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Dataanalys och visualisering för optimering av skärande bearbetning
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Computer Science.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Computer Science.
2020 (Swedish)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Sustainable development
The essay/thesis is partially on sustainable development according to the University's criteria
Alternative title
Data analysis and visualization for optimization of cutting processing (English)
Abstract [sv]

Sandvik Coromant och deras kunder har maskiner inom skärande bearbetning som genererar data vid produktion. Nyttolasten av det data som genereras innehåller olika mätvärden från sensorer inuti maskinen samt händelser som sker i maskinen under produktion.

I det här arbetet har insamlad data från maskiner använts för att försöka öka maskinernas produktivitet genom att bistå tekniker och maskinoperatörer med relevant information. För att förmedla informationen utvecklades ett mjukvarusystem som analyserar och visualiserar maskindata.

Dataanalysen gjordes med hjälp av artificiell intelligens som tränades på sekventiell data för att prediktera verktygsbrott. Vid identifikation av en datasekvens som potentiellt kan leda till ett verktygsbrott, meddelas maskinoperatören via en mobilapplikation installerad på en portabel enhet.

Datavisualiseringarna består av interaktiva linjediagram och tidssorterade listor av maskinhändelser. De interaktiva linjediagrammen är tvådimensionella och visar mätvärden med sitt ursprung från någon maskins sensorer längs y-axeln och tiden längs x-axeln. Interaktiviteten som finns tillgänglig för användare i linjediagrammen är zoomning, panorering samt klickbara datapunkter.

Abstract [en]

Sandvik Coromant and their customers have cutting machines that generate data during production. The payload of the data generated contains various measurement values from sensors inside the machineas, well as events that occur in the machine during production.

In this work, the data collected from the machines has been analyzed to try to increase the machines’ productivity by assisting technicians and machine operators with relevant information. To communicate the information, a software system was developed that analyzes and visualizes machine data.

The data analysis was done using artificial intelligence trained on sequential data to predict tool failure. When identifying a data sequence that could potentially lead to a tool failure, the machine operator is notified via a mobile application installed on a portable device.

The data visualizations consist of interactive line charts and time-sorted lists of machine events. The interactive line diagrams are two-dimensional and show measurement values originating from any machine's sensors along the y axis and time along the x axis. The interactivity available to users in the line graphs is zooming, panning, and clickable data points.

Place, publisher, year, edition, pages
2020. , p. 62
Keywords [en]
tool breakage, prediction, visualization, neural networks, cutting processing
Keywords [sv]
verktygsbrott, prediktering, visualisering, neurala nätverk, skärande bearbetning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hig:diva-32529OAI: oai:DiVA.org:hig-32529DiVA, id: diva2:1438472
External cooperation
Sandvik Coromant
Subject / course
Computer science
Educational program
Högskoleingenjör
Supervisors
Examiners
Available from: 2020-06-11 Created: 2020-06-10 Last updated: 2020-06-11Bibliographically approved

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Examensarbete AL NN 2020(1521 kB)247 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
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Output format
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