Fused Deposition Modelling (FDM) is a popular additive manufacturing (AM) technique that converts three-dimensional computer-aided design (CAD) models into complex-shaped objects using thermoplastic filaments as a raw material. Existing recyclable plastics, such as Acrylonitrile Butadiene Styrene (ABS), Polyetherimide (PEI), Polyether ether ketone (PEEK), Polylactic Acid (PLA), Polyethylene Terephthalate (PET), and Nylon, can be used to fabricate AM components as alternative for commercially available filaments, as they are a significant source of waste and widely available. This study presents a method of converting waste polymers into filaments for 3D printing. A dual-material extrusion system capable of processing both recycled PET and PLA under identical conditions was developed using a band heater, Proportional-Integral-Derivative (PID) temperature controller, motor, extruder screw and hopper, etc. The experiments were performed to produce the 3D filaments using recycled plastics (in the form of PET) and PLA pellets, and the results were compared. For recycled PET, waste plastics were collected, shredded, and passed through the hopper. The material was melted in the heating chamber at a temperature range of 150–220 °C for PET and 180–210 °C for PLA. The auger bit’s rotatory action delivered the molten material to the nozzle, where the final filament was extruded. The tensile strength of PLA specimens (44 ± 3.6 MPa) was found ~11% higher than PET (39 ± 3.2 MPa). Whereas, the percentage elongation of PET and PLA specimens were found as 4 and 5.2% respectively. This performance affirms that recycled PET, when appropriately processed, can bridge the gap between environmental responsibility and mechanical functionalit y in 3D printing applications. This model will likely help industries improve their efficiency and performance by lowering the cost of the new materials and making it more efficient for them. The produced filament can create personalized accessories, household goods, and distinctive, tailored fashion products.

Predicting stress fIelds of porous metamaterials under arbitrary rotations is essential for reliable online monitoring in additive manufacturing. However, existing neural operators are limited by two major issues: geometric orientation bias where identical lattices are misinterpreted under rotation, and non-physical stress leakage caused by the failure of Partial Differential Equation (PDE) constraints at discontinuous void-solid interfaces in porous metamaterials. To address these challenges, we propose the Symmetry-Imbued Hamiltonian Neural Operator (SIHNO) that embeds geometric symmetry and an energy-structured inductive bias into a unified neural operator architecture. Specifically, SIHNO introduces a Rotation-Steerable Lattice Projector (RSLP) that lifts lattice images into a continuous-angle geometric symmetry representation. This geometric representation is further coupled with a Hamiltonian-Fueled Propagator (HFP) that replaces local PDE constraints with Hamiltonian-inspired energy propagation. Finally, a slice-aware convolutional decoder reconstructs stress fields based on RSLP and HFP. Comprehensive experiments demonstrate that SIHNO outperforms existing neural operators. The proposed framework provides a robust architecture for stress-field prediction in additive manufacturing.

Lean manufacturing is now called Industry 4.0, in which traditional production tools are data-driven and have implications for corporate sustainability. Jidoka (JIDO) has been transformed into JIDO 4.0, which employs Internet of Things (IoT) systems and sensors to gather data from the production process for real-time monitoring and decision making. Based on three hypotheses, this study proposes a structural equation model (SEM) to examine the relationships between JIDO 4.0, digital sustainability (DISU), and environmental sustainability (ENSU) in manufacturing companies. The SEM was tested with data from 834 responses to a questionnaire for managers, engineers, and supervisors, validated using Lawshe’s content validity ratio and Aiken’s V. The Warp3 algorithm in WarpPLS 8.0 was used to detect nonlinear relationships between constructs. The results indicate that the three proposed hypotheses are supported, showing that JIDO has the greatest direct effect on DISU (β = 0.588), and DISU is the most influential predictor of ENSU (β = 0.553). The indirect effect of JIDO on ENSU, mediated by DISU (β = 0.325), was nearly equal to the direct effect (β = 0.342), indicating that DISU acts as a bridge between intelligent autonomy and environmental benefits. A sensitivity analysis based on conditional probabilities indicated that when DISU was high, the probability of achieving a high ENSU was 64.8%. These results indicate that JIDO is a data-driven organizational capability with direct and indirect effects on ENSU, and that it provides managers with empirical evidence to prioritize their investments in smart and lean technology.