Abstract The machining mechanics of carbon fiber reinforced polymer (CFRP) materials is influenced by the coupled effects of the workpiece anisotropy, tool edge geometry, and cutting parameters. Predicting the chip formation mechanism is crucial for optimizing cutting parameters, reducing tool wear, and improving efficiency and surface quality. This study quantitatively evaluates the effect of main CFRP failure criteria on the chip formation mechanism in modeling the machining mechanics of CFRP. The results show that the Hashin-Puck and Davila criteria excel at capturing chip formation across all fiber orientations because of the incorporation of ‘internal friction’ concept, while others only achieve accurate predictions in specific fiber orientation ranges due to improper shear strength consideration. The sources of the prediction similarities, differences, and limitations of failure criteria are experimentally validated. Sensitivity analyses quantitatively determine the effect of the tool rake angle on the machining energy consumption and cutting forces across the fiber orientation range. This research can be used to select the optimal failure criteria, design proper cutting tool geometry, and inform the cutting parameter choices for CFRP machining operations.

Abstract Aluminum is the world's second most consumed metal, and its production contributes substantially to global greenhouse gas (GHG) emissions. When formulating decarbonization strategies, it is imperative to ensure their coherence and alignment with existing industrial practices and standards. A material flow analysis (MFA) is needed to gain a holistic and quantitative understanding of the flows and stocks of products/materials associated with all participants within the supply chain. To support risk-informed decision policymaking in decarbonizing aluminum manufacturing, this study develops a dynamic system model that maps global aluminum flows and computes their embedded GHG emissions. A baseline scenario is devised to reflect the current business and operation landscape, and three decarbonization strategies are proposed. Deterministic simulation is performed to generate dynamic material flows and performance metrics. Monte Carlo simulation is then implemented to evaluate the robustness of the system's performance under demand uncertainties. The results reveal the immense carbon implications of material efficiency, as well as the preponderant role of post-consumer scrap recycling in decarbonizing aluminum manufacturing. Informed by simulation outputs, macro decarbonization guidelines are formulated for various criteria. The object-oriented programming framework that underlies the dynamic MFA may be integrated with network analysis, agent-based simulation, and geospatial interfaces, which may lay the foundation for modeling more fine-grained material flows and supply chain structures.

Abstract Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness in identifying and characterizing defect regions in L-PBF-ed Cobalt-Chrome (CoCr) parts.

Abstract This study models the temperature evolution during additive friction stir deposition (AFSD) using machine learning. AFSD is a solid-state additive manufacturing technology that deposits metal using plastic flow without melting. However, the ability to predict its performance using the underlying physics is in the early stage. A physics-informed machine learning approach, AFSD-Nets, is presented here to predict temperature profiles based on the combined effects of heat generation and heat transfer. The proposed AFSD-Nets includes a set of customized neural network approximators, which are used to model the coupled temperature evolution for the tool and build during multi-layer material deposition. Experiments are designed and performed using 7075 aluminum feedstock deposited on a substrate of the same material for 30 layers. A comparison of predictions and measurements shows that the proposed AFSD-Nets approach can accurately describe and predict the temperature evolution during the AFSD process.

Abstract This paper presents a new method to efficiently update workpiece and determine cutter-workpiece engagement (CWE) in multi-axis milling simulation based on a uniform voxel modeling space. At each cutter location, a novel algorithm named direct voxel tracing is developed and used to generate a functional cutter surface voxel model to reliably establish the internal space of the milling cutter. The cutter internal space is represented by its voxel boundary with small memory usage. Through the Boolean subtraction between two successive voxel boundaries of the cutter internal space, a minimal voxel deactivation region is attained within which all active workpiece voxels are deactivated (removed) to update the workpiece model. To determine the associated CWE map, a 3D circle voxelization algorithm is employed. By slicing the cutter surface by a sequence of planes perpendicular to and along the cutter axis, CWE can be determined as the sliced 3D circles are voxelized. Quantitative comparisons of the proposed method against existing voxel modeling and vector modeling-based methods have been made. The results have demonstrated much improved computational efficiency of the proposed method in simulating the complex multi-axis milling operations.

Abstract Kuo King (“K.K.”) Wang was a pioneer in injection molding of polymers and non-polymer materials that helped the manufacturing industry around the world evolve to its current practices. He built an interdisciplinary research team at Cornell University that used a scientific base for analyzing the injection molding process by integrating and extending existing knowledge. He also pioneered and established a renowned consortium of global corporations. The corporations incorporated the research findings into their manufacturing processes. The relationships continued for decades and the research results are widely implemented in manufacturing around the world.

