Polymer Processing—An Introduction

This article discusses technologies focused on processing plastic materials or producing direct tools used in plastics processing. The article focuses on extrusion and injection molding, covering applications, materials and their properties, equipment, processing details, part design guidelines, and special processes. It also covers the functions of the extruder, webline handling, mixing and compounding operations, and process troubleshooting. Thermoforming and mold design are covered. Various other technologies for polymer processing covered in this article are blow molding, rotational molding, compression molding, transfer molding, hand lay-up process, casting, and additive manufacturing.

Increasing the Sustainability of the Hybrid Mold Technique through Combined Insert Polymeric Material and Additive Manufacturing Method Design

To reduce plastic waste generation from failed product batches during industrial injection molding, the sustainable production of representative prototypes is essential. Interesting is the more recent hybrid injection molding (HM) technique, in which a polymeric mold core and cavity are produced via additive manufacturing (AM) and are both placed in an overall metal housing for the final polymeric part production. HM requires less material waste and energy compared to conventional subtractive injection molding, at least if its process parameters are properly tuned. In the present work, several options of AM insert production are compared with full metal/steel mold inserts, selecting isotactic polypropylene as the injected polymer. These options are defined by both the AM method and the material considered and are evaluated with respect to the insert mechanical and conductive properties, also considering Moldex3D simulations. These simulations are conducted with inputted measured temperature-dependent AM material properties to identify in silico indicators for wear and to perform cooling cycle time minimization. It is shown that PolyJetted Digital acrylonitrile-butadiene-styrene (ABS) polymer and Multi jet fusioned (MJF) polyamide 11 (PA11) are the most promising. The former option has the best durability for thinner injection molded parts, and the latter option the best cooling cycle times at any thickness, highlighting the need to further develop AM options.

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.

Piston-Based Material Extrusion of Ti-6Al-4V Feedstock for Complementary Use in Metal Injection Molding

Piston-based material extrusion enables cost savings for metal injection molding users when it is utilized as a complementary shaping process for green parts in small batch sizes. This, however, requires the use of series feedstock and the production of sufficiently dense green parts in order to ensure metal injection molding-like material properties. In this paper, a methodological approach is presented to identify material-specific process parameters for an industrially used Ti-6Al-4V metal injection molding feedstock based on the extrusion force. It was found that for an optimum extrusion temperature of 95 °C and printing speed of 8 mm/s an extrusion force of 1300 N ensures high-density green parts without under-extrusion. The resulting sintered part properties exhibit values comparable to metal injection molding in terms of part density (max. 99.1%) and tensile properties (max. yield strength: 933 MPa, max. ultimate tensile strength: 1000 MPa, max. elongation at break: 18.5%) depending on the selected build orientation. Thus, a complementary use could be demonstrated in principle for the Ti-6Al-4V feedstock.

Optimization of Injection Molding of Automotive Plastic Horn Cover Part Using Taguchi Method and Reverse Engineering

The aim of the paper consisted in the development of an injection mold for plastic horn cover parts in commercial vehicles. Three mold types were designed in anticipation of the structure and quality of molds, and injection molding numerical analyses were conducted for the three types of molds. One mold type was selected in consideration of the resin flow patterns inside the mold, surface quality, and final deflection amount of the horn cover. To perform optimal injection molding using the selected mold, optimization of injection molding parameters was performed using the Taguchi method, one of the designs of experiment (DOE) and ANOVA methods. As a result, it was confirmed that the deflection amount of the molding under optimal molding parameters decreased by about 34.3% compared to the deflection amount before optimization of the molding parameters. Based on these encouraging results, the previously selected mold type was actually manufactured. The horn cover was molded using the obtained optimal injection molding parameters to the manufactured mold. To verify the precision of the molded horn cover, the deflection amount of the molding was measured with a 3D scanner. The deflection amount of the horn cover was estimated to be about 11% to 43% larger for each measurement position than the deflection amounts in the analysis results. The manufactured mold was revised to solve the problem that the deflection amount of the actual molding is larger than the deflection amount predicted by injection molding analysis. The dimensions and surface quality of the horn cover with a revised mold were satisfactory.