As manufacturing transitions from Industry 4.0 to Industry 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social manufacturing to achieve sustainability goals. This transition reflects a paradigmatic shift from a technology-centric model focused on automation and efficiency toward a more holistic framework that embeds human-centricity and environmental responsibility into industrial systems. Whereas Industry 4.0 emphasizes digital innovation and productivity, Industry 5.0 seeks to align technological advancement with broader ecological and societal objectives. Despite advancements in automation and digitalization, existing frameworks lack a structured approach to leveraging GAI for environmental, social, and economic sustainability. This study explores the transformative role of GAI in adaptive social manufacturing, addressing the gap in the existing frameworks. Employing a multi-method research design, including content analysis, expert-driven validation, and system dynamics modeling, the study identifies nine key sustainability dimensions of Industry 5.0 and maps them to 17 GAI functions. The findings reveal that GAI significantly enhances adaptive social manufacturing by optimizing resource efficiency, promoting inclusivity, and supporting ethical governance. System dynamics analysis highlights the complex interdependencies between GAI-driven functions and sustainability outcomes, underscoring the need to balance technological innovation with human values. The research provides a novel framework for industries seeking to implement GAI in sustainable production systems, bridging theoretical insights with practical applications. Additionally, it offers actionable strategies to address challenges such as workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0’s sustainability goals.

This study aims to prioritize the concept of value in the context of Industry 5.0, emphasizing a shift from traditional efficiency metrics to a focus on interconnected value dimensions. The primary objective is to explore how the evolution of value perspectives through industrial revolutions has influenced the strategic direction of Industry 5.0. A systematic approach was adopted, combining an extensive literature review, content analysis, and Interpretive Structural Modeling (ISM). This methodology was employed to create a strategic roadmap and a value model that elucidates the interrelationships among the core components of value in Industry 5.0. The study finds that the concept of value has undergone significant transformations across industrial eras, culminating in Industry 5.0, which presents a distinct and human-centric definition. The research identifies critical value dimensions that interconnect human welfare, sustainability, and technological progress. Through ISM, the study outlines how these elements interact and influence industrial practices, offering a fresh perspective on value prioritization. The findings offer valuable insights for policymakers, businesses, and organizations to realign their strategies and activities with the evolving values of Industry 5.0. Emphasizing human-centric technology and sustainability, the study provides actionable guidelines to enhance performance and ensure that organizational goals contribute to the broader objectives of Industry 5.0. This research introduces a novel perspective by integrating humanities and a systems-based outlook in understanding the role of value in industrial and social development. It highlights the transformative impact of Industry 5.0, marking a significant departure from previous industrial paradigms. The study’s originality lies in its comprehensive approach to prioritizing value dimensions and its potential to guide both academic research and industrial practices.

Every new brand which competes and increases its market share needs some tools and methods to be applied to its products. The objective of this re-search is to investigate the use of metaphor as a tool for peak shift effect and its impact on memory, which can be effective in promoting brand identity from a customer perspective. In this respect, this research uses a combina-tion of memory test and comparison of two objects method. Therefore, the research method was according to environment specifications. The results indicated that the products having metaphor in comparison to products with no metaphor has an effective role in peak shift and image persistence and its name in user’s mind. In conclusion, the brand identity can be promoted in a variety of ways, among which the design of metaphor, which is related to product design and graphic design, is one of these ways

This research investigates differences in visual form perception of products in individualist and collectivist societies to help designers and market analysts design products according to their users’ formal preferences. This study was conducted in two phases. First, five visual form types were selected out of 500 samples by design expertise for the test: geometric, organic, complex, simple, and symbolic forms. Second, three groups of American (highly individualist), German (individualist), and Iranian (collectivist) were selected based on their cultural dimensions. We developed Exploratory Factor Analysis (EFA) with the emotions to focus solely on limiting emotions. Since the emotions were in two opposite spectrums, four emotions were considered as positive and the next four emotions as negative. ANOVA was conducted for normally distributed data and Kruskal–Wallis test for non-normal data. The results of this research showed that people with different cultural dimensions have different reactions, feelings, and preferences to products with different formal features. While individualists embrace geometric, simple, and symbolic forms, collectivists prefer organic forms most. The complex form is rejected by all three groups. This research extends our knowledge about cultural differences and provides a very critical tool for designers to design their products according to their users’ emotions.

This study examines the use of injection moulding to evaluate mechanical properties in plastic products, such as shear and residual stresses. Key process variables like melt temperature, mould temperature, hold pressure duration, and pure hold duration are meticulously chosen for study. A full factorial experiment design is utilised to determine the best settings. These variables notably influence the end product’s physical and mechanical properties. Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. A CAD model of a dashboard part is incorporated into a finite element analysis to measure shear and residual stresses. Four specific parameters from the injection moulding process are subjected to an in-depth experimental design. It is worth noting that the injection moulding process does not incorporate a type-2 fuzzy neural network (T2FNN). However, in this particular investigation, T2FNN was employed to replicate the mechanical stress model associated with dashboard injection moulding. Its purpose was to estimate shear and residual stress levels. Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. The proposed model was developed and implemented using MATLAB software. A Pareto front was derived from the MOGA by employing the T2FNN within the process, identifying fourteen optimal solutions.

Design thinking is defined as using strategies from the field of design to solve problems in the disciplines that are traditionally not considered design.