Översikt
- Datum:Startar 21 januari 2026, 10:00Slutar 21 januari 2026, 12:00
- Plats:VDL (Virtuella utvecklingslaboratoriet), Chalmers, Tvärgata 4C
- Opponent:Prof. Mohamed El-Mansori, Ecole Nationale Supérieure d'Arts et Métiers (ENSAM) ParisTech, France.
- AvhandlingLäs avhandlingen (Öppnas i ny flik)
The Swedish manufacturing industry is seeking innovative ways to produce eco-efficient and resource-efficient products while maintaining high quality and competitiveness. To achieve this, the control and optimization of production processes, particularly with novel materials and surface engineering, are essential. As demand for functional and aesthetically appealing surfaces increases, the industry must bridge the gap between technical performance and perceived quality. This thesis examines how surface design and characterization can be integrated with design intention and perception to ensure robust and sustainable production.
The definition and interpretation of surface roughness and appearance varies across different industries and academic fields. Surface topography significantly influences both functional properties, such as wear, friction, and wettability, as well as perceived attributes like gloss and texture. However, conventional average roughness parameters (Ra or Sa) provide limited insight into these multidimensional characteristics. This research, therefore, proposes a methodology that combines standard surface parameters with statistical analysis to identify and optimize the most significant parameters that describe surface function and appearance.
Through case studies and industrial collaborations, surfaces produced by additive, subtractive, and formative manufacturing processes were analyzed using areal parameters (ISO 25178), power spectral density, and scale-sensitive fractal analysis. Regression-based methods were used to identify parameter combinations that best describe surface characteristics and their correlation with process variables. By linking technical and emotional functions, hard and soft metrology, the developed methodology enables an improved understanding of how production conditions affect both functional performance and perceived quality.
The research emphasizes the importance of transdisciplinary collaboration between design, engineering, and production to preserve design intent throughout the manufacturing chain. The proposed framework contributes to the development of robust production systems that coexist with surface functionality and perceived quality, supporting sustainability goals and future integration with AI-driven optimization. Ultimately, this work demonstrates how the interplay between measurable surface characteristics and human perception can guide the industry in designing meaningful, high-quality products that perform well, both technically and emotionally. Contribution: The research bridges the gap between mechanical engineering, product design, and manufacturing by linking surface functionality with perceived quality. It advances surface characterization beyond average roughness, enabling predictive, data-driven optimization.
The definition and interpretation of surface roughness and appearance varies across different industries and academic fields. Surface topography significantly influences both functional properties, such as wear, friction, and wettability, as well as perceived attributes like gloss and texture. However, conventional average roughness parameters (Ra or Sa) provide limited insight into these multidimensional characteristics. This research, therefore, proposes a methodology that combines standard surface parameters with statistical analysis to identify and optimize the most significant parameters that describe surface function and appearance.
Through case studies and industrial collaborations, surfaces produced by additive, subtractive, and formative manufacturing processes were analyzed using areal parameters (ISO 25178), power spectral density, and scale-sensitive fractal analysis. Regression-based methods were used to identify parameter combinations that best describe surface characteristics and their correlation with process variables. By linking technical and emotional functions, hard and soft metrology, the developed methodology enables an improved understanding of how production conditions affect both functional performance and perceived quality.
The research emphasizes the importance of transdisciplinary collaboration between design, engineering, and production to preserve design intent throughout the manufacturing chain. The proposed framework contributes to the development of robust production systems that coexist with surface functionality and perceived quality, supporting sustainability goals and future integration with AI-driven optimization. Ultimately, this work demonstrates how the interplay between measurable surface characteristics and human perception can guide the industry in designing meaningful, high-quality products that perform well, both technically and emotionally. Contribution: The research bridges the gap between mechanical engineering, product design, and manufacturing by linking surface functionality with perceived quality. It advances surface characterization beyond average roughness, enabling predictive, data-driven optimization.