RECENT ADVANCES IN TITANIUM TECHNOLOGY IN THE UNITED STATES

Assessment of density-functional models for organic molecular semiconductors: The role of Hartree–Fock exchange in charge-transfer processes

Advancements in understanding the microstructure and properties of additive manufacturing Ti-6Al-4V alloy: A comprehensive review

From the very start of their use until today, processes in Additive Manufacturing (AM) have found a way to grow from prototype production to individual and small-series production. Improvements in machinery, materials and other challenges in AM development have improved product quality, its mechanical properties and dimensional accuracy. Research in the field of dimensional accuracy must be focused on achieving better tolerances. From the beginning of AM, there has been a big issue in assuring dimensional repeatability and reproducibility of a part being printed over the course of several days. In order to examine that, a test plate was designed and built repeatedly with PolyJet technology over the course of several weeks. Measurements of dimensional accuracy and shape deviations of several typical features were carried out using X-ray Computed Tomography. Measurement results were analysed and presented in order to indicate the repeatability and reproducibility of PolyJet AM technology. Results show that PolyJet technology consistently produces parts within ±100 μm, at a 95% confidence interval, under reproducibility conditions of over a 1-month period. Accuracy for measurands (distance) in the x and y axis was significantly better than it was for the z axis which was from 56 to 197 µm, i.e., in the x and y axis, it was from −8 to 76 µm. Shape errors (i.e., cylindricity) were larger than positional or dimensional errors; this can be attributed to relatively large surface roughness and small feature sizes on the test plate that was used.

Ensuring reliable electric power to critical equipment is vital to the operation of many industrial, commercial, and military facilities. Accurate assessments of electrical system reliability are needed to determine if improvements are necessary and, in many cases, these assessments are required as a part of any new construction. However, it is generally impossible to perform actual testing on the system and hand calculations are also impossible for all but the most trivial systems. There are computer modeling tools which calculate system reliability or availability for electrical or mechanical systems based on the reliability of individual components, how those components are interconnected, and performance criteria for the system based on facility requirements. Other computer modeling tools are frequently cumbersome to use, require access to a mainframe computer, and base the results on suspect data complied in the 1970s. The Reliability and Availability Modeling Program (RAMP) is designed to perform reliability analysis using component operational and maintenance data on 242 items in the categories of power generation, power distribution, and HVAC. The authors describe the main features of RAMP, its software, and its use.

Background: The use of Additive Manufacturing (AM) in military logistics is limited by the uncertainty about the legal risks of using the method. Objectives: The purpose of this study was to identify the potential legal risks associated with AM of military materiel and its effects. The study was conducted as a case study, where AM is considered in military logistics. Methods: Legal issues were approached from the normative point of view in the context of potential legal dispute situations in four different cases. The topic was studied by reviewing and analyzing literature sources, official sources, and Finnish Defence Forces military materiel purchasing contracts and interviews. The study interpreted and systematized the general principles of IPR and contract law when using AM in the military. Results: An identified result of the study was that the use of AM to print military materiel under normal conditions may entail risks that should be contractually managed. In other cases, e.g. crises or wartime, there are risks, but they are difficult to predict and manage or are acceptable. The results of the study concern countries with a similar military economic system to Finland in terms of military logistics. The results can be applied to the activities of different security sectors, due to the similarity of their activities. Conclusions: The use of AM can enable the material to be used in safety situations where speed and versatility are required for material readiness. The use of the method requires a legal right, the holder of which has the right to manufacture the product. In different situations, the legal basis is open to interpretation and may pose risks to the decision-maker. The research will provide information to decision-makers for the preparation of contracts for the acquisition and maintenance of military material and implementing AM to military logistics.

