Classifying laser solders : Machine learning in production

Sammanfattning

Advancements in machine learning and artificial intelligence has created opportunities for vast improvements in the manufacturing sector. This study was conducted at a world-leading manufacturing company with the goal to assist in the development of a framework for application of machine learning in the company's operational workflow. Specifically, with the aim to investigate the potential benefits and pitfalls when utilizing machine learning to supervise a laser soldering process. This thesis analyzes and designs all the required steps for a machine learning approach for this specific manufacturing process. This included (1) image capturing, (2) preprocessing, (3) modelling, (4) testing and (5) functional tool. The thesis also discusses strategies for dealing with limitations posed by the industrial environment, for example unattainable process data and imbalanced datasets. In conclusion, it became evident that for a machine learning approach in an industrial setting it is crucial to understand the underlying process, the importance of a reliable data collection setup as well as the necessity of a proper framework. The thesis also proposes a sliding window approach as a preprocessing method for similar image classification tasks.