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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to the wide scope of applications of additive manufacturing (AM) in making final products, the mechanical strength of AM parts has become very important. Therefore, different tests are being developed to determine the structural integrity of three-dimensional printed components. In this respect, the pin-bearing test is designed to evaluate the response of a fastener, plate, and hole to stress. In this study, two different polymer materials were used to fabricate the samples utilizing the fused deposition modeling technique. Since the specimen width and hole diameter have effects on the pin-bearing strength and structural integrity of the parts, we prepared the specimens with four hole diameters to determine the influence of this ratio. A series of tensile tests were performed, and the stiffness and pin-bearing strength of additively manufactured specimens were determined. The preferred bearing failure mode was observed in several tested specimens. Subsequently, a scanning electron microscope investigation was conducted on the damaged area of the examined specimens to obtain insights into the damage mechanisms and failure behavior of the aforementioned specimens. We used digital image correlation technique to determine the strain field of dumbbell-shaped test coupons. The results of this research can be utilized for new designs of AM parts with a higher mechanical strength. [ABSTRACT FROM AUTHOR]

Accurate modeling of the dynamic stall remains a challenge for the design and construction of turbine blades and helicopter rotors. At the same time, wind turbines, for instance, are becoming steadily larger, further increasing the demands on their structure and necessitating even more detailed modeling of the forces at hand. The primarily used (semi-)empirical models today have a long research history and are invariably based on phaseaveraged data from oscillating blade pitch experiments. However, much potential for more accurate modeling of uncertainties and force peaks is wasted here, since averaging blurs many features of the response signals. Even computational fluid dynamics can help little in this regard, since the Reynolds-averaged Navier-Stokes equations used in practice cannot account for cycle variations, and scale-resolving models require extremely large amounts of computational resources. This paper presents an approach for a fully stochastic machine learning model that can nevertheless simulate these critical properties. Aerodynamic coefficients are compared with experimental data for different test cases. It is shown that synthetic force profiles which cannot be distinguished from the experimental data visually and are very close to them in the frequency spectrum can be generated. Additionally, attention is drawn to the difficulty of evaluating such a model, as traditional error metrics are of little use. A combination of dynamic time warping and the Earth mover's distance provides a robust solution for this problem. [ABSTRACT FROM AUTHOR]

Technical advantages of additive manufacturing (AM) have drawn great attention over the past few years. This cost-effective manufacturing process proved its potential applications in a wide range of fields. Although AM techniques (known as 3D printing) are able to fabricate geometrically complex components, it is necessary to evaluate internal and external dimensions of the printed parts. In this context, x-ray computed tomography (CT) as a nondestructive evaluation technique has been utilized. Indeed, CT can be used for geometric analysis, defects detection, quantitative comparison, structural quantification and porosity analysis. In the current study, we present a brief review of 3D printing processes and evolution of CT technology. Moreover, applications of CT in assessment of 3D-printed components are explained in detail. Although CT has been used in academic and industrial researches, abilities of this inspection method are not yet fully documented for precision engineering applications. In this work, usage of this technique in study of printed components are categorized in four subdomains and discussed. The documented data proved that CT is an appropriate non-contact technique for technical evaluation of various printed parts. As usage of CT in assessment of printed parts is still evolving, the limitations, challenges and future perspective are outlined. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The supply of energy is sustainable only if it is predominantly based on renewable or regenerative energies. For this reason, the use of micro-hydropower plants on rivers and streams is considered recently. This is a particular challenge for the preservation of ecologically permeable streams, so that no dams or similar structures can be considered. While the axial turbine design has prevailed in wind power, there is still no consensus for the generation of energy in free water flow conditions. In this work, an existing prototype of an unusual vertical axis Kirsten–Boeing turbine was investigated. A multivariate optimization process was created, in which all important machine parameters were checked and improved. By using neural networks as a metamodel coupled with flow simulations in ANSYS CFX, a broadly applicable optimization strategy is presented that yielded a blade design that is 36% more efficient than its predecessor in experiments. During the process, it was shown how to set up a complex sliding mesh problem with ANSYS expressions while evaluating a free surface problem. [ABSTRACT FROM AUTHOR]