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DOE OSTI2021

Dissimilar joining of ZEK100 and AA6022 for automotive application

Mg and Al joining is problematic due to different high temperature flow characteristics that often results in weld defects and brittle intermetallic compounds (IMC). We present process development on lap joining of 1.27 mm 6022 (top sheet) and 1.5mm ZEK100 (bottom sheet) for an automotive door application. We conducted process development in linear welding using power control mode (modulating torque and spindle speed) with multiple tool designs, and welding parameters. Process variables were correlated to joint micro-structure and mechanical properties. A 2D computational model was created to understand the failure mechanism and contribution of mechanical interlocking towards joint strength. For a thin (< 2 mm) automotive sheet with softer material as a bottom sheet, adequate control of process variables is critical to maintain weld stability and avoid excessive IMC layer at the interface. A combination of mechanical anchoring and metallurgical reaction is likely responsible for bonding.

Das, Hrishikesh↗

DOE OSTI2022

Initial Results on a New Light-Duty 2.7-L Opposed-Piston Gasoline Compression Ignition Multi-Cylinder Engine

Abstract Gasoline compression ignition (GCI) is a cost-effective approach to achieving diesel-like efficiencies with low emissions. The fundamental architecture of the two-stroke Achates Power Opposed-Piston (OP) Engine enables GCI by decoupling piston motion from cylinder scavenging, allowing for flexible and independent control of cylinder residual fraction and temperature leading to improved low-load combustion. In addition, the high peak cylinder pressure and noise challenges at high-load operation are mitigated by the lower brake mean effective pressure (BMEP) operation and faster heat release for the same pressure rise rate of the OP Engine. These advantages further solidify the performance benefits of the OP Engine and demonstrate the near-term feasibility of advanced combustion technologies, enabled by the opposed-piston architecture. This paper presents initial results from steady-state testing on a brand new 2.7-L OP GCI multi-cylinder engine (MCE) designed for light-duty truck applications. Successful GCI operation calls for a high compression ratio (CR), leading to higher combustion stability at low loads, higher efficiencies, and lower cycle HC + NOX emissions. Initial results show a cycle average brake thermal efficiency (BTE) of 31.7%, which is already greater than 11% conventional engines, after only ten weeks of testing. Emissions results suggest that Tier 3 Bin 160 levels can be achieved using a traditional diesel after-treatment system. Combustion noise was well controlled at or below the United States Council for Automotive Research limits. In addition, initial results on catalyst light-off mode with GCI are also presented.

Energy & Fuels↗

DOE OSTI2023

Access Capabilities Through Ccs Communications Protocol (acccs)

AcCCS is a collection of scripts that leverage open-source code, off-the-shelf hardware, and published protocol specifications to create a flexible and inexpensive test and evaluation device for the Electric Vehicle industry. AcCCS is capable of emulating either an Electric Vehicle (EV) or Electric Vehicle Supply Equipment (EVSE). It provides a flexible set of Python scripts to test and evaluate the various communication protocols between the Electric Vehicle Communication Controller (EVCC) and Supply Equipment Communication Controller (SECC). This capability is useful for cybersecurity researchers, automotive OEMs, and EVSE manufactures.

Rohde, KennethW [Idaho National Laboratory (INL), ↗

DOE OSTI2024

Machine Learning for Joint Quality Control

The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Controllable growth of interpenetrating or random copolymer networks

Interpenetrating and random copolymer networks are vital in a number of industrial applications, including the fabrication of automotive parts, damping materials, and tissue engineering scaffolds. We develop a theoretical model for a process that enables the controlled growth of interpenetrating network (IPNs), or a random copolymer network (RCN) of specified size and mechanical properties. In this process, a primary gel “seed” is immersed into a solution containing the secondary monomer and crosslinkers. After the latter species are absorbed into the primary network, the absorbed monomers are polymerized to form the secondary polymer chains, which then can undergo further crosslinking to form an IPN, or undergo inter-chain exchange with the existing network to form a RCN. The swelling and elastic properties of the IPN and RCN networks can be tailored by modifying the monomer and crosslinker concentrations in the surrounding solution, or by tuning the enthalpic interactions between the primary polymer, secondary monomer and solvent through a proper choice of chemistry. This process can be used repeatedly to fabricate gels with a range of mechanical properties from stiff, rigid materials to soft, flexible networks, allowing the method to meet the materials requirements of a variety of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Application of Controller Area Network for Power Converter Network Monitoring and Control

