Van-Long Trinh

Finding a new clean energy power is a meaningful solution for the energy paucity and environmental protection. This paper introduces the microstructured PDMS surface (Mi-PDMS) for triboelectric nanogenerator (TENG) to harvest mechanical energy into electrical energy. The micro/nano structured surfaces have ability of enhancing friction and deformation that is the key to improve output performance of the TENG. Triboelectric nanogenerator, a modern energy harvesting technique, produces electricity through cyclic contact behavior of the triboelectrical materials by upshot of electrostatic induction and electrification. This power production method shows marvellous attributes of clean, light-weight, flexibility, portability, and stability. TENG has recently received significant attention from research groups to develop and construct models to harvest mechanical energy into electrical energy for practical applications such as portable electronics, lighting diode bubbles, sensors, charging, and self-powered electrical devices. Polydimethylsiloxane (PDMS) is an engineering polymer with negative triboelectric, transparent, non-toxic, and biocompatible features. PDMS has been used for many practical applications such as bio-applications, harvesting energy, medical treatment, and micro-electro-mechanical systems (MEMS) devices. By introducing the Mi-PDMS, the TENG attitudes the output performance of the current of 1.5 µA and the voltage of 18V, respectively. The work also expresses the working mechanism, the electrical induction, governing equations, and applications of the TENG.

Product quality is a very important criteria of a production process such as injection molding process. There are still fierce defects that exist on the molded product such as shrinkage, weldlines, and warpage. To improve product quality of injection molding process, it is clear that defects have been eliminated out of molded parts. The shrinkage is occurred and appeared on the final product during injection molding process that related to injection processing parameters such as injection pressure, melting temperature, filling time, and mold temperature. This paper demonstrates an optimal method of processing parameters by using Taguchi method and RSM technique to minimize the shrinkage of product during injection molding process. The product material is acrylonitrile butadiene styrene (ABS) with good characteristics of toughness, strength, and chemical resistance. The shrinkage analysis and simulation tasks are conducted by using EasyFill software. The results show that the optimal shrinkage can be reached. The optimal shrinkage is lower about 0.52% in comparison to the minimal value of proposal experiments and about 22.14% the maximum value of the experimental matrix. The paper hopes that the results are useful for engineers and practical production in the near future.

Injection molding process is the mass production type to produce vast amount of plastic product for many practical applications such as aerospace, automotive, electronics, and household equipment. Pressure-volume-temperature (PVT) behavior is one of the most important pressure-volume-temperature relationships of polymer material for injection molding process to meet product quality such as weight index and minimization of defects of warpage, shrinkage, weldlines, short-shot, and air traps. PVT behavior has strongly affected by the cooling rate during the injection molding process with the fierce phase transition of molten polymer material from amorphous state to crystalline state. The relationship is actually difficult to control to improve the product quality during injection molding process because of there are many processing conditions participated into the process such as injection pressure, melt temperature, cooling time, and mold temperature. To form a solid product by processing method using an injection mold, the plastic material has undergone severe states of deformation under varying of pressure, volume, and temperature that related to PVT behavior during injection molding process. This paper demonstrates a PVT behavior of polymer material to improve product quality during injection molding process for sustainable manufacturing process. The simulation system was performed with a sample with the maximum length of 40.8 mm, the maximum width of 11.5 mm, the base thickness of 2.6 mm, and the injecting material as a high-density polyethylene (HDPE). The basic processing factors include melt temperature of 210°C, injection pressure of 33.92 bar, and cooling time of 12 s. The injection molding process was conducted by using EasyFill software. The results were collected with useful information for sustainable manufacturing including good moldability, the filling flow rate of 1575.9 mm3/s in 0.13 s at the filling stage, the filling time of about 1.058 s, the cooling time of 12.013 s, the minimum volume shrinkage of 0.32%, and the maximum warpage of 0.125 mm. The defects can be identified for designer and engineer who will find solutions to reduce it for improving the product quality and reaching sustainable injection molding process. The results are crucial reference for manufacturers in the polymer processing such as injection molding process

The quadrotor is an extensively researched and popular unmanned aerial vehicle in various fields, with navigation being one of the most widely studied areas. This paper focuses on setting the path for the quadrotor to move in a predefined 3D map environment. A real-time mathematical model for the quadrotor’s motion was developed, incorporating the moment of resistance and the rotor rolling coefficient using improved Lagrange dynamic modeling. Two navigation scenarios were then built using Dijkstra and A star (A*) algorithms to evaluate the efficiency of the mathematical model based on criteria such as distance, time, and speed. The results indicate that Dijkstra’s algorithm resulted in a 0.55% shorter travel distance for the quadrotor than the A* algorithm, while the quadrotor’s flight time of the A* algorithm is 0.52% longer. However, the maximum speed of the quadrotor guided by the A* algorithm is 7.87% higher than that of Dijkstra’s algorithm. These findings provide a basis for developing an optimal navigation strategy for the quadrotor to meet specific requirements.

Smart devices and emerging technologies are highly popular devices and technologies that considerably improve our daily living by reducing or replacing human workforces, treating disease, monitoring healthcare, enhancing service performance, improving quality, and protecting the natural environment, and promoting non-gas emissions, sustainable working, green technologies, and renewable energy. Triboelectric nanogenerators (TENGs) have recently emerged as a type of advanced energy harvesting technology that is simple, green, renewable, flexible, and endurable as an energy resource. High-performance TENGs, denoted as advanced TENGs, have potential for use in many practical applications such as in self-powered sensors and sources, portable electric devices, power grid penetration, monitoring manufacturing processes for quality control, and in medical and healthcare applications that meet the criteria for smart devices and emerging technologies. Advanced TENGs are used as highly efficient energy harvesters that can convert many types of wasted mechanical energy into the electric energy used in a range of practical applications in our daily lives. This article reviews recently advanced TENGs and their potential for use with smart devices and emerging technology applications. The work encourages and strengthens motivation to develop new smart devices and emerging technologies to serve us in many fields of our daily living. When TENGs are introduced into smart devices and emerging technologies, they can be applied in a variety of practical applications such as the food processing industry, information and communication technology, agriculture, construction, transportation, marine technology, the energy sector, mechanical processing, manufacturing, self-powered sensors, Industry 4.0, drug safety, and robotics due to their sustainable and renewable energy, light weight, cost effectiveness, flexibility, and self-powered portable energy sources. Their advantages, disadvantages, and solutions are also discussed for further research.

Selecting the optimal industrial robot is a crucial factor in enhancing production efficiency and reducing operational costs. However, conflicts among evaluation criteria make this process challenging, requiring a structured decision-making approach. The present study proposes a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Additive Ratio Assessment (FARAS). The model consists of six main steps: constructing the pairwise contribution matrix from experts regarding the relationships between criteria, developing the fuzzy pairwise comparison matrix for weights, determining the fuzzy weights of each criterion, obtaining expert evaluations of the alternatives, normalizing the data, and ranking the alternatives. Industrial robot selection must achieve a balance between performance and cost considerations. High-performance robots offer superior accuracy and reliability but come with high investment costs, whereas low-cost options may not meet technical requirements. The proposed approach utilizes fuzzy set theory to address the uncertainties inherent in expert evaluations. FAHP determines the criterion weights, whereas FARAS ranks robots based on aggregated performance scores. A case study involving ten industrial robots was conducted to validate the model's effectiveness. The results indicate that Robot 1 is the optimal choice, whereas Robot 6 ranks the lowest. The findings of the study demonstrate that the hybrid fuzzy MCDM approach not only improves accuracy but also provides a comprehensive decision-support tool, enabling manufacturing organizations to select suitable robots based on scientifically grounded data.

Additive Manufacturing (AM) is a modern method of producing parts by depositing material layers on each other. The technology can be used to fabricate many different types of products for daily applications. Tensile Strength (TS) is one of the most important mechanical properties of fabricated products, such as 3D-printed products. This study investigates the influence of Additive Manufacturing Parameters (AMPs) on the TS of manufactured products. 3D printed samples were produced using Fused Deposition Modeling (FDM) technology with Polylactic Acid (PLA) material. The AMPs investigated were temperature, speed, layer thickness, and bed temperature. TS tests were carried out on a tensile testing machine, and the results showed that AMPs have a great influence on TS. In addition, the optimal set of AMPs was found for the manufacturing process to improve TS. TS can be predicted using a response model obtained from response optimization analysis. The results of this study can be applied in various practical applications.