Abstract By directed energy deposition (DED), a flexible design of cooling channels in forming tools, e.g., hot stamping, with a variety of sizes and a high positioning flexibility compared to machining processes is possible. The subsequent ball burnishing of the tool surfaces, in combination with a variation of the DED process parameters, enables control of the tool surface properties and friction behavior. Parameters such as the ball-burnishing pressure or the path overlapping in the DED process are investigated to quantify their effects on roughness, hardness, friction, residual stresses, and heat transfer coefficient of generic tool surfaces. The friction coefficient at elevated temperatures depends strongly on the surface roughness of the tool steel surfaces generated by DED and ball burnishing. The latter process improves the surface integrity: the roughness peaks are leveled by up to 75%, and the hardness and the residual stresses are enhanced by up to 20% and 70%, respectively. However, the roughness of the tool surfaces is determined mainly by the path overlapping of the welded beads in the DED process. Despite the higher surface roughness, the heat transfer coefficient is in the range of conventionally manufactured tool surfaces of up to 2700 W/m2K for contact pressures up to 40 MPa. First hot stamping experiments demonstrate that the tools manufactured by the novel process combination are able to manufacture 22MnB5 hat profiles with an increased and more homogeneous hardness, as well as more homogeneous thickness distribution, compared to conventionally manufactured tools.

Abstract Magnetorheological shear thickening polishing (MRSTP) is a novel hybrid polishing method that combines the magnetorheological effect and the shear thickening effect. It has great potential for ultra-precision machining of complex surfaces. However, the absence of a correlation between material removal and the rheological properties of the polishing media has posed difficulties for further improvements in polishing efficiency and quality in MRSTP. In this work, a material removal model for MRSTP was established based on the principles of magneto-hydrodynamics, non-Newtonian fluid kinematics, and microscopic contact mechanics. This model combines the material removal model for a single abrasive particle with a statistical model of active grits. When comparing the experimental and theoretical results, it became evident that the developed material removal model can accurately predict the material removal depth of the workpiece under different processing parameters such as rotational speed of the rotary table and magnetic field strength. The average prediction error was found to be less than 5.0%. Furthermore, the analysis of the rheological behavior and fluid dynamic pressure of the polishing media reveals the coupling effects between the magnetic, stress, and flow fields. This provides theoretical guidance for the actual processing of MRSTP. Finally, the maximum material removal rate of 3.3 μm/h was achieved on the cylindrical surface of the Ti–6Al–4V workpiece using the MRSTP method. These results demonstrate that the MRSTP method holds great potential in the field of ultra-precision machining of difficult-to-machine materials.

Abstract Laser-induced forward transfer (LIFT) is proposed as a highly efficient and high-resolution printing technique. Tilting of the microjet in the LIFT process affects the deposition deviation, lowering the printing resolution. In this paper, the tilting behaviors of the metal microjet in the nanosecond LIFT process are investigated based on a high-speed observation. Experiments were conducted on the copper film under different laser fluences. Observations based on the pump-probe method were performed to capture the ejection behavior of microjets. It is found that the tilting direction is isotropic, and the tilting angle follows Gaussian distribution. The tilting behavior originates from the disturbance of residual stress within the film during jet generation because the statistical result of the tilting angle hardly varies with the propagation time. In addition, the tilting angle is found to decrease linearly with the laser fluence due to the ejection velocity increasing at a higher rate than the lateral velocity. The lateral offset of the tilting microjet at different flight distances matches well with the position deviations, verifying the tilting behavior of the microjet. This study provides essential comprehension of the tilting behavior of metal microjet in the LIFT process.

Abstract Natural fiber-reinforced plastic (NFRP) composites are ecofriendly and biodegradable materials that offer tremendous ecological advantages while preserving unique structures and properties. Studies on using these natural fibers as alternatives to conventional synthetic fibers in fiber-reinforced materials have opened up possibilities for industrial applications, especially for sustainable manufacturing. However, critical issues reside in the machinability of such materials because of their multiscale structure and the randomness of the reinforcing elements distributed within the matrix basis. This paper reports a comprehensive investigation of the effect of microstructure heterogeneity on the resultant behaviors of cutting forces for NFRP machining. A convolutional neural network (CNN) links the microstructural reinