Human-centred design as a research field is characterised by multidisciplinarity and a variety of many similar methods. Previous research attempted to classify existing methods into groups and categories, e.g., according to the degree of user involvement. The research question here is the following: How can human-centredness be measured and evaluated based on resulting product concepts? The goal of the paper is to present and apply a new metric—the Human-Centredness Metric (HCM)—for the early estimation of the quality of any human-centred activity based on the four goals of human-centred design. HCM was employed to evaluate 16 concepts, utilising a 4-point Likert scale, covering four different everyday products that were created by four students, which used three different human-centred design methods for this. The first concept was created without the application of any additional human-centred design method. The results illuminated trends regarding the impact of additional human-centred design methods on the HCM score. However, statistical significance remained elusive, potentially due to a series of limitations such as concept complexity, the small number of concepts, and the early developmental stage. The study’s limitations underscore the need for refined items and expanded samples to better gauge the impact of human-centred methods on product development.

In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.