used to analyse and classify the thermographic images, which were divided into different groups depending on the time of acquisition. Based on classification result, one gets information about the cutting capability of the tool for further machining. The proposed model, combining Infrared Thermography, Computer Vision, and Deep Learning, proved to be a suitable method with results of more than 96% accuracy. The most appropriate time of image acquisition is 6–12 s after the cut is finished. While existing temperature based TCM systems focus on measuring a cutting tool absolute temperature, the proposed system analyses a temperature distribution (relative temperatures) on the whole image based on image features.

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“…One possibility to improve industrial processes in the case of a shipyard is presented in [46], introducing augmented reality as one of the key Industry 4.0 paradigms to the ship production process. Similar technology has been presented in [47], considering the possibility of measuring the schedule performance using the Internet of Things and marker-based image processing. Moreover, the importance of data-mining and analysis techniques to create effective cost and performance estimates is pointed out in [48], while a methodology to integrate discrete-event simulations into production planning is presented in [49].…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Towards Digital Twinning of Fabrication Lines in Shipyards

Hadžić

Ložar

Opetuk

et al. 2023

JMSE

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The digital twinning concept stands as a remarkable opportunity to integrate sophisticated mathematical models within the context of existing manufacturing systems. Such models may provide shipyard management with predictive analytics, improving the final results at the strategic, tactical, and operational levels. Therefore, the possibility of integrating the Markovian-framework-based finite-state method into the context of ship production is presented in this study, including its outline, digital thread, and factory-floor data reliance. First, the predictive analytics problem is addressed by the finite-state method in the case of the shipyard’s fabrication line, and the obtained results are validated afterward using a numerical model through discrete-event theory. The predictive analytics indicate an almost ideal balancing of the fabrication line, except for the buffers storing stiffeners before the coat-dying and marking operations. In addition, the improvability analysis of the shipyard’s fabrication lines extended the scope of the predictive analytics using bottleneck identification and affecting the key performance indicators through a digital thread, as well as by improved maintenance strategies.

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A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process

Cited by 13 publications

References 36 publications

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography

Towards Digital Twinning of Fabrication Lines in Shipyards

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