The present study investigated the performance of three ceramic inserts in terms of the micro-geometry (nose radius and cutting edge type) with the aid of a 3D finite element (FE) model. A set of nine simulation runs was performed according to three levels of cutting speed and feed rate with respect to a predefined depth of cut and tool nose radius. The yielded results were compared to the experimental values that were acquired at identical cutting conditions as the simulated ones for verification purposes. Consequently, two more sets of nine simulations each were carried out so that a total of 27 turning simulation runs would adduce. The two extra sets corresponded to the same cutting conditions, but to different cutting tools (with varied nose radius). Moreover, a prediction model was established based on statistical methodologies such as the response surface methodology (RSM) and the analysis of variance (ANOVA), further investigating the relationship between the critical parameters (cutting speed, feed rate, and nose radius) and their influence on the generated turning force components. The comparison between the experimental values of the cutting force components and the simulated ones demonstrated an increased correlation that exceeded 89%. Similarly, the values derived from the statistical model were in compliance with the equivalent FE model values due to the verified adequacy.

As the number of Internet users grows, the increase in smart devices interconnected through the Internet of Things (IoT) have contributed to improvements in the functionality of everyday products and enhancement of user experience. Yet, they affect user privacy and render personal data more vulnerable. To foster a digital future fully aware of user privacy requirements, a line of design research emerges that focuses on balancing product innovation with user data protection. This matter relates to sociocultural, economic, and technological aspects, and its core is a human-centered design strategy. Still, there is a gap in academic research oriented towards guiding product developers on how to consider personal data privacy concerns when designing honest IoT devices. To define this gap and delve deeper into this relevant topic, this paper presents a systematic literature review of recent academic research using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. This review focuses on prevalent research topics such as data privacy, personal data, data surveillance, and user behaviour in IoT. The result is a state-of-the-art compilation of 45 scientific studies mapping the most relevant concepts and approaches for product development in the last ten years of research, aligned with some central research questions. The Discussion and Conclusion sections provide a deep understanding of the complexity of the fast-changing landscape of privacy and personal data management using IoT products. Finally, this study proposes future academic research directions devoted to providing product designer specific, specialised help from different (yet interconnected) scientific approaches.

Hard turning is one of the most used machining processes in industrial applications. This paper researches critical aspects that influence the machining process of AISI-4140 to develop a prediction model for the resultant machining force-induced during AISI-4140 hard turning, based on finite element (FE) modelling. A total of 27 turning simulation runs were carried out in order to investigate the relationship between three key parameters (cutting speed, feed rate, and depth of cut) and their effect on machining force components. The acquired numerical results were compared to experimental ones for verification purposes. Additionally, a mathematical model was established according to statistical methodologies such as the response surface methodology (RSM) and the analysis of variance (ANOVA). The plurality of the simulations yielded results in high conformity with the experimental values of the main machining force and its components. Specifically, the resultant cutting force agreement exceeded 90 % in many tests. Moreover, the verification of the adequacy of the statistical model led to an accuracy of 8.8 %.

The resources of modern Finite Element Analysis (FEA) software provide engineers with powerful mechanisms that can be used to investigate numerous machining processes with satisfying results. Nevertheless, the success of a simulation, especially in three dimensions, relies heavily on the accuracy of the cutting tool models that are implemented in the analyses. With this in mind, the present paper presents an application developed via Computer-Aided Design (CAD) programming that enables the automated design of accurate cutting tool models that can be used in 3D turning simulations. The presented application was developed with the aid of the programming resources of a commercially available CAD system. Moreover, the parametric design methodology was employed in order to design the tools according to the appropriate standards. Concluding, a sample tool model was tested by performing a number of machining simulations based on typical cutting parameters. The yielded results were then compared to experimental values of the generated machining force components for validation. The findings of the study prove the functionality of the tool models since a high level of agreement occurred between the acquired numerical results and the experimental ones.

Trochoidal milling is a well-stablished machining strategy which still allows for the introduction of new approaches. This strategy can be applied to any kind of material, although it is usually associated to advanced materials, such as titanium and nickel alloys. This study is based on the adaptation of the feed speed of a milling tool with Ti-6Al-4V, so the chip width can be maintained constant without modifying the path geometry. A singularity in the experimental stage was to mill an Archimedes spiral groove instead of the conventional straight grooves. This made it possible to obtain a concave wall as well as a convex one and to optimize the amount of material used. The time efficiency compared to a constant feed, was slightly superior to 20%, reducing tool wear also. These techniques require milling machines with high mechanical and kinematic performance, as well as the absence of clearance between joints and a high acceleration capacity.

This study presents an investigation of the effects of structural characteristics, such as the layer height, infill density, top/bottom layer line directions and infill pattern, on the structural efficiency of Acrylonitrile Butadiene Styrene (ABS)-based specimens. The Fused Filament Fabrication (FFF) technique was utilized for the specimen fabrication, and the Ultimate Tensile Strength (UTS) and Strength-to-Mass (S/M) ratio were examined. The tests were planned according to the Central Composite Design (CCD), and an empirical model for each response was developed, with respect to the applied factors and their interactions. The analysis revealed that the characteristics with the strongest influence on the UTS and the S/M ratio were the infill and the layer height, respectively. Moreover, it was observed that the honeycomb structure contributed to the highest UTS compared to the other patterns. Finally, an optimization analysis based on the desirability function was performed, highlighting the combination of a 0.3 mm layer, 21.81% and 76.36% infill, 0° direction and the honeycomb pattern as the optimal for maximizing both UTS and S/M ratio under different desirability.