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A persistent purpose for self-powered and wearable electronic devices is the fabrication of graphene-PVDF piezoelectric nanogenerators with various co-solvents that could provide enhanced levels of durability and stability while generating a higher output. This study resulted in a piezoelectric nanogenerator based on a composite film composed of graphene, and poly (vinylidene fluoride) (PVDF) as a flexible polymer matrix that delivers high performance, flexibility, and cost-effectiveness. By adjusting the co-solvent in the solution, a graphene-PVDF piezoelectric nanogenerator can be created (acetone, THF, water, and EtOH). The solution becomes less viscous and is more diluted the more significant the concentration of co-solvents, such as acetone, THF, and EtOH. Additionally, when the density is low, the thickness will be thinner. The final film thickness for all is ~25 µm. Furthermore, the- crystal phase becomes more apparent when graphene is added and combined with the four co-solvents. Based on the XRD results, the peak changes to the right, which can be inferred to be more dominant with the β-phase. THF is the co-solvent with the highest piezoelectric output among other co-solvents. Most of the output voltages produced are 0.071 V and are more significant than the rest.

The geometric tolerance of notching machines used in the fabrication of components for induction motor stators and rotators is less than 50 &micro, m. The blunt edges of worn molds can cause the edge of the sheet metal to form a burr, which can seriously impede assembly and reduce the efficiency of the resulting motor. The overuse of molds without sufficient maintenance leads to wasted sheet material, whereas excessive maintenance shortens the life of the punch/die plate. Diagnosing the mechanical performance of die molds requires extensive experience and fine-grained sensor data. In this study, we embedded polyvinylidene fluoride (PVDF) films within the mechanical mold of a notching machine to obtain direct measurements of the reaction forces imposed by the punch. We also developed an automated diagnosis program based on a support vector machine (SVM) to characterize the performance of the mechanical mold. The proposed cyber-physical system (CPS) facilitated the real-time monitoring of machinery for preventative maintenance as well as the implementation of early warning alarms. The cloud server used to gather mold-related data also generated data logs for managers. The hyperplane of the CPS-PVDF was calibrated using a variety of parameters pertaining to the edge characteristics of punches. Stereo-microscopy analysis of the punched workpiece verified that the accuracy of the fault classification was 97.6%.