Additive manufacturing (AM), or 3D printing, enables efficient fabrication of complex and customized components. Despite its growth across industries, users frequently encounter print failures due to design errors, process limitations, and inadequate monitoring. While existing research has explored various aspects of these failures, much of it remains fragmented, with limited consolidated overviews that map common problems, troubleshooting strategies, and guidelines across the AM workflow. This study conducted a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to identify and categorize common 3D printing problems and their solutions. Relevant studies published between 2000 and 2024 were extracted from major databases. A total of 126 peer-reviewed articles were selected and analyzed. Three major categories of recurring challenges were identified: (1) design and pre-processing errors; (2) geometric errors and dimensional deviations; (3) failures in in-process error detection and response. A variety of mitigation strategies have been proposed across the literature, including STL and slicing optimization, thermal management, machine calibration, and sensor-based real-time monitoring. These approaches reflect the multifactorial nature of 3D printing failures, which often arise from the complex interplay of design, material, and process parameters. This review provides a structured summary of failure types and mitigation strategies across the AM workflow.

The rapid growth of the mHealth market has led to the development of several tools to evaluate user experience. However, there is a lack of universal tools specifically designed for this emerging technology. This study was conducted with the aim of developing and verifying a user experience evaluation scale for mHealth apps based on factors proposed in previous research. The initial draft of the tool was created following a comprehensive review of existing questionnaires related to mHealth app evaluation. The validity of this scale was then tested through exploratory and confirmatory factor analysis. The results of the factor analysis led to the derivation of 16 items, which were conceptually mapped to five factors: ease of use and satisfaction, information architecture, usefulness, ease of information, and aesthetics. A case study was also conducted to improve mHealth apps concerning personal health records using this scale. In conclusion, the developed user experience evaluation scale for mHealth apps can provide comprehensive user feedback and contribute to the improvement of these apps.

Industry 4.0, as the fourth industrial revolution, represents significant challenges and numerous innovation opportunities for future product realization. A critical area of Industry 4.0 is the advancement of new design theories, design methods, and design principles to drive and enable the revolution with designers, engineers, teams, and organizations. This paper focuses on the advancement of a design theory and design principles for a growing manufacturing capability for Industry 4.0: additive manufacturing (AM). With high degrees of freedom, the field and use of AM requires design guidance and highly practical knowledge for supporting ideation processes, enabling understanding of capabilities, and creating a basis to innovative with the technology. Some design principles for AM exist in the literature; however, designers seek more fundamental and practical design guidelines for successfully creating and building their customized design artefacts, especially as Industry 4.0 moves forward. In this study, a crowdsourced repository for additively manufacturable components is used as the source of design data, within an empirical study, to extract practical design principles for AM. A total of 23 crowdsourced design principles for AM are extracted and clustered according to level specificity: (i) design for manufacturing, (ii) design for digital manufacturing, (iii) design for AM, and (iv) design for fused deposition modeling. These 23 AM design principles, as a foundation for AM design and Industry 4.0, are provided in a common framework; expressed for ready use by designers, developers, and researchers; and illustrated through some contemporary designs.

Online A/B testing is widely used as an experimental methodology for product improvement and business optimization. However, interpreting experimental results often involves subjective judgment and biases from experiment designers, which can undermine the reliability and reproducibility of test outcomes. In particular, experiment designers frequently exhibit inconsistent decision-making when dealing with neutral results—cases where neither statistically significant positive nor negative effects are observed. This study aims to explore the feasibility of automating A/B test decision-making using Generative AI and empirically analyze how well AI decisions align with those of experiment designers and experts. Utilizing 1,407 experimental cases from 48 companies on the Hackle online experimentation platform, the study compares decision-making outcomes between experiment designers and Generative AI, analyzing agreement rates and identifying patterns across companies. Statistical analyses, including chi-square tests and inter-rater agreement evaluation, were employed to assess differences and reliability. The findings indicate meaningful discrepancies between AI and experiment designers but demonstrate that AI decisions closely align with expert judgments. These results suggest that Generative AI can serve as a complementary tool to enhance the consistency and reliability of A/B test result interpretation.