Additive manufacturing (AM), also known as 3D printing, has revolutionized the landscape of advanced manufacturing. Among various AM techniques, material extrusion (MEX) stands out for its exceptional design freedom, unmatched potential for customization, and significantly reduced product development times. Thermoplastic materials are deposited layer-by-layer in a meticulously controlled manner, enabling the creation of intricate geometries and functional prototypes with remarkable ease. This process simplicity, coupled with the vast array of available materials, has cemented MEX’s position as the most widely adopted AM technique. However, the success of MEX hinges on a critical factor: the precise extrusion and subsequent deposition of material onto the printing surface. Inconsistencies in this process can lead to a multitude of defects, compromising the final product’s structural strength, aesthetics, and functionality. Unfortunately, current limitations exist in real-time monitoring and error detection within MEX processes. Traditional methods often rely on manual inspections, which are time-consuming, prone to human error, and incapable of immediate corrective actions. This research addresses this critical gap by proposing a novel approach that leverages the power of machine learning and cost-effective vision systems. A customized camera module was installed on the MEX extruder, providing a clear view of the localized material deposition area. This strategic placement allowed the camera to capture high-resolution images under various process parameters and layer heights. Flow rate and z-height platform offset were first identified as the primary factors influencing MEX’s material extrusion quality. The total of 291,444 captured images, from different combinations of the flow rates and z-height offsets, formed the foundation for training a modified deep learning algorithm (DL) with convolutional neural network (CNN) and multioutput classification capabilities. By meticulously analyzing the vast dataset of captured images, the proposed DL model learned to identify subtle variations and patterns associated with different material deposition scenarios. This empowered the algorithm to predict multiple process parameter values simultaneously. The trained model demonstrated remarkable performance in real-time prediction of the critical process parameters in a separate test dataset. These exceptional results showcased the immense potential of this novel approach in ensuring consistent and high-quality material deposition within MEX processes. This research presents an effective approach that harnesses the combined power of machine learning and vision systems to revolutionize MEX processes. This work offers a significant advancement over existing literature in material extrusion quality categorical prediction. The proposed system surpasses prior approaches by directly inferring process parameters with high accuracy. This capability paves the way for the development and implementation of automated error detection and correction systems, offering a novel approach for real-time quality control in material extrusion processes. Such advancements will not only minimize process inconsistencies and improve product quality but also unlock the potential for increased automation and intelligence within MEX.

Purpose– The purpose of this paper is to review the current status of additive manufacturing (AM) used for tissue engineering (TE) scaffold. AM processes are identified as an effective method for fabricating geometrically complex objects directly from computer models or three-dimensional digital representations. The use of AM technologies in the field of TE has grown rapidly in the past 10 years.Design/methodology/approach– The processes, materials, precision, applications of different AM technologies and their modified versions used for TE scaffold are presented. Additionally, future directions of AM used for TE scaffold are also discussed.Findings– There are two principal routes for the fabrication of scaffolds by AM: direct and indirect routes. According to the working principle, the AM technologies used for TE scaffold can be generally classified into: laser-based; nozzle-based; and hybrid. Although a number of materials and fabrication techniques have been developed, each AM technique is a process based on the unique property of the raw materials applied. The fabrication of TE scaffolds faces a variety of challenges, such as expanding the range of materials, improving precision and adapting to complex scaffold structures.Originality/value– This review presents the latest research regarding AM used for TE scaffold. The information available in this paper helps researchers, scholars and graduate students to get a quick overview on the recent research of AM used for TE scaffold and identify new research directions for AM in TE.