This presentation explores the adaptation and implementation of CAN bus technology in a network of power converters, emphasizing its potential beyond automotive applications. In this system, multiple power converters can utilize the CAN bus to transmit diverse data types, each assigned specific message and node IDs. A central controller continuously monitors the status of each converter, ensuring efficient management and coordination of power distribution. Additionally, an individual CAN node is

Montoya, Armando Yassir↗

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats (Integration of Additive Manufacturing and Reinforced Polymer Composite)

Lightweight automotive seats offer multiple benefits to original equipment manufacturers in terms of cost savings from various aspects, including less material usage, more integrated processes, and compliance with Corporate Average Fuel Economy Standards. Original equipment manufacturers have been focusing on innovative ways to produce light weight automotive seats. The commercially available automotive seats are currently made of multiple metal components combined through welding and fasteners. The use of additive manufacturing and composite structures is particularly useful for light weighting the automotive components. Additive manufacturing (AM) offers multiple advantages over traditional manufacturing processes such as freedom of design thereby enabling complex structural geometries, mass customization and waste minimization, and control over the fiber alignment through deposition in a predetermined pattern. Combining metal inserts with polymer composites through a novel manufacturing process allows design of lightweight and high-performance materials for automotive components. However, fabricating these metal polymer composite structures through traditional manufacturing processes limits their mechanical properties due to limited design freedom, lack of control over fiber orientation in composite parts, and poor interfacial bonding between the constituent materials. It is essential to develop a novel manufacturing process to enable high throughput production of lightweight automotive seats using metal and polymer composites. As such it is important to design the automotive seat suitable for manufacturing via this process and perform mechanical characterization on various subcomponents of the seat to ensure that the design and performance requirements provided by the auto manufacturer are met. The aim of this project is to develop a novel manufacturing technique to produce lightweight automotive seat by combining AM with conventional manufacturing processes. The car seat back panel will be designed via topology optimization and numerical simulations to minimize the overall weight while ensuring it meets all the performance requirements. The optimization of the seat back structure will be based on computational stress analysis to maximize the stiffness and minimize the weight. Materials currently used by Ford Motor Company will be adopted for a few subcomponents while the in-house composite materials will be used for the rest of the seat back. The composite and metallic materials will be tested to determine their mechanical properties as these are necessary for simulations. A novel manufacturing process will be developed to integrate AM metal inserts with discontinuous reinforced composite through large scale additive manufacturing and compression overmolding processes. The developed manufacturing technique will be used to fabricated various subcomponents suitable for the seat back design and mechanically tested to determine their properties. The manufacturing of the lightweight seat back design through this process involves integrated AM metal inserts with the composite structure for recliner connection. The manufacturing of the entire seat back which is lightweight through the novel manufacturing process will be discussed. The performance of the designed seat back will be investigated through numerical simulations and shown to meet all the requirements provided by the auto manufacturer. The final goal of developing a novel manufacturing process for lightweight automotive seats is met through design optimization of seat back, manufacturing of subcomponents, mechanical characterization, and validation through numerical simulations. The routes to achieve the final goal of the project and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. The project resulted in the development of a novel manufacturing process to integrate metal inserts with tailored polymer composite preforms through overmolding. Leveraging this proven manufacturing process, a lightweight seat back was designed through topology optimization and numerical simulations. The designed seat back uses AM metal inserts and compression overmolding of tailored polymer composite preforms obtained via large scale additive manufacturing. The metal polymer composite structures fabricated through this process exhibited enhancement in stiffness and improved ductility upon testing. Overall, the project provided an alternative design and manufacturing technique for automotive seat back that enables weight saving while meeting the safety and performance requirements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats: Integration of Additive Manufacturing and Reinforced Polymer Composite

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Attention-based 3D – convolutional neural network model for mechanical property predictions using visible light images in metal additive manufacturing

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process parameters to predict the ultimate tensile strength (UTS), yield strength (YS), total elongation (TE), and uniform elongation (UE). The 3D-CNN layers of the model employed attention mechanisms to prioritize features in the data, thereby improving prediction accuracy. Furthermore, the effect of each process parameter and anomaly class is investigated using attention-based dynamic sigmoid weighted gates to interpret the influence each class has on the final prediction. Different combinations of the in situ data were fed into the 3D-CNN, with varying amounts of image layers, to determine the ideal combination for predicting mechanical properties in situ. Here, the 3D-CNN model achieved mean absolute percentage errors (MAPE) below 5% for both UTS and YS while using only a single camera input and under half of the available image layers.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Raspberry colloid-templated approach for the synthesis of palladium-based oxidation catalysts with enhanced hydrothermal stability and low-temperature activity

