This paper presents a novel process to fabricate piezoelectric films from polyvinylidene fluoride (PVDF) polymer using integrated fused deposition modeling (FDM) 3D printing and corona poling technique. Corona poling is one of many effective poling processes that has received attention to activate PVDF as a piezoelectric responsive material. The corona poling process occurs when a PVDF polymer is exposed to a high electric field created and controlled through an electrically charged needle and a grid electrode under heating environment. FDM 3D printing has seen extensive progress in fabricating thermoplastic materials and structures, including PVDF. However, post processing techniques such as poling is needed to align the dipoles in order to gain piezoelectric properties. To further simplify the piezoelectric sensors and structures fabrication process, this paper proposes an integrated 3D printing process with corona poling to fabricate piezoelectric PVDF sensors without post poling process. This proposed process, named ‘Integrated 3D Printing and Corona poling process’ (IPC), uses the 3D printer’s nozzle and heating bed as anode and cathode, respectively, to create poling electric fields in a controlled heating environment. The nozzle travels along the programmed path with fixed distance between nozzle tip and sample’s top surface. Simultaneously, the electric field between the nozzle and bottom heating pad promotes the alignment of dipole moment of PVDF molecular chains. The crystalline phase transformation and output current generated by printed samples under different electric fields in this process were characterized by a Fourier transform infrared spectroscopy and through fatigue load frame. It is demonstrated that piezoelectric PVDF films with enhanced β -phase percentage can be fabricated using the IPC process. In addition, mechanical properties of printed PVDF was investigated by tensile testing. It is expected to expand the use of additive manufacturing to fabricate piezoelectric PVDF-based devices for applications such as sensing and energy harvesting.

This paper presents a fabrication process to enhance homogeneous dispersion of BaTiO₃ nanoparticles in polyvinylidene fluoride matrix nanocomposites using fused deposition modeling (FDM) 3D printing technique. The nanocomposites integrate the functional property (piezoelectric, pyroelectric, and dielectric) of BaTiO₃ with the flexibility and lightweight of polyvinylidene fluoride. Traditionally, the simple yet effective way to fabricate the nanocomposites includes solvent-casting, spin-coating, and hot-embossing. However, these methods have disadvantages such as heterogeneous dispersion of BaTiO₃ nanoparticles in polyvinylidene fluoride matrix due to the higher density of BaTiO₃ compared with polyvinylidene fluoride and agglomeration during fabrication process. This heterogeneous dispersion could weaken functional and mechanical properties. Herein, fused deposition modeling 3D printing technique was utilized for homogeneous dispersion to alleviate the agglomeration of BaTiO₃ in polyvinylidene fluoride through two processes: filament extrusion and 3D printing. In addition, thermal poling was applied to further enhance piezoelectric response of the BaTiO₃/polyvinylidene fluoride nanocomposites. It is found that 3D printed BaTiO₃/polyvinylidene fluoride nanocomposites exhibit three times higher piezoelectric response than solvent-casted nanocomposites.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.

Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies.

Purpose: Shape memory polymer (SMP) is capable of recovering its original shape from a high degree of deformation by applying an external stimulus such as thermal energy. This research presents an integration of two commercial SMP materials (DiAPLEX and Tecoflex) and a material extrusion (ME) printer to fabricate SMP parts and specimens. The material properties such as Young’s modulus of the specimens was examined as a process output. Furthermore, stress-strain curve, strain recovery, instant shape-fixity ratio, long-term shape-fixity ratio and recovery ratio of SMP specimens during a thermo-mechanical cycle were investigated. Design/methodology/approach: The ME fabrication settings for the SMP specimens were defined by implementing a design of experiments with temperature, velocity and layer height as process variables. Findings: It was found, according to main effect and iteration plots, that fabrication parameters have an impact on Young’s modulus and exist minimum iteration among variables. In addition, Young’s modulus variation of DiAPLEX and Tecoflex specimens was mostly caused by velocity and layer height parameters, respectively. Moreover, results showed that SMP specimens were able to recover high levels of deformation. Originality/value: This paper is a reference for process control and for rheological properties of SMP parts produced by ME fabrication process.