Innovation Management in Quality Control : A case study on porosity detection

This thesis explores possible implementation of a vision-based porosity detection tool for use in the quality control process at the case company, Volvo AB. Surface porosity in metal components can lead to structural weaknesses, making early and accurate detection essential for maintaining high manufacturing standards. Traditional testing methods such as X-ray or dye penetrant inspection are often time-consuming, costly or unsuitable for certain production environments. This project focuses on developing recommendations for the process of implementing a portable camera-based solution aimed at providing a more efficient alternative for porosity detection. The research is grounded in the principles of innovation management with focus on process innovation, as the project addresses incremental technological change within a rigid workflow, technology adoption models and continuous improvement practices. Interviews and observations were conducted with experts across different functions in the company to map the current quality control process and identify suitable integration points for the new tool. Additionally, a benchmarking study was performed, comparing existing standards and industry practices related to surface defect detection. The outcome of this thesis is a set of specific, actionable recommendations for integrating the vision-based tool into the company’s quality control procedures. These recommendations are designed to help improve detection accuracy, reduce manual inspection time and support long-term process optimization with the use of the vision-based tool. The findings contribute to ongoing efforts in digital transformation and lean manufacturing within the heavy machinery sector.