A comprehensive benchmarking study of a critical aerospace component, the pickle fork derivative, has been performed by fabricating it using various additive-subtractive hybrid manufacturing process routes. The objective was to evaluate and compare the process capability, geometric fidelity, and hybrid manufacturability of emerging deposition and forging technologies for structural applications. The selected processes included wire arc additive manufacturing (WAAM), wire-laser directed energy deposition (DED), laser powder bed fusion (LPBF), powder-blown DED, additive friction stir deposition (AFSD), and agility Forging. Wrought 316 L stainless steel and Al 6061 parts were machined and served as baselines. Each preform was fabricated, scanned using structured light, and subsequently finish-machined following a standardized hybrid processing route to ensure consistent benchmarking. The results reveal distinct process-dependent characteristics in build resolution, surface integrity, and overbuild allowance, which directly influence subsequent machining requirements and achievable dimensional accuracy. Fusion-based processes such as L-PBF and laser wire-DED produced near-net geometries with minimal overbuild, whereas WAAM, AFSD, and agility forging exhibited higher material allowances due to coarser resolution, thermal distortion, and build volume limitations. Despite these variations, all preforms were successfully machined to achieve dimensional conformity with the target CAD geometry, demonstrating the compatibility of additive and hybrid approaches for structural part fabrication. Further, preliminary mechanical performance evaluations were performed for hardness, strength, and fatigue resistance. The study highlights the critical role of process selection and preform accuracy in optimizing hybrid manufacturing workflows, providing key insights for the integration of additive, subtractive, and forging processes in aerospace component production.

Upcycling machining chips and metallic waste into usable feedstock for sustainable largescale additive manufacturing, particularly solid-state techniques such as additive friction stir deposition (AFSD), has advanced rapidly in recent years. However, the relative performance, energy use, and environmental implications of different feedstock preparation routes remain insufficiently understood. This study addresses this gap by comparing three pathways: hydraulic compaction into binder-free bars, mechanical briquetting, and conventional remelting with casting. The work begins by establishing process-structure relationships for compacted bars produced through optimized press-based consolidation, including evaluation of density, mechanical integrity, microstructural bonding, and hardness. These experimental results are then benchmarked against established recycling pathways using published data to compare energy consumption, material yield, greenhouse gas emissions, and production cost on a process-level basis. Compaction trials produced mechanically robust feedstock bars with consistent density and near-complete material retention, enabled by solid-state consolidation without melting. While direct process-level costs for compaction are higher than some energy-normalized estimates for briquetting, they are lower than remelting-based routes when energy consumption, consumables, and yield losses are considered. The combination of low energy demand, high material utilization, and negligible direct operational emissions positions hydraulic compaction as a practical and sustainable strategy for integrating recycled materials into solid-state additive manufacturing workflows.

Additive Manufacturing (AM) processes have versatile capabilities but are susceptible to the formation of as-cast non-equilibrium microstructures, process-induced defects, and porosity, which have deleterious effects on the mechanical performance. As part of our NSF-ERC-HAMMER program, isothermal forging was investigated as a novel post-processing technique for refining microstructure, reducing process defect severity, and thereby improving mechanical properties. Specimens of Laser Powderbed Fusion (LPBF) AlSi10Mg were fabricated over a range of process parameters and tensile tested as a baseline. Initial work focused on duplicate AM material that was then hot forged with 20% strain to investigate the effects of isothermal forging at one temperature and strain rate on the microstructure, tensile, and fatigue properties of the as-deposited materials. The microstructures, process-induced defect populations, and tensile/fatigue properties of both as-deposited and forged materials were quantified and analysed by OM, EBSD, XCT, and SEM by various NSF- ERC-HAMMER team members. Isothermal hot forging was found to induce recrystallisation and modify process-induced defect geometry along with increasing tensile ductility. The effects of AM deposition parameters and forge post-processing conditions on LPBF AlSi10Mg will be discussed in terms of microstructure, mechanical properties, and fractography. (c) 2024 The Authors. Published by ELSEVIER Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework's potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. This research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

In wire-arc additive manufacturing (WAAM), melt pool dynamics produce variations in bead geometry that create undesired part geometries and can also lead to material defects. In the current state of the art, reducing melt pool and bead variation is still an aspect of WAAM that is underdeveloped. Several factors influence the bead geometry including the type of material that is being deposited and process inputs. In this study, ER70S-3 mild steel was deposited onto steel build plates using a cold metal transfer (CMT) deposition process. Varying plate preheat surface temperatures were used from ambient temperature to 300 degrees C using different welding travel speeds. To control build plate surface temperature, a preheat stage was designed and implemented to simulate different interpass temperatures. Beads were deposited and subsequently measured to determine their geometric properties such as bead height and bead width. Initial results indicate that as the preheat temperature increases, resulting bead height decreases, bead height variation decreases, and bead width increases. Initial microstructural data also indicates that as preheat temperature increases, the microstructure becomes more equiaxed with decreasing grain sizes.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

An integrated, multi-system research platform has been developed to explore the fabrication of large-scale metal components though hybrid manufacturing. The resulting research cell is highly flexible as it incorporates multiple robot stations, a multi-axis part transfer system and a five-axis CNC machine tool. Current process capabilities include multi-material wire-arc additive manufacturing, fringe projection scanning metrology, robotic part handling and finish machining. A geometric digital twin is used to establish and transfer part positions, datums and coordinate axes across these processes. Mechanical and electrical system integration is complete and a sequential process flow has been demonstrated by fabricating a monolithic, single wall part geometry.

