Application of Additive Manufacturing (AM) has significantly increased in the past few years. AM also known as three-dimensional (3D) printing has been currently used in fabrication of prototypes and end-use products. Considering the new applications of additively manufactured components, it is necessary to study structural details of these parts. In the current study, influence of a post-processing on the mechanical properties of 3D-printed parts has been investigated. To this aim, Acrylonitrile Butadiene Styrene (ABS) material was used to produce test coupons based on the Fused Deposition Modeling (FDM) process. More in deep, a device was designed and fabricated to fix imperfection and provide smooth surfaces on the 3D-printed ABS specimens. Later, original and treated specimens were subjected to a series of tensile loads, three-point bending tests, and water absorption tests. The experimental tests indicated fracture load in untreated dog-bone shaped specimen was 2026.1 N which was decreased to 1951.7 N after surface treatment. Moreover, the performed surface treatment was lead and decrease in tensile strength from 29.37 MPa to 26.25 MPa. Comparison of the results confirmed effects of the surface modification on the fracture toughness of the examined semi-circular bending components. Moreover, a 3D laser microscope was used for visual investigation of the specimens. The documented results are beneficial for next designs and optimization of finishing processes.

Injection molding is a multiphase process that requires accurate simulation of the filling phase. This is a key element in predicting the complete injection molding cycle. The filling phase presents a complex set of challenges, including migrating melt fronts, multi-phase flow, non-Newtonian fluid dynamics, and intertwined heat transfer. Evolving from 1D to 2D, 2.5D, and 3D techniques, filling simulation research has adapted to capture the intricacies of injection-molded parts. However, the need for accuracy in the characterization of the rheological properties of polymers during filling is still of paramount importance. In order to systematically categorize the numerical methods used to simulate the filling phase of injection molding, this review paper provides a comprehensive summary. Particular emphasis is given to the complex interaction of multiple geometric parameters that significantly influence the dynamic evolution of the filling process. In addition, a spectrum of rheological models is thoroughly and exhaustively explored in the manuscript. These models serve as basic mathematical constructs to help describe the complex viscous behavior of polymers during the filling phase. These models cover a spectrum of complexity and include widely recognized formulations such as the Power-Law, second-order, Herschel–Bulkley, Carreau, Bird–Carreau, and Cross models. The paper presents their implementation to include the temperature-dependent influence on viscosity. In this context, the extensions of these models are explained in detail. These extensions are designed to take into account the dynamic viscosity changes caused by the different thermal conditions during the filling process. An important contribution of this study is the systematic classification of these models. This categorization encompasses both academic research and practical integration into commercial software frameworks. In addition to the theoretical importance of these models, their practical value in overcoming challenges in the field of injection molding is emphasized. By systematically outlining these models within a structured framework, this classification promotes a comprehensive understanding of their intrinsic characteristics and relevance in different scenarios.

This contribution provides a detailed comparison of the impact of various rheological models on the filling phase of injection molding simulations in order to enhance the accuracy of flow predictions and improve material processing. The challenge of accurately modeling polymer melt flow behavior under different temperature and shear rate conditions is crucial for optimizing injection molding processes. Therefore, the study examines commonly used rheological models, including Power-Law, Second-Order, Herschel-Bulkley, Carreau and Cross models. Using experimental data for validation, the accuracy of each model in predicting the flow front and viscosity distribution for a quadratic molded part with a PA66 polymer is evaluated. The Carreau-WLF Winter model showed the highest accuracy, with the lowest RMSE values, closely followed by the Carreau model. The Second-Order model exhibited significant deviations in the edge region from experimental results, indicating its limitations. Results indicate that models incorporating both shear rate and temperature dependencies, such as Carreau-WLF Winter, provide superior predictions compared to those including only shear rate dependence. These findings suggest that selecting appropriate rheological models can significantly enhance the predictive capability of injection molding simulations, leading to better process optimization and higher quality in manufactured parts. The study emphasizes the significance of comprehensive rheological analysis and identifies potential avenues for future research and industrial applications in polymer processing.

In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on others. To address this issue, digital twins (DTs) are introduced as virtual models that replicate the entire injection molding process. This paper focuses on the data and technology needed to build a DT model for injection molding. Each stage can have its own DT, which are integrated into a comprehensive model of the process. DTs enable the smart automation of production processes and data collection, reducing manual efforts in supervising and controlling production systems. However, implementing DTs is challenging and requires effort for conception and integration with the represented systems. To mitigate this, the current work presents a model for systematic knowledge-based engineering for the DTs of injection molding. This model includes fault detection systems, 3D printing, and system integration to automate development activities. Based on knowledge engineering, data analysis, and data mapping, the proposed DT model allows fault detection, prognostic maintenance, and predictive manufacturing.