Mihael Debevec

Existing pneumatic artificial muscle (PAM) static geometrical models based on the principle of virtual work provide only approximate force predictions since they neglect the effects of volume change and radial bladder deformation work loss. In this study, we propose an improved geometrical static model called the Accurate Volume and Bladder Deformation Loss (AVBDL) model. This model introduces a physically consistent calculation of muscle volume at different contractions and pressures and incorporates a new way of describing work losses due to radial deformation of the bladder. The hyperelastic properties of the bladder were experimentally characterized and modeled using the Mooney–Rivlin formulation. The AVBDL model was validated against experimental data from four types of pneumatic muscles and compared with three established analytical models. Results show that the AVBDL model significantly improves force prediction accuracy, achieving a normalized root mean square (NRMS) error of 6.7–16.4%, compared to 20–68% for existing models. Due to its analytical transparency, reduced error, and broad applicability, the AVBDL model provides a robust basis for accurate simulation and control of pneumatic artificial muscles.

Digital twin technology has proven to be a transformative enabler for sustainable manufacturing by providing real-time virtual representations of physical assets and supply chain processes. This paper explores the integration of digital twins with agile supply chain strategies to improve the sustainability of manufacturing systems. By leveraging real-time data and advanced simulations, digital twins facilitate dynamic decision making, optimize resource utilization and reduce environmental impact. A case study is presented in which a digital twin is implemented with the aim of improving the responsiveness of agile supply chains and suggesting appropriate times for the delivery of components and the shipment of the final product, with the goal of minimizing the time components spend in warehouses. The analysis shows how digital twins improve clarity, adaptability and predictive capabilities, leading to greater efficiency and sustainability. The results show that the combination of digital twin technology and agile supply chain frameworks contributes significantly to resource optimization, emissions reduction and overall operational resilience. The proposed approach proves to be highly effective for various manufacturing environments, especially those that strive to balance efficiency and sustainability goals.

Digital twin technology has proven to be a transformative tool in the development of smart factories by providing real-time data-driven simulations and virtual representations of physical assets and systems. This paper explores the integration of digital twins in the context of Industry 4.0 and sustainability, highlighting their potential to increase operational efficiency, optimize material usage and minimize waste. To demonstrate these benefits, case studies are presented in which a digital twin of a real manufacturing system was implemented. The digital twin enabled us to run different “what-if” scenarios to evaluate the improvements in the manufacturing system, efficiency and reduction of raw material consumption and waste by incorporating quality control operations at all assembly stations. Based on the analyzed results of both case studies, we demonstrated that additional quality control operations had a significant impact on the efficiency of the manufacturing system and its sustainability, as waste was significantly reduced. The proposed approach has proven to be highly effective for different types and sizes of manufacturing systems, especially those with high waste generation.