It is becoming increasingly urgent to develop and utilize novel, more efficient and stable materials for mobile and stationary emission control applications as the deleterious consequences of anthropogenic air pollution are becoming more evident and pressing. Tightening regulations, particularly related to automotive exhaust treatment, together with continued improvements in engine design, that result in lowering the engine operating temperatures and inadvertently lead to the release of an overwhelming proportion of pollutants during the cold start, present new challenges for materials design, specifically for oxidation catalysts. In particular, improvements in the low-temperature activity while maintaining catalyst stability at high temperatures are required from the next-generation catalyst. Typical catalysts for removal of pollutants from automotive exhaust streams incorporate platinum group metals (PGMs). They tend to be inefficient at low temperatures (below 250 °C), thus accounting for the cold start problem, yet sinter and lose their activity at high temperatures that are frequently encountered during catalyst operation. High PGM loadings are often employed to compensate for catalyst inefficiencies and fast degradation, ultimately resulting in high-cost catalytic converters. Here, we have developed a new approach for the design and formation of catalytic materials that allows for both significantly more efficient PGM incorporation and improved overall catalyst performance at reduced PGM loadings. The method provides control over the composition and geometry of the support through self-assembly of sacrificial composite template — “raspberry” polymeric colloids decorated with catalytic particles — accompanied by infiltration with metal-oxide precursor and subsequent removal of the colloids. This method simultaneously structures the porous network and organizes the catalytic particles within it. Uniquely, the resulting catalytic particles are partially embedded in the support matrix and partially exposed to the pore interior, producing catalytic sites that are both stable and accessible. Herein, the feasibility of this novel and versatile approach for automotive catalytic conversion is demonstrated: the studies include testing alumina-based raspberry-colloid-templated (RCT) catalysts containing Pd nanoparticles (RCT Pd/Al2O3) for oxidation of propane and carbon monoxide under simulated diesel exhaust conditions and hydrothermal aging at 800 °C for 50 h in the simulated stream. The RCT Pd/Al2O3 catalysts exhibit exceptional activity toward CO oxidation, reduced reaction onset temperature, and high stability to elevated temperatures (demonstrated through prolonged exposure to temperatures up to 950 °C) and reactive gas streams, without migration, sintering or loss of the precious metal NPs. Notably, the novel catalyst shows the same or slightly better performance than the commercial catalysts even when the PGM load is reduced by ~80 % compared to the commercial counterparts. These results provide confidence for the utilization of the RCT approach for the fabrication of robust nanostructured catalysts for next-generation, energy-efficient catalytic converters with improved performance at low and high temperatures and reduced costs. The RCT methodology is, in addition, highly generalizable, and can thus be applied for the design of a wide range of catalytic systems in the automotive sector and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Controlling the Plastic Anisotropy of Magnesium Alloy by Tailoring the Grain Size and Yttrium Content

Hexagonal close-packed (HCP) magnesium alloys are widely used in automotive and aerospace industries due to their low density and high specific-strength. Their applicability is mainly restricted due to poor formability and pronounced plastic anisotropy. The formability is usually improved by altering the chemistry (adding rare-earth elements like Y) or modulating the microstructure (e.g., grain refinement). However, grain refinement alone cannot yield the desired ductility, and the scarcity of rare-earth elements also limits the extent to which the alloying strategy can be used. To overcome these issues, in this work, it is proposed that the formability of Mg alloys can be improved by combining the grain refinement and alloying approaches. To quantitively explore this possibility, a crystal-plasticity-based constitutive model, which is sensitive to both alloying concentration and grain sizes, is developed. To demonstrate, the model is applied to study the combined effect of Y content and grain size on the mechanical responses of Mg alloy. The calculations are used to build maps of plastic anisotropy measures, such as tension–compression asymmetry ratio and Lankford coefficients, for a wide range of Y content and grain sizes. From these maps, the grain size that would yield the desired performance of Mg alloy for a fixed Y content can be identified. This work provides an accelerated pathway to optimize both the microstructure and chemistry simultaneously to achieve formability and to reduce the dependence on alloying.