Complex geometry, variable macrostructure, anisotropy, distributed defects, surface roughness, and residual stresses of additively manufactured (AM) components challenge nondestructive evaluation processes developed and validated for conventional manufacturing. Electromagnetic and eddy current (EC) technologies were identified as promising for in-process, real-time monitoring, and post-process examination of AM components. Powder and solid specimens were prepared for electromagnetic property measurements and technique development and optimization. The post-process specimens were fabricated with roughness representative of surface conditions during fabrication inside the AM machine. Artificial and natural discontinuities were fabricated with the electrical discharge machining process and by varying the laser fusion parameters. Extensive computed modeling was conducted to develop an optimized array EC (AEC) sensor and technique for AM real-time monitoring. The AEC sensor could withstand temperatures up to 100ºC. Computational modeling was conducted to investigate the likelihood of metal powder disturbance by the EC sensor electromagnetic field. A real-time data acquisition system was assembled and integrated with an open-architecture laser powder bed fusion (L-PBF) test bed. The EC system performance for real-time monitoring of the L-PBF process was demonstrated through layer-by-layer testing of Inconel 625 coupons, with AM built discontinuities and lack of fusion. The L-PBF process was representative of a typical AM process used for super alloys and steels with preheat of substrate plate and AM test specimens to 100ºC. Existing AEC equipment and techniques were used for post-process examination of AM solid specimens at room temperatures. The AEC technology demonstrated excellent sensitivity to seeded and natural surface and near-surface subsurface discontinuities and surface topography during real-time monitoring and post-processing. Electromagnetic and EC techniques were demonstrated to examine powder before fabrication. The application of modeling tools reduced developmental time and improved efficiency. Further research and development was recommended to improve data acquisition process and transition AEC monitoring technology to other AM processes.

Additive manufacturing is an innovative manufacturing process that enables rapid manufacturing of functional products and parts. On the other side, considering environmental aspects in design is beneficial as it leads to lower costs, improved product quality, new business opportunities. Thus, in order to foster the potential of AM in product innovation and product manufacturing in the light of environmental concerns, a new design method is necessary. This paper proposes a method in the context of Design with Additive Manufacturing, to take into account the specificities of this manufacturing process in a Design to Environment approach. The method is focused on the Early Design Stages (EDS) of the product development process, which are crucial not only for choices regarding the product characteristics but also for the environmental parameters that need to be taken into consideration. The implementation of the proposed method in creativity session of the EDS underlined the need for dedicated supports in terms of environmental decisions. More and specifically the need for providing tools to capitalize the decisions made focusing on each Life Cycle Stage of the product was identified as a requirement for this support.

Computational modeling has been increasingly used to aid and improve engineering design, fabrication, and manufacturing. In the biomedical field, scientists and clinicians could use computational models to better understand biological phenomena and develop more precise treatment strategies. This thesis employs computational modeling of materials and structures to study two examples in biomedical applications: the development of a patient-specific additively manufactured knee implant and the prediction of an embryonic chick in its first stages of growth. Additive manufacturing (AM) has emerged as an innovative way of manufacturing products of complicated and customized geometries. Bioengineering has a special interest in AM as the possibility of creating patient-specific implants that can help increase satisfaction and comfort with procedures. This study explores the use of patient data in combination with finite element modeling and analysis to evaluate the performance of an AM knee implant. The approach is demonstrated on a distal femur replacement for a 50-year-old male patient from the open-access Natural Knee Data. The performance of the implant is influenced by the printing process parameters that are used to print the part. The results show that build orientations have a significant impact on both shape distortions and residual stresses. Understanding the developmental growth from a single cell into a more complex multicellular structure contributes to topics such as tissue engineering and growth defects as well as developing individual treatments. In combination with experimental results, computational analysis can increase the understanding of the behavior of organisms. Morphomechanics are used to create a computational model to simulate the tissue growth in the embryonic chick during the early stages of its development. As the chick embryo develops, the behavior and positioning of the embryo is affected by the membrane in which it develops. The effects of the growth and its surroundings result in a series of coupled bending and twisting of the embryonic body. Using computational modeling in personalized medical implants and developmental biology, this study contributes to the goals of advancing precision health initiatives. Patient-specific implants that are created to be a perfect fit would increase the probability of more patients recovering with diminished pain, increased mobility, and an improvement in their quality of life. The deformation and behavior of biological development supports the research of quantifying health conditions that may result from environmental, developmental, or genetic influences. Understanding these factors supports the advancement of preventative medical research to preserve the health of patients.

