Zhongjun Qiu

During the injection molding process of thin‐walled parts, the polymer melt encounters significant flow resistance within narrow mold cavities, which easily results in short‐shot defects. A two‐dimensional ultrasonic‐assisted injection molding (2DUIM) method is proposed and used to improve the melt filling performance in thin‐walled injection molding under normal process parameters. A 2DUIM mold that can directly exert two‐dimensional (2D) ultrasonic vibration on the polymer melt in the cavity is developed to fabricate an accelerometer pendulum with complex thin‐walled structures. The 2DUIM experiments for the accelerometer pendulum demonstrated that the introduction of 2D ultrasonic vibration markedly enhanced the melt filling performance. Specifically, the filling area of the cavity corresponding to the mass block increased by up to 18.1 mm ² relative to that without ultrasonic assistance, accounting for 19.7% of the total filling area of the C2 cavity. Meanwhile, 2D ultrasonic vibration reduces the surface flatness and residual stress of the mass block, improving the molding quality of the accelerometer pendulum. This study presents a novel and effective strategy for overcoming the inherent difficulties in the injection molding of thin‐walled parts.

In modern industry, the surface defect inspection of injection moulded products is crucial for controlling product quality and optimising the manufacturing process. With the development of optical measurement and computer technology, machine vision inspection methods have been widely adopted instead of manual inspection. However, current machine vision inspection methods are difficult to simultaneously ensure the accuracy and efficiency of surface defect inspection of injection moulded products. Considering this problem, a novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products is proposed. To train and evaluate the proposed deep learning algorithm, an image acquisition platform is established and the dataset of surface defects in moulded products is obtained. In the proposed deep learning algorithm, reparameterisation-based convolution modules are employed for feature extraction and feature fusion. A median iterative clustering algorithm based on hierarchical clustering initialisation is proposed to obtain prior anchors that are highly matched with the actual distribution of defect sizes. A novel focus-entire union over covering loss function is utilised for bounding box regression. On these bases, the proposed deep learning algorithm applied to machine vision inspection is evaluated on the dataset of surface defects in moulded products. The experimental results indicate that compared to the traditional inspection algorithms and other deep learning algorithms currently used in machine vision inspection, the proposed deep learning algorithm exhibits superior inspection accuracy and inspection efficiency on the acquired dataset. The inspection precision reaches 0.964, the inspection recall reaches 0.955, and the inference time for each subgraph is only 6.1 ms, confirming its effectiveness.

The factors influencing the accuracy of the line scan imaging system (LSIS) are highly complex, and their error propagation mechanisms are poorly clarified. To reasonably and effectively mitigate the impact of mechanical factors on imaging accuracy, a novel comprehensive error model for LSIS is proposed in this paper, revealing the relationship between imaging errors and various mechanical error sources from manufacturing, assembly, and movement. Based on the analysis of the generation and propagation relationship of mechanical errors in LSIS, the integration of the mechanical system and the imaging system is accomplished to form a comprehensive multibody system, thus establishing a complete linear transmission mechanism for mechanical errors in the imaging process within the model. The experiments demonstrate that this model can provide a reference for the localization of mechanical error sources by utilizing the changes in extrinsic parameters during the calibration process. Furthermore, when the sensitivities of error sources are not exceptionally low, and certain cases with systematic errors are excluded, the model can make accurate estimations of the sensitivities of imaging errors to those errors generated by various mechanical sources. The estimated relative errors do not exceed 6%. The results confirm the effectiveness of this model, which can be utilized to ascertain the range of error sources and assess the extent to which errors from various sources impact imaging accuracy. This model provides a basis for the precision optimization of LSIS.

