The increased adoption of three-dimensional (3D) digital prototyping software programs makes it necessary to train novice designers to use these programs efficiently. However, existing studies spanning from engineering to design education indicate that students feel incompetent in understanding 3D digital prototypes and navigating the software, so there is a need to find effective training methods. In the current study, training modules were developed to teach participants fit correction skills through an iterative problem-based learning (PBL) approach. A review of the literature was performed to develop the fit correction tasks and guide the module development process. Expert feedback was used to fine-tune the tasks and module interface. The current study explored the effects of PBL-based virtual reality (VR) training on learning how to correct two-dimensional (2D) apparel patterns to improve consequent 3D garment fit. Results indicated that the training module significantly improved spatial visualization and fit correction skills. Participants with higher apparel spatial visualization skills saw a higher improvement in fit correction skills because of the training. At lower spatial visualization skill levels, women saw a higher increase in apparel spatial visualization skills after the training than men but the difference between the learning outcomes across genders reduced when participants had higher spatial skills before training. These findings were supported by the results of qualitative data obtained through interviews. The participants found the PBL approach, immediate feedback, and aid in visualization through garment simulations beneficial for understanding the concepts. At the same time, they indicated that the training module could be used as a supplement to the traditional classroom but cannot replace the physical garment fitting practice. The findings of the study verified that PBL using virtual garment simulations can have a positive impact on learning outcomes and help identify the stage of education at which learners can be exposed to PBL.

Abstract In fashion design education, students are taught how to prepare 2D patterns to create 3D garments through flat pattern making and draping courses. However, there is a lack of focus on garment fitting, and students are expected to have a natural intuition regarding fit corrections. As a result, most students graduate without gaining the crucial skill of reading misfit signs in 3D and acting on correcting 2D patterns accordingly. For this reason, in the current study, we developed a novel skill test instrument based on the Extended Skill Cycle Model using 3D simulations to quantify students' fit correction skills. We evaluated the instrument's reliability and validity for its suitability in teaching and research applications. Fit correction questions with virtual garment simulations were designed, and participants were provided with three potential pattern modification options, out of which one option corrected garment fit. The reliability of the questionnaire was determined using Cronbach's alpha, and validity was analyzed by comparing pre- and post-training scores, comparing scores of participants with different levels of experience, and calculating the item difficulty and discrimination index. There was a positive correlation between the pre- and posttraining test scores, confirming that the instrument could measure the increase in skill level. In addition, the pre-training scores of participants at the very minimum patternmaking experience level were lower than those of participants with higher experience levels. Therefore, our instrument could be used in patternmaking courses in fashion design education to measure student skill development.

Apprenticeship learning (AL) aims to induce decision-making policies by observing and imitating expert demonstrations.Existing AL approaches typically rely on online interactions and assume that the demonstrations follow a single reward function.Nevertheless, in real-world human-centric applications, policies are usually learned in an offline setting, with the demonstrations driven by multiple reward functions that evolve over time.To address these challenges, we introduce a novel AL framework: Time-aware Hierarchical EM Energy-based Sub-trajectory (THEMES) clustering.We evaluate the effectiveness of THEMES in two challenging human-centric domains -healthcare and education.Our experimental results across multiple datasets demonstrate that THEMES can accurately induce policies, outperforming competitive baselines and ablations, demonstrating its potential for tackling a broad range of complex, real-world human-centric tasks.

Fit sessions are essential in making well-fitted clothing. During these sessions, apparel fit is determined iteratively by a team of designers, technical designers, fit models, and merchandisers. With the advent of the digitalization of new product development processes in the fashion industry, fit sessions have been seen as a bottleneck for they are still held in person. However, the process is currently still irreplaceable, and what makes the feedback provided by fit models important is an area that has never been tapped. Therefore, the present study aimed to understand fit models' role in fit sessions as well as how they assess the fit of garments and deliver their feedback. On-site observation during fit sessions and individual interviews with fit models were conducted. It was found that fit models gave comfort, fit, and tactile comments by testing garments while standing and moving between several postures. They had knowledge that overlapped with those of technical designers and designers and this empowerment enabled them to take part in the decision-making process in fit sessions. It also was found that fit models' feedback on garments was essential as they were the first people to try on the garments and present the customers' points of view.

The CLOTECH Conference – organized every two years – is focused on the popularization of achievements of the worldwide scientific institutions in a range of directions for innovation and development of protective clothing, gloves and footwear, smart textiles and clothing, computer techniques in designing and making up clothing, mass customization and rapid prototyping, marketing and competitiveness of textiles in the European market, standardization and certification of textiles and clothing. The first CLOTECH conference was organized in 2003 on the national scale in Poland. In 2008 the CLOTECH conference gained the status of an international event and from this time successfully continue the communication in science and industry. The 2024 Conference was held 5-6 September in Dresden, Germany and was organized by the Chair of Development and Assembly of Textile Products, ITM, TU Dresden under the guidance of Prof.Dr. Yordan Kyosev. We were happy to welcome 116 participants from17 Countries: Germany, Ukraina, USA, Bulgaria, Portugal, Iran, Kenia, Poland, Romania, Niederland, Latvia, Ethiopia, Tunesia, Czech Republik, United Kingdom, Greece, Lithuania, Bangladesch, which presented 46 oral and 21 poster presentations. Selected papers of the conference will be published after successfull review process in the Diamond Open Access Journal 'Communications in Development and Assembling of Textile Products' www.cdatp.org

Three-dimensional (3D) technologies such as 3D digital prototyping have recently been used in apparel engineering to reduce the number of costly physical prototypes. This technology can simulate garment drapes by digitally sewing 2D patterns and placing them on a 3D avatar. Yet, current limitations in simulation accuracy mean physical prototypes are still indispensable. Integrating AI technologies shows promise in accelerating digital transformation in the apparel industry. AI can support decision-making in apparel design, size and fit prediction, and fashion recommendations. It can also assist decision-making in technical design by predicting garment fit before making physical prototypes or offering solutions for misfit areas. Research indicates that models such as Naive Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) offer robust solutions for fit evaluation. However, they require further testing across diverse garment styles and demographics. Developing a nuanced classification system for fit conditions could improve model accuracy, ultimately supporting the industry's shift towards digital and AI-enhanced product development. In this study, we propose a framework to achieve such a system.

Apparel product development is an iterative problem-solving process that is heuristic in nature and involves turning 2D flat patterns into a 3D garment that would fit human anatomy. The digital transformation trend in the apparel industry will accelerate in the post-pandemic era. Therefore, it is crucial to better understand the dynamics as well as the types of information generated during apparel product development to translate this information into the digital realm to better support the apparel industry. Technical apparel designers acquire important knowledge on garment fit during their years in the workforce. Documenting their knowledge could assure that the company's know-how would be saved and used to train the future workforce. Technical designers' problem-solving strategies can be tracked, coded, and made a part of artificial intelligence (AI) technology for product development sessions. This would help strengthen the competitiveness of both existing and new design companies. However, the knowledge possessed by the technical designers to enable much-improved digitalization would require a thorough analysis of tacit or implicit knowledge, which is acquired through years of experience. Due to the nature of how apparel designers learn by directly manipulating materials with their hands, this knowledge type is challenging to document. Nonetheless, if a successful method can be developed to document this knowledge, it would be very rewarding, especially for organizational knowledge management, and allow companies to digitalize using AI.