Polyamide 6-Aluminum Assembly Enhanced by Laser Microstructuring

The presented work’s aim is the application of low-power laser treatment for the enhancement of interfacial micromechanical adhesion between polyamide 6 (filled with glass fiber) and aluminum. A fiber laser beam was used to prepare micro-patterns on aluminum sheets. The micro-structuring was conducted in the regime of 50, 100, 200 and 300 mm/s laser beam speeds, for both sides. The joining process was realized in an injection molding process. Metallic inserts were surface engraved and overmolded in one-side and two-side configurations. A lap shear test was used to examine the strength of the joints. Engraved metallic surfaces and adequate imprints on polyamide side were checked by optical microscope with motorized stages, and roughness parameters were also determined. Microscopic observations made it possible to describe the grooves’ shape and to conclude that a huge recast melt was formed when the lowest laser beam speed was applied; thus, the roughness parameter Ra reached the highest value of 16.8 μm (compared to 3.5 μm obtained for the fastest laser speed). The maximum shear force was detected for a sample prepared with the lowest scanning speed (one-sides joints), and it was 883 N, while for two-sided joints, the ultimate force was 1410 N (for a scanning speed of 200 mm/s).

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

A Review on Machine Learning Models in Injection Molding Machines

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

Injection Molding Technology

The injection molding process is one of the most efficient and economical casting processes. The process is becoming increasingly common in various industries in large-scale and mass production of castings. It should be noted that by having great advantages over other methods of obtaining high-quality castings of higher accuracy, injection molding makes it possible to bring the dimensions of the castings as close as possible to the dimensions of the finished parts.

Chip-scale optical airflow sensor

Airflow sensors are an essential component in a wide range of industrial, biomedical, and environmental applications. The development of compact devices with a fast response and wide measurement range capable of in situ airflow monitoring is highly desirable. Herein, we report a miniaturized optical airflow sensor based on a GaN chip with a flexible PDMS membrane. The compact GaN chip is responsible for light emission and photodetection. The PDMS membrane fabricated using a droplet-based molding process can effectively transform the airflow stimuli into optical reflectance changes that can be monitored by an on-chip photodetector. Without the use of external components for light coupling, the proposed sensor adopting the novel integration scheme is capable of detecting airflow rates of up to 53.5 ms−1 and exhibits a fast response time of 12 ms, holding great promise for diverse practical applications. The potential use in monitoring human breathing is also demonstrated.

Vibration and Sound Response of Glass-Fiber-Reinforced Polyamide 6 Using Microcellular-Foaming-Process-Applied Injection Molding Process

In this study, the vibration and sound response characteristics of composites produced via injection molding applied with a microcellular foaming process (MCPs) were improved. The study was conducted using PA6 and glass fiber composites, which are representative thermoplastic engineering plastics. Two types of specimens were used: a plate specimen to confirm the basic sound and vibration characteristics, and a large roof-rack specimen from an actual vehicle with a complex shape. The frequency response function curve was calculated by conducting an impact test, and natural frequency and damping ratio were measured based on the curve. The results confirmed that, in the case of a specimen manufactured through the injection molding process to which MCPs were applied, the natural frequency was lowered, and the damping ratio decreased. The degree of change in the natural frequency and damping ratio was confirmed. To determine the cause of the change in the natural frequency and damping ratio, the mode shape at the natural frequency of each specimen was measured and the relationship was confirmed by measuring the density and the elastic modulus of the composite. In addition, the usability of the specimens to which MCPs were applied was verified by conducting impact strength and tensile strength tests.

Optimization of injection molding process parameters based on GA-ELM-GA

The most common optimization method for the optimization of injection mold process parameters is range analysis, but there is often a nonlinear coupling relationship between injection molding process parameters and quality indicators. Therefore, it is difficult to find the optimal process combination in range analysis. In this article, a genetic algorithm optimized extreme learning machine network model (GA-ELM) combined with genetic algorithm (GA) was proposed to optimize the process parameters of the injection mold. Take the injection molding process parameter optimization of an electrical appliance buckle cover shell as an example. In order to find the process parameters corresponding to the minimum warpage deformation, an orthogonal experiment was designed and the results of the orthogonal experiment were analyzed. Then, the corresponding optimal process combination and the degree of influence of process parameters on the warpage deformation were obtained. At the same time, the extreme learning machine network model (GA-ELM) optimized by the genetic algorithm was used to predict the warpage deformation of the plastic part. The trained GA-ELM model can map non-linear coupling relationship between the five process parameters and the warpage deformation well. And the optimal process parameters in the trained GA-ELM network model was searched by the powerful optimization ability of genetic algorithm. Generally speaking, the warpage deformation after optimization by range analysis is reduced by 6.7% compared with the minimum warpage after optimization by orthogonal experiment. But compared to the minimum warpage deformation after orthogonal experiment optimization, the warpage deformation after GAELM-GA optimization is reduced by 22%, which is better than that of the range analysis, thus verifying the feasibility and the optimization of the optimization method. This optimization method provides a certain theoretical reference and technical support for the field involving the optimization of process parameters.

ANALYSIS OF THE TEMPERATURE FIELD IN A MOLD CAVITY FOR POLYMER INJECTION PROCESS

A common situation in the design of injection molds is to achieve an efficient performance in terms of heat transfer, this will allow a higher production rate with better finished parts [1]. One of the most important factors in the design is the cooling time: about 80% of the processing time is determined by it [2]. Seeking to contribute with the increase of productivity, this work presents results of simulations through the finite volume method (MVF) of the injection molding process; those results are compared with an analysis of design of experiments (DOE) with different injection conditions, revealing the study variables that are fundamental to improve the process. Thus, a statistical analysis and a computer simulation analysis are presented to identify the variables inherent to the process and recommend their values.