36 MATERIALS SCIENCE↗

DOE OSTI2022

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗

DOE OSTI2023

Cradle-to-grave mercury emissions of light-duty gasoline and electric vehicles in China

China is actively promoting vehicle electrification, which is deemed to help achieve its ambitious carbon neutrality goal by 2060. Here we show that vehicle electrification in China leads to an increase in automotive emissions of mercury, a persistent, global hazardous pollutant regulated in the United Nations’ Minamata Convention. We found that with current technologies, life-cycle mercury emissions of battery electric vehicles of 300 miles of all-electric range are 92% higher than conventional gasoline internal combustion engine vehicles, primarily due to the high mercury emissions from coal-based electricity generation. Notably different from greenhouse gases, mercury emissions are mainly embedded in vehicular material production and vehicle manufacturing, accounting for 50–60% for electric vehicles and ~90% for gasoline vehicles of their life-cycle mercury emissions. Even with a deeply decarbonized power grid, mercury footprints of electric vehicles would still be higher than those of gasoline vehicles, implying a potential increase in automotive mercury emissions in any countries that promote vehicle electrification. Measures including decarbonizing electric grid, implementing mercury-specific emission control through vehicle supply chain, and increasing metal recycling in electric vehicle batteries will help mitigate the unintended mercury emission increase caused by vehicle electrification.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Development and Test of High Temperature Surface Acoustic Wave Gas Sensors

The demand for sensors in hostile environments, such as power plant environments, aerospace environments, oil and gas extraction, and high-temperature metallurgy environments, has risen over the past decades in a continuous attempt to increase process control, improve energy and process efficiency in production, reduce operational and maintenance costs, increase safety, and perform condition-based maintenance in equipment and structures operating in high-temperature harsh-environment conditions. The increased reliability, improved performance, and development of new sensors and networks with a multitude of components, especially wireless networks, are the target for operation in harsh environments. Gas sensors, in particular H2 sensors, operating above 200 C are required in the instrumentation, process control and general safety of a number of industries including coal, natural gas, and nuclear power generation facilities, the aerospace and automotive industries, metallurgical production and defense-related applications. The surface acoustic wave (SAW) platform is a particularly promising option for high-temperature, harsh-environment gas sensing applications since the platform exhibits advantages, such as battery-free and wireless operation, small size, possibility for scale production using well-developed technologies from the semiconductor industry, and low cost of installation and operation. In this work, one-port SAW resonators (SAWRs) operating along five different orientations on a commercially available langasite (LGS) wafer employing Pt-Al2O3 electrodes and reflectors were designed, fabricated, and used as high-temperature H2 sensors. Two of the selected orientations were predicted and confirmed to have temperature-compensated operation above 150 C. A gas sensor test setup was developed, capable of gas cycling between N2, O2 and N2/H2 mixtures under extended high-temperature periods (up to 650 C for over 20 hours). Thin film Pt-Al2O3 was used as the electrode material for the transducers and reflectors capable of high-temperature operation, and also as H2 sensing film. In addition, yttria-stabilized zirconia (YSZ) thin films with Pt decoration were tested as sensing films aimed to enhance the SAWR sensor response to H2. The SAW devices were monitored in excess of 1700 hours in real-time during gas cycling sequences up to 600 C, leading to the following findings: i) the Pt-Al2O3 electrodes performed better for H2 sensing than the Pt-decorated YSZ sensing film, showing as much as 50% higher frequency variation response in the 200 C to 400 C range; ii) different crystallographic orientations operating on the same LGS wafer experienced different responses to H2 exposures up to 500 C; iii) the surface oxidation state of the SAWR sensors was shown to have an important impact on subsequent H2 exposure responses. In addition, a sensor system employing two LGS SAWRs, aligned along two different orientations, has been developed to simultaneously determine H2 presence and temperature. Finally, wireless interrogation of a SAWR sensor was successful within the gas cycling test fixture, and successful wireless H2 detection was achieved above 400 C.

