In recent years, the rapid advancement of technology has caused an increase in the development of wearable products. These are portable devices that can be worn by people. The main goal of these products is to improve the quality of life as they focus on the safety, assistance and entertainment of their users. The introduction of many new technologies has allowed these products to evolve into many different fields with multiple uses. The way in which the design of wearable products/devices is approached requires the study and recording of multiple factors so that the final device is functional and efficient for its user. The current research presents an in-depth overview of research studies dealing with the development, design and manufacturing of wearable products/devices and applications/systems in general. More specifically, in this review, a comprehensive classification of wearable products/devices in various sectors and applications was carried out, resulting in the creation of eight different categories. A total of 161 studies from the last 13 years were analyzed and commented on. The findings of this review show that the use of new technologies such as 3D scanning and 3D printing are essential tools for the development of wearable products. In addition, many studies observed the use of various sensors through which multiple signals and data could be recorded. Finally, through the eight categories that the research studies were divided into, two main conclusions emerged. The first conclusion is that 3D printing is a method that was used the most in research. The second conclusion is that most research directions concern the safety of users by using sensors and recording anthropometric dimensions.

Computer technology influences the capability to enhance the functionality of manufacturing and product design technologies. Innovations in computational design and digital manufacturing empower designers and manufacturers to create novel representations and algorithms for designing, analyzing, and planning the production of highly complicated products achievable through state-of-the-art technology. Various principles, including computational physics, geometric reasoning, and automated spatial planning, enable engineers to generate entirely new categories of products in the footwear industry. This study aims to review the methods and tools that have been published in the literature for the last twenty years, and provide a better understanding of the parameters, tools, and controls that contribute to the design and manufacturing processes of shoes. The main focus is on highlighting the product design-related trends within the footwear industry. A structured framework becomes apparent in the literature through the grouping and presentation of information. This framework facilitates drawing conclusions about the trends and existing needs derived from in-depth research in the field of footwear. Additionally, it reveals the upcoming methods and tools that will contribute to the enhancement and development of this emerging and promising industry sector. In conclusion, the categorization limitation within the footwear industry could serve as the foundation for exploring key areas to be analyzed further in other industries, for instance, in furniture, clothing, and packaging.

Computational design together with the digitization of most fabrication processes play an important role in many research areas. Digital tools such as 3D modeling and computational design have been increasingly used. Computational design combines traditional 3D product design together with programming a general-purpose CAD system in order to promote system integration. In essence, using CAD-based textual or visual programming languages a series of products can be designed with accuracy and take advantage of product customization and automation of downstream applications. The present paper aims at customizing furniture design based on automating both the design and the fabrication procedures. The customer is able to define a series of geometrical characteristics, i.e., width, length, internal dimensions, and various other properties. The outcome consists of automating a great deal of processes, i.e., 3D modeling and assembling, visualization, creating the bill of materials (BOM), producing assembly instructions for the user, drawings and prototyping files, weight estimation.

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.

Materials are a mainstay of both industry and everyday life. The manufacturing and processing of materials is a very important sector as it affects both the mechanical properties and the usage of the final products. In recent years, the increased use of 3D printing and, by extension, its materials have caused the creation of gaps in terms of strength that require further scientific study. In this study, the influence of various printing parameters on 3D-printed specimens made of polyethylene terephthalate glycol (PETG) polymer was tested. More specifically, three printing parameters were selected—infill, speed, and type—with three different values each (50%, 70%, and 90%), (5 mm/s, 20 mm/s, and 35 mm/s) and (Grid, Rectilinear, and Wiggle). From the combinations of the three parameters and the three values, 27 different specimens were obtained and thus, 27 equivalent experiments were designed. The measurements were evaluated, and the process was modeled with the Artificial Neural Network (ANN) method, revealing a strong and robust prediction model for the tensile test, with the relative error being below 10%. Both infill density and infill pattern were identified as the most influential parameters, with the Wiggle type being the strongest pattern of all. Additionally, it was found that the infill density acts increasingly on the strength, whereas the printing speed acts decreasingly.