Iridescent patterns are insufferable molding defects during mass manufacturing of polymer optic devices by using injection molding (IM) technique and seriously affect the appearance and imaging performance of parts. However, at present, the research on the factors affecting iridescent pattern defects is lacking, resulting in the difficulty of obtaining effective methods to weaken iridescent pattern defects. This article aims to clarify the correlation mechanism between injection process parameters and iridescent patterns and provide a theoretical basis for reducing iridescent pattern defects. Based on the process characteristics of IM and the optical mechanism of iridescent patterns, the correlation between iridescent patterns and influencing factors such as optical characteristics and the material structure is established. The influence mechanism of process parameters on iridescent patterns is further clarified by experiments. The results show the iridescent pattern color is related to the retardation, while the iridescent pattern brightness is affected by the retardation and transmittance. Process parameters change the retardation and transmittance by varying factors such as the thickness, birefringence, and crystallinity. Higher mold temperature, reasonable melt temperature, injection speed, and holding pressure are conducive to reducing the colorful fringe area and iridescent pattern brightness, thereby weakening the iridescent patterns on the part.

Demolding is the key step of micro‐injection molding (μIM), which influences the molding quality of polymer parts to a great extent. The essence of demolding is to overcome interface interactions between the mold cavity surface and the part surface, which is closely related to factors such as the cavity surface topography and process parameters. However, a theoretical model predicting the demolding force of μIM is still lacking, resulting in the difficulty of improving the quality of demolding in μIM. Therefore, a comprehensive demolding force model is proposed to clarify the demolding mechanism of μIM. The model focuses on the adhesion behavior and friction behavior in the special contact mode formed by the replication effect of μIM. The theory proof and computation results indicate that the cavity surface topography determines the contact mode of the mold‐part interface (MPI), which in turn changes the real interface contact area, the composition mode of adhesion stress, and the friction forms, thereby affecting the adhesion and friction force during demolding. In addition, the process parameters can also change the contact mode of MPI as the process parameters have a great effect on the filling capacity and the degree of replication, thereby influencing the demolding process.

Micro polymer parts are usually fabricated by ultrasonic‐assisted micro‐injection molding which is an effective and low‐cost method. This method solves some injection molding defects such as short shot by reducing the melt viscosity, and the clarification of its mechanism is a challenge. This article is aimed at studying the evolution mechanism of two‐dimensional ultrasonic vibration on the polymer melt unstable flow. A two‐dimensional ultrasonic vibration, whose displacements components in the‐ and‐directions are large, is applied to the polymer melt. The unstable flow is a flow field with perturbations. The formulas of perturbation stream function and velocity perturbations are established based on the theories of absolute instability and convective instability. The established formulas are verified by numerical analysis. The results show that the flow of polymer melt is convectively unstable and the vortices are formed under the combined actions of shear stress and transverse pressure. The distribution of vortices is bound to affect the mold filling quality. Accordingly, a two‐dimensional ultrasonic vibration with the phase difference of 90° is adopted to make strong vortices occur symmetrically, to improve the uniformity of viscosity field, and to obtain a better melt filling capability during injection molding.

A polymeric micro-needle array with high quality has been fabricated using a longitudinal ultrasonic-assisted micro-injection molding (LUμIM) method. To realize the practicability and stability in actual industrial processing, this paper is aimed at studying the improvement mechanism of ultrasonic vibration on the molding quality. The melt-filling process in the micro-needle array cavity is simulated, and the improvement effect of ultrasonic vibration is discussed. The enhancement effect of ultrasonic vibration on material properties of polypropylene and polymethylmethacrylate parts are experimentally investigated. The results show that in the manufacturing of the micro-needle array part using LUμIM, the mold-filling quality is improved by the enhanced melt filling capability and pressure compensation effect, which are caused by the increased corner viscosity gradient, reduced the filling time and melt viscosity under ultrasonic vibration. Material properties of both the semi-crystalline polymer and amorphous polymer could be enhanced by the transformation of micromorphology. It is proved that for a semi-crystalline polymer, this novel method could be employed as a material properties enhancement method, and an optimal excitation voltage of ultrasonic vibration is obtained to achieve the best material properties.