Electromagnetic (EM) methods are important geophysical tools for mineral exploration. Forward and inverse computer modeling are commonly used to interpret EM data. Real-life geology can be complex, and our computer modeling tools need to faithfully represent subsurface features to achieve accurate data interpretation. Traditional rectilinear meshes are less flexible and have difficulty conforming to the complex geometries of realistic geologic models, resulting in large numbers of mesh cells. In contrast, unstructured grids can represent complex geologic structures efficiently and accurately. However, building realistic geologic models and discretizing these models with unstructured grids suitable for EM modeling can be difficult and requires significant effort and specialized computer software tools. Therefore, it is important to develop workflows that can be used to facilitate model building and mesh generation. We have developed a procedure that can be used to build arbitrarily complex geologic models with topography using unstructured grids and a finite-volume time-domain code to calculate EM responses. We present an example of a trial-and-error modeling approach applied to a real data set collected at a uranium exploration project in the Athabasca Basin in Canada. The uranium mineralization is closely related to graphitic fault conductors in the basement. The deep burial depth and small thickness of the graphitic fault conductors demand accurate data interpretation results to guide subsequent drill testing. Our trial-and-error modeling approach builds initial realistic geologic models based on known geology and downhole data and creates initial geoelectrical models based on physical property measurements. Then, the initial model is iteratively refined based on the match between modeled and real data. We show that the modeling method can obtain 3D geoelectrical models that conform to known geology while achieving a good match between modeled and real data. The method can also provide guidance of where future drill holes should be directed.

Additive manufacturing (AM) has revolutionized the manufacturing industry by offering flexibility, customization, and rapid prototyping capabilities. Complex geometries and low volume parts can be produced via AM in much lower lead time and cost compared to traditional manufacturing methods. However, ensuring the quality and reliability of AM parts remains a significant challenge due to variations in material properties and process parameters. Traditional material property analysis methods, that require experimental testing and computational modeling, are often costly and time-consuming and often not applicable to the complex geometries of AM components. These challenges are further magnified when repetitive sampling is required. To address these challenges, this paper presents a novel approach that combines experimental findings with Finite Element Modeling (FEM) to construct a digital twin (DT) model of AM parts. The DT model will serve as a virtual representation along with real-time monitoring, simulation, and optimization capabilities. Numerical simulations are conducted to correlate the resonance frequency of parts at different process conditions with their material characteristics, represented as RF Z-score values. Hence, the results suggest that such an approach provides manufacturers with cost-effective and efficient means of analyzing material properties and optimizing AM processes. Moreover, by leveraging digital twin technology, manufacturers can achieve greater process control and quality assurance in additive manufacturing. The findings of this study contribute to advancing the understanding and implementation of digital twin technology in AM; therefore, paving the way for enhanced productivity and reliability in additive manufacturing processes.

Uncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.

The exponential increase in available neural data has combined with the exponential growth in computing (“Moore's law”) to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation.

Molded interconnect device (MID) technology is a key enabling technology with growing markets in automotive, communications, consumer electronics through integration with lighting and sensor technologies. Additive manufacturing (AM) is also a key technology as energy, material and resource efficient alternative to conventional subtractive and formative production technologies. In this contribution, powder-based additive layered manufacturing mainly selective laser melting (SLM) is focused to check the potential for substrate manufacturing, system integration and packaging concepts in electronic systems particularly for high-temperature MID applications. The challenge for an optimal mechatronic integrated device design is to simultaneously consider both the application-specific requirements and to integrate advantages of MID technology. This work focuses on the process development for the generation of conductive patterns on ceramic substrates by SLM technique. The process influencing parameters such as laser power, velocity and scan concepts were tested to check the bonding characteristics. The obtained layer thicknesses and the corresponding process-generated failure mechanisms and the mechanical stability are presented. The potential of SLM for high temperature applicability is further discussed. The influences of the printed powder height, laser parameters in combination with laser scan strategies and pattern height are discussed. The hurdles and prospects of SLM as alternative for functionalization are highlighted and discussed.