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.

Machine tool vibrations have great impact on machining process. In this paper the dynamic behavior and modal parameters of milling machine is presented. For this purpose, the CAD model of the milling machine structure is provided in CATIA and then Natural frequencies and mode shapes of the machine tool structure are carried out through FEM modal analysis under ANSYS Workbench. The model is evaluated and corrected with experimental results by modal testing on FP4M milling machine. Finally, the natural frequencies and mode shapes obtained by both experimental and FEM modal analysis are compared. The results of two methods are in widely agreement.

The purpose of this research is to investigate different milling parameters for optimization to achieve the maximum rate of material removal with the minimum tool wear and surface roughness. In this study, a tool wear factor is specified to investigate tool wear parameters and the amount of material removed during machining, simultaneously. The second output parameter is surface roughness. The DOE technique is used to design the experiments and applied to the milling machine. The practical data is used to develop different mathematical models. In addition, a single-objective genetic algorithm (GA) is applied to numerate the optimal hyperparameters of the proposed adaptive network-based fuzzy inference system (ANFIS) to achieve the best possible efficiency. Afterwards, the multi-objective GA is employed to extract the optimum cutting parameters to reach the specified tool wear and the least surface roughness. The proposed method is developed under MATLAB using the practically extracted dataset and neural network. The optimization results revealed that optimum values for feed rate, cutting speed, and depth of cut vary from 252.6 to 256.9 (m/min), 0.1005 to 0.1431 (mm/rev tooth), and from 1.2735 to 1.3108 (mm), respectively.

Knowing how a musical instrument vibrates can benefit the tonal characteristics shaping of the instrument. In this research, an approach for investigating the mode shapes and natural frequencies of Setar body is addressed. First, mechanical properties of wood used in the production of Setar are analyzed experimentally. Then a numerical modal test is performed to find the mode shapes and natural frequencies of Setar structure. To validate the results obtained by the numerical method, experimental modal testing is also done for the structure, and it is found that the results of both the methods are in good consistency. As the vibration pattern of plates is of utmost importance in the production of musical instruments, vibration patterns of a Setar plate are experimentally extracted and the results are compared with finite element analysis.