## 1 Introduction
AM permits precise replications of existing products and makes it possible to increase the functional performance with less weight [12; 13; 14; 15]. Furthermore, the integration of the functions in AM can reduce the need for assembly procedures. Additionally, the 3D printing of thermoplastic products with satisfying values of accuracy and strength is difficult as the process parameters significantly influence the properties of the obtained parts. This always requires other specific engineering knowledge [16]. However, one problem in the AM fabrication of customized orthoses is the requirement for specialized engineering knowledge to acquire and post-process the patient's wrist and to correctly model the obtained shape by the CAD systems [17]. In fact, the acquired patient's arm must be gathered and processed, usually manually, and this could generate many inaccuracies if the technician does not have specialized engineering knowledge. Also, there are many RE technologies with different features in terms of accuracy, cost, and ease of use, so it is fundamental to have accurate knowledge of this technology. For all these reasons, nowadays, the lack of dedicated and easy-to-use design software significantly limits the possibility of the widespread use of additively manufactured orthoses and can generate many inaccuracies. Furthermore, no design methods focus on orthosis design, which may lead to inefficient devices. Studies on how to make data gathering, processing, and manufacturing easier and more available in general medical practice have been carried out [18]. Different authors have proposed different solutions to improve and speed up the design process of personalized devices regarding the 3D acquisition or CAD modeling phases of orthoses or prostheses. The approaches proposed in the past, although interesting, are not ready to be used directly in clinical practice since the design of these devices requires significant interaction among medical staff and technical experts. Presently, there are a dearth of design methods specifically fitted to orthosis design, potentially resulting in suboptimal devices. Since the patient's medical condition, the body segment to be treated, and the kinematics of the orthosis represent unique characteristics, they deserve to be taken into consideration during the conceptual design phase of the orthosis itself [19]. Various efforts to enhance the design process of personalized devices have emerged. [PERSON] et al. [20] advocated for a simplified design and manufacturing process involving scanning one arm side for generating a splint, offering advantages like ventilation, cleanability, and lightness, albeit with limited patient protection. [PERSON] et al. [21] proposed a hybrid model with a patient-fitted inner structure and a standardized outer cover. [PERSON] et al. [22] explored automation to streamline the procedure. Other authors focused on the 3D acquisition or CAD modeling phases [23; 24; 25], yet these approaches, while intriguing, lack immediate clinical applicability, necessitating significant collaboration between medical and technical experts. Furthermore, this work delves into the use of additive manufacturing for orthosis realization, an ideal technology for mass customization. AM reduces the labor requirements compared to the traditional methods and excels in rapidly and cost-effectively fabricating intricate shapes [26; 27; 28; 29; 30], resulting in a polished surface finish dependent on the process parameters. The orthopedic field has witnessed an expansive integration of AM, offering flexible solutions for customized implants to specific shapes and sizes [31; 32]. This study proposes a novel approach to expeditiously design and fabricate personalized orthoses. The CAD modeling process is tailored for non-expert users, employing an automatic process rooted in generative design (GD). The GD approach was developed to overcome the problems that it is possible to encounter during the CAD modeling with the conventional software for AM [19].