Optimising the automated vision inspection system of battery sheets
| dc.contributor.author | Sivakumar, Guru Prasath | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap | sv |
| dc.contributor.examiner | Despeisse, Mélanie | |
| dc.contributor.supervisor | Chari, Arpita | |
| dc.date.accessioned | 2021-07-01T12:14:50Z | |
| dc.date.available | 2021-07-01T12:14:50Z | |
| dc.date.issued | 2021 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | The utilisation of lithium-ion batteries in the field of Electric Vehicles (EVs) has increased its global demand. Northvolt, a Swedish lithium-ion battery manufacturer is setting up Europe’s largest battery manufacturing (in terms of battery capacity) facility at Skellefteå, Sweden. The electrode manufacturing process stage in the battery production line at Northvolt Ett will perform the quality operation through a vision inspection system, which is being developed in-house at Northvolt Labs. This thesis is focused on identifying a suitable machine learning model through which the anomalies can be detected; also, to investigate how the big data collected through the vision inspection system can be further utilised for improving the battery manufacturing process at Northvolt Ett. In total, five research questions on anomaly detection and big data usage were formulated. Qualitative study was performed to understand the various stakeholder requirements, challenges in detecting anomalies, manufacturing process constraints etc. at Northvolt. Experts from different working backgrounds at Northvolt were interviewed and over 50 research articles were analysed to answer the research questions. Deep learning was found to be a suitable method for anomaly detection and various deep learning techniques such as Convolutional Neural Network (CNN), Generative Adversarial Network (GAN) can be used to detect anomalies and predict the type of anomaly on a high-level classification. A framework is proposed for transforming the data collected through vision inspection into meaningful value for improving the battery manufacturing process at Northvolt Ett. Overall, it was understood that the behaviour of the machine learning/deep learning model is greatly influenced by the richness of the input data. Keywords: Vision Inspection, Anomaly Detection, Deep Learning, Convolutional Neural Network, Big Data Analysis. | sv |
| dc.identifier.coursecode | IMSX30 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/302920 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Vision Inspection, Anomaly Detection, Deep Learning | sv |
| dc.subject | Convolutional Neural Network, Big Data Analysis | sv |
| dc.title | Optimising the automated vision inspection system of battery sheets | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |