This paper presents a novel approach for de-agglomeration and surface functionalization of detonation nanodiamond at the same time in one pot. Various kinds of polymer chains (e.g., polystyrene, polymethyl methacrylate and polyglycidyl methacrylate) can be chemically grafted onto the de-agglomerated nanodiamond by the wet-stirred-media-milling process. The surface grafted polymers were identified by FTIR, TGA and TEM. The polymer grafted onto surface of de-agglomerated nanodiamond was used as an additive in lubricant oil. We investigated the tribological properties of the two-phase lubricant oil and nanodiamond–polymer composite. The results show that this kind of nanodiamond–polymer composite possesses better anti-wear, friction-reduction and load-carrying capacity than the nanodiamond additive. Impressively, the addition of 3000 ppm nanodiamond–polymer composite to lubricating oil can reduce friction coefficient by 75% and wear rate by ∼99%. The polymer grafted onto the nanodiamond significantly improves durability and lubricant performance of the nanodiamond additive. This is because nanodiamond–polymer can be more compatible with lubricant oil than nanodiamond. The surface functionalization of nanodiamond can significantly affect lubricant performance and the durability of nanodiamond additive, which has not been discussed in previous papers.

The injection-molding process is a non-linear process, and the product quality and long-term production stability are affected by several factors. To stabilize the product quality effected by these factors, this research establishes a standard process parameter setup procedure and an adaptive process control system based on the data collected by a nozzle pressure sensor and a tie-bar strain gauge to achieve this goal. In this research, process parameters such as the V/P switchover point, injection speed, packing pressure, and clamping force are sequentially optimized based on the characteristics of the pressure profile. After the optimization process, this research defines the standard quality characteristics through the optimized process parameters and combines it with the adaptive process control system in order to achieve the purpose of automatic adjustment of the machine and maintain high-quality production. Finally, three different viscosity materials are used to verify the effectiveness of the optimization procedure and the adaptive process control system. With the system, the variation of product weight was reduced to 0.106%, 0.092%, and 0.079%, respectively.

This research developed an adaptive control system for injection molding process. The purpose of this control system is to adaptively maintain the consistency of product quality by minimize the mass variation of injection molded parts. The adaptive control system works with the information collected through two sensors installed in the machine only—the injection nozzle pressure sensor and the temperature sensor. In this research, preliminary experiments are purposed to find master pressure curve that relates to product quality. Viscosity index, peak pressure, and timing of the peak pressure are used to characterize the pressure curve. The correlation between product quality and parameters such as switchover position and injection speed were used to produce a training data for back propagation neural network (BPNN) to compute weight and bias which are applied on the adaptive control system. By using this system, the variation of part weight is maintained to be as low as 0.14%.

Injection molding is a popular process for the mass production of polymer products, but due to the characteristics of the injection process, there are many factors that will affect the product quality during the long fabrication processes. In this study, an adaptive adjustment system was developed by C++ programming to adjust the V/P switchover point and injection speed during the injection molding process in order to minimize the variation of the product weight. Based on a series of preliminary experiments, it was found that the viscosity index and peak pressure had a strong correlation with the weight of the injection-molded parts. Therefore, the viscosity index and peak pressure are used to guide the adjustment in the presented control system, and only one nozzle pressure sensor is used in the system. The results of the preliminary experiments indicate that the reduction of the packing time and setting enough clamping force can decrease the variation of the injected weight without turning on the adaptive control system; meanwhile, the master pressure curve obtained from the preliminary experiment was used as the control target of the system. With this system, the variation of the product weight and coefficient of variation (C_V) of the product weight can be decreased to 0.21 and 0.05%, respectively.

The nut manufacturing machine operates almost continuously throughout the day and, consequently, requires frequent maintenance, the duration of which depends on the type of fault. Unpredicted downtime can cause loss of production volume, inability to meet critical timelines, and product quality degradation. Vibration research was conducted on a six-die nut manufacturing machine, which experienced frequent faults on the fourth and sixth dies that exerted the greatest impact on the nut material. The application of several analytical techniques on vibration data – the fast Fourier Transform, Empirical Mode Decomposition, Multiscale Entropy, Shock Response Spectrum, and correlation – enabled the identification of fault indicators, which would be useful for predictive maintenance. These features were detectable up to 8 h 28 min and up to 1 h 9 min prior to failure or deterioration resulting from severe and regular damage, respectively. Faults included quality deterioration and die and non-die breakage, fusion, bending and cracking. This research can optimise machine efficiency through timely upkeep facilitated by predictive maintenance.

Injection molding is a highly nonlinear procedure that is easily influenced by various external factors, thereby affecting the stability of the product’s quality. High-speed injection molding is required for production due to the rapid cooling characteristics of thin-walled parts, leading to increased manufacturing complexity. Consequently, establishing appropriate process parameters for maintaining quality stability in long-term production is challenging. This study selected a hot runner mold with a thin wall fitted with two external sensors, a nozzle pressure sensor and a tie-bar strain gauge, to collect data regarding the nozzle peak pressure, the timing of peak pressure, the viscosity index, and the clamping force difference value. The product weight was defined as the quality indicator, and a standardized parameter optimization process was constructed, including injection speed, V/P switchover point, packing, and clamping force. Finally, the optimized process parameters were applied to the adaptive process control experiments using the developed control system operated within the micro-controller unit (MCU). The results revealed that the control system effectively stabilized the product weight variation and standard deviation of 0.677% and 0.0178 g, respectively.