The objective of the wire arc additive manufacturing (WAAM) hybrid cell is to significantly reduce lead time associated with large scale parts. The WAAM process utilizes a 6 degree-of-freedom (DOF) robot manipulator in addition to a 2 DOF part positioner. After printing, the part is translated into position for scanning to inform the subtractive manufacturing process. Scanning in a manual setting can be a long and arduous process to generate scans of sufficient quality for the basis of informed machining. Effective path planning for scanning with the intent to reduce scanning time, increase quality of scans, and move toward a full automation of the hybrid manufacturing cell is investigated in this paper. Simulation software is used to create and verify path plans prior to importing and implementing on a 6 DOF robotic manipulator to which a GOM ATOS Q 3D scanner is mounted. The quality, amount of time necessary to produce, and number of scans required to produce a sufficient representation for machining are compared using three methods. The first method is a manual scanning configuration, the second is using a generalized path plan, and the third is using a geometry-based path plan.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Machining chatter is a phenomenon resulting from self-oscillation between a machining tool and workpiece. This self-oscillation results in variation on the machined product that reduces the ability to meet desired specifications. Chatter is a widely studied topic as it directly relates to the quality of machined products. This study details the application of a Random Forest (RF) classifier with Recursive Feature Elimination (RFE) to machining audio collected by a single microphone during down-milling operations. This approach allows straightforward feature elimination that results in an easily understood set of analyzed dimensions. Stability is predicted solely based on the classification output of the RF classifier. Our approach proves highly predictive with consistent machining setup and a small sample set. We also review transferability between machining setups and present key findings. Our RF approach demonstrates the ability to analyze and classify chatter through a low-cost approach with limited training data required. The motivation for using a single microphone is to enable detection on machines without other sensors, such as accelerometers, present in the machining setup. The value of the in-process sensor and chatter classifier is highlighted because the machining setup included asymmetric dynamics that reduced the accuracy of the traditional analytical stability solution. We see a natural progression to deploying this audio-only methodology with real-time processing and classification using either a laptop or smartphone. This progression will allow visual indicators during the machining process that can alert machinists of progression into unstable machining processes.

Laser powder bed fusion (L-PBF), also known as selective laser sintering or direct laser melting, is an additive manufacturing process in which part geometries are formed simultaneously with the underlying material. The microstructure, defect content, and surface quality are all synthesized conjointly with the part shape. While the geometric design freedom allowed by this process enables new complex features and parts with small (similar to 1 mm) features, challenges associated with process qualification can deter wider adoption. Furthermore, a lack of historical performance data for statistical process control of witness coupons, for either bulk material or for small features, makes the barrier to entry more difficult. Here, we demonstrate long-term, property-based process monitoring and variability assessment using both small-featured (1 mm) and larger, bulk-representative material witness coupons. Over a one-year period, more than 550 tensile bars and 80 Charpy impact bars were printed alongside 316 L stainless steel parts built using L-PBF and tested to detect shifts in the process over time. Miniature tensile bars with a 1 mm(2 )gage area were tested using a high throughput mechanical testing system. In parallel, a larger test coupon was used to monitor density, hardness, and Charpy impact toughness. This collection of measurements was used to determine detectable property shifts correlated to L-PBF process changes including powder feedstock, machine hardware, software versioning, and machine parameter settings. The benefits of using small featured, high-throughput samples are discussed based on process sensitivity and the number of repeat tests possible for each build. This study not only reveals the utility of property-based process monitoring but illustrates the sensitivity of these measurements to detect process changes and provides further evidence for property stability in modern L-PBF.

In the pursuit of improving additively manufactured (AM) component quality and reliability, fine-tuning critical process parameters such as laser power and scan speed is a great first step toward limiting defect formation and optimizing the microstructure. However, the synergistic effects between these process parameters, layer thickness, and feedstock attributes (e.g. powder size distribution) on part characteristics such as microstructure, density, hardness, and surface roughness are not as well-studied. In this work, we investigate 316L stainless steel density cubes built via laser powder bed fusion (L-PBF), emphasizing the significant microstructural changes that occur due to altering the volumetric energy density (VED) via laser power, scan speed, and layer thickness changes, coupled with different starting powder size distributions. This study demonstrates that there is not one ideal process set and powder size distribution for each machine. Instead, there are several combinations or feedstock/process parameter 'recipes' to achieve similar goals. This study also establishes that for equivalent VEDs, changing powder size can significantly alter part density, GND density, and hardness. Through proper parameter and feedstock control, part attributes such as density, grain size, texture, dislocation density, hardness, and surface roughness can be customized, thereby creating multiple high-performance regions in the AM process space.

Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. In this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.

The nature of melt pool boundaries (MPBs) in additively manufactured alloys are characteristically delineated by differences in grain or subgrain orientation, size (thickness), and morphology. They may also possess more subtle attributes, such as enrichment of secondary phases or non-uniform residual stresses, which have yet to be fully realized. Several groups have recently reported the MPBs in laser - powder bed fusion (L-PBF) stainless steels suffer preferential localized attack under certain oxidizing and acidic conditions. The enhanced corrosion susceptibility has been attributed to secondary phases, porosity, or non-uniformities in residual stress at the MPB, but without definitive evidence.Here we present electrochemical and detailed electron microscopy results that show the characteristics and origin of MPB corrosion susceptibility in a L-PBF austenitic stainless steels to be caused by local Cr and Mo solute depletion. The solute concentration was simulated based on the Scheil equation applied to solidification at MPBs. High resolution elemental analysis of a MPB determined the Cr and Mo concentration to both be reduced by 2 at% compared to the bulk concentration, matching well with simulation. Crevice corrosion experiments showed the MPBs to have increased susceptibility to corrosion attack in an aggressive halide aqueous environment.