The lubrication of the mechanical components reduces friction, and increases the efficiency and the reliability. However, the interaction of moving components with the lubricant leads to power losses due to viscous and inertial effects. Nowadays, the study of lubricant behavior can be carried out through computational fluid dynamics (CFD) simulations. Nevertheless, the modeling of the computational domain within complex mechanical systems (e.g., ordinary, planetary and cycloidal gearboxes, roller bearings, and pumps) requires the exploitation of specific CFD techniques. In the last decades, many mesh-based or meshless approaches have been developed to deal with the complex management of the topological changes of the computational domain or the modeling of complex kinematics. This paper aims to collect and to classify the scientific literature where these approaches have been exploited for the study of lubricated mechanical systems. The goal of this research is to shed a light on the current state of the art in performing CFD analysis of these systems. Moreover, the objective of this study is to stress the limits and the capabilities of the main CFD techniques applied in this field of research. Results show the main differences in terms of accuracy achievable and the level of complexity that can be managed with the different CFD approaches.

Design is inherently affected by human-related factors and it is of no surprise that the fine-tuning of instruments capable of measuring aspects of human behavior has attracted interest in the design field. The recalled instruments include a variety of devices that capture and quantitatively assess people's unintentional and unconscious reactions and that are generally referred as neurophysiological or biometric. The number of experimental applications of these instruments in design was extremely limited as of 2016, when Lohmeyer and Meboldt published a first report on relevant measures and their interpretation in design. In the last few years, the number of relevant publications has increased dramatically and this determines the opportunity to carry out a comprehensive review in the field. The reviewed contributions are analyzed and classified according to, among others, instruments used, the kind of stakeholders involved and the supported design research activities. The role of biometric measures with respect to traditional research methods is emphasized too. The discussed instruments can represent supports or substitutes for traditional approaches, as well as they are capable of exploring phenomena that could not be addressed hitherto. The intensity of research concerning experiments with biometric measurements is discussed too; a particular focus of the final discussion is the individuation of obstacles that prevent them from becoming commonplace in design research.

In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

Idea generation is acknowledged to benefit from intentionally administered stimuli or designers’ processes that include the search for external sources of inspiration. Text-based and graphic forms of stimuli are the most leveraged in design literature, but it has not been yet demonstrated which form is most effective for boosting creativity. This is due to the fact that previous studies have employed many varying conditions which do not allow for comparisons to be made. The present paper presents an experiment in which three groups of 27 participants were asked to generate new ideas for new-borns’ outfits. To perform the task, the participants first considered five stimuli presented to them in one of the forms depending on which group the participants had been assigned to, i.e. textual, pictorial or combined (juxtaposition of the two). The stimuli were intended to share the semantic content, thus limiting potential bias due to different meanings. The outcomes of the experiment were evaluated in terms of creativity and non-obviousness. The presence of a pictorial dimension resulted in a significant increase in terms of rarity and non-obviousness of ideas, but did not affect quality, originality or quantity. The limited overlap among ideas emerging from the three forms suggests the potential value of developing design tools for idea generation that mix multiple forms of stimuli.

Previous studies have failed to provide a comprehensive view on the value perception of green products. The present research takes up this challenge through an experiment in which 43 participants have interacted with and evaluated 40 products—20 baseline products and 20 green products of the same categories. The experiment included both self-assessments to monitor conscious evaluations of the products and biometric measurements (Eye-Tracking and Galvanic Skin Response) to capture unconscious aspects. The results show that different forms of perceived value emerge clearly. Green products, for which participants required greater efforts in the search for relevant information, boost the value attributed to creative solutions still believed of high quality. This effect is significantly more evident for participants showing remarkable interest for sustainability issues. Conversely, alternative products feature greater value perception because they are acknowledged to be functional and reliable.