Abstract

The amount of digital data surrounding us has exploded within the past years. In industry, data are gathered from different production phases with the intent to use the data to improve the overall manufacturing process. However, management and utilization of these huge data sets is not straightforward. Thus, a computer-driven approach called data mining has become an attractive research area. Using data mining methods, new and useful information can be extracted from enormous data sets.

In this thesis, diverse industrial problems are approached using data mining methods based on similarity. Similarity information is shown to give an additional advantage in different phases of manufacturing. Similarity information is utilized with smaller-scale problems, but also in a broader perspective when aiming to improve the whole manufacturing process. Different ways of utilizing similarity are also introduced. Methods are chosen to emphasize the similarity aspect; some of the methods rely entirely on similarity information, while other methods just preserve similarity information as a result.

The actual problems covered in this thesis are from quality control, process monitoring, improvement of manufacturing efficiency and model maintenance. They are real-world problems from two different application areas: spot welding and steel manufacturing. Thus, this thesis clearly shows how the industry can benefit from the presented data mining methods.