Ayes Moncada, Armando↗

DOE OSTI2025

Formation mechanisms of Sn-rich δ phase and its role in strengthening Cu-10Sn manufactured by laser powder bed fusion

Cu-Sn alloys produced via laser powder bed fusion (L-PBF) additive manufacturing (AM) have gained significant attention because they combine the advantages of AM relevant to intricate component design with outstanding combinations of strength, ductility, and resistance to wear and corrosion. However, a detailed understanding of the microstructure that contributes to the enhancement of the mechanical properties of L-PBF Cu-10Sn alloys remains unclear. In particular, there is a lack of understanding of the formation mechanisms of the Sn-rich δ phase commonly observed in Cu-10Sn. This study reveals two distinct variants of the δ phase possessing unique morphological characteristics. These characteristics are attributed to the local solidification conditions inherent to the melt pool boundaries versus those at the interiors of melt pools. A phase transformation pathway that elucidates the origin of the morphological variants of the δ phase from the Sn-rich metastable phases during the cyclic heating of the AM process is proposed. We report superior mechanical properties in L-PBF Cu-10Sn compared to those of conventionally manufactured counterparts due to the synergistic contributions from grain boundaries, dislocations, and the δ phase. Notably, the δ phase alone contributes approximately 22 % to the overall strength observed in the L-PBF Cu-10Sn alloy. The discovery of two types of distinct Sn-rich δ phase offers key insights into precise microstructural control in AM Cu-Sn alloys to enhance mechanical properties, providing practical strategies for improving material performance for diverse applications in automotive, aerospace, and machinery industries.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Genesis of a novel high-rate composite manufacturing process using large-scale additive manufacturing – compression molding (AM-CM) system: Possibilities and limitations

Oak Ridge National Laboratory (ORNL) has developed a highly automated manufacturing process for thermoplastic composites that combines the benefits of Additive Manufacturing and Compression Molding (AM-CM) to produce high-performance functional composite structures at automotive production rates. Here, the AM-CM process creates highly precise preforms by additively placing extruded fiber-filled polymers (with controlled fiber orientations and multi-material configurations) in the desired mold location before undergoing a secondary compression molding process immediately before the preform cools down. Preforms can be in the form of short, long-chopped, or continuous fiber-filled thermoplastic polymers (e.g., CF/GF-filled ABS, PC, LM-PAEK, etc.). The AM-CM process combines the benefits of controlled fiber alignment, that is only achievable in AM-printed parts with the classical CM process, which eliminates porosity and good surface finish. A preform created using AM-CM can integrate various materials to enable additional architectural functionalities, including over-molding, selective stiffening, and the incorporation of electrically or thermally conductive channels. All these advantages come with a fast part production cycle time. The AM-CM process can manufacture multi-material, multi-functional parts in under 3 min, starting from raw material (pellets) to the final product. The novel AM-CM process offers superior microstructural control and enhanced multi-functionality previously unattainable with any other traditional high-rate thermoplastic composite manufacturing method. This work covers the manufacturing concept, system development, materials and applications of AM-CM process in detail.

Kumar, Vipin [Oak Ridge National Laboratory (ORNL)↗

DOE OSTI2024

Abrasive Waterjet Machining

The abrasive waterjet machining process was introduced in the 1980s as a new cutting tool; the process has the ability to cut almost any material. Currently, the AWJ process is used in many world-class factories, producing parts for use in daily life. A description of this process and its influencing parameters are first presented in this paper, along with process models for the AWJ tool itself and also for the jet–material interaction. The AWJ material removal process occurs through the high-velocity impact of abrasive particles, whose tips micromachine the material at the microscopic scale, with no thermal or mechanical adverse effects. The macro-characteristics of the cut surface, such as its taper, trailback, and waviness, are discussed, along with methods of improving the geometrical accuracy of the cut parts using these attributes. For example, dynamic angular compensation is used to correct for the taper and undercut in shape cutting. The surface finish is controlled by the cutting speed, hydraulic, and abrasive parameters using software and process models built into the controllers of CNC machines. In addition to shape cutting, edge trimming is presented, with a focus on the carbon fiber composites used in aircraft and automotive structures, where special AWJ tools and manipulators are used. Examples of the precision cutting of microelectronic and solar cell parts are discussed to describe the special techniques that are used, such as machine vision and vacuum-assist, which have been found to be essential to the integrity and accuracy of cut parts. The use of the AWJ machining process was extended to other applications, such as drilling, boring, milling, turning, and surface modification, which are presented in this paper as actual industrial applications. To demonstrate the versatility of the AWJ machining process, the data in this paper were selected to cover a wide range of materials, such as metal, glass, composites, and ceramics, and also a wide range of thicknesses, from 1 mm to 600 mm. The trends of Industry 4.0 and 5.0, AI, and IoT are also presented.

36 MATERIALS SCIENCE↗