Additive Manufacturing (AM), generally also referred to as 3D printing, has gone through vast development in the past 20 years which still continues. In particular, the market segment of personal 3D printers has achieved an average annually growth rate of approximately 170% from 2008 to 2013. The purpose of this research is to identify the best AM process applied in personal printers in terms of cost, sustainability, surface roughness, and human perception, as these aspects are essential for this new thriving market segment's future. In addition, the research investigates which objective roughness parameters are suitable for qualifying subjective perceptions. The primary AM processes, Fused Deposition Modeling, Stereolithography and Polyjet printing are in the focus of this research. Manufacturing costs as well as environmental impact are calculated, five independent roughness parameters (Ra, Rz, Rq, Rsk, and Rku) are measured and the subjective perception of samples is assessed through sensorial analysis. In conclusion, samples manufactured with Polyjet printing have the best subjective quality, but the highest costs and environmental impact. Biplots of roughness parameters versus sensorial ranking indicates a significant correlation between maximum peak-to-valley height Rz and tactile and visual perception, while the kurtosis of the topography height distribution Rku correlated best to the hedonic rank.

Product improvement, usually through changes in design and functionality, is relying more and more on the continuous analysis of large amounts of data. Product data can come from many sources with varying effort in obtaining the data, e.g., condition monitoring and maintenance data. Intelligent products, also known as “product embedded information devices” (PEID), are already equipped with sensors and onboard computing capabilities and therefore able to generate valuable data such as the number of user interactions during the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product interaction automatically and utilize it as part of data-driven product improvement. Product engineers and designers will gain insights into the use phase and can derive design changes and quality improvements. The methodology guides the user through research on product use dimensions based on the principles of user-centered design (UCD). The findings are applied to define what usage elements, such as specific actions and context, need to be available from the use phase. During systems development, machine learning is suggested to fuse sensor data to efficiently capture the usage elements. After product deployment, use data are retrieved and analyzed to identify the improvement potential. This research is a first step on the long way to self-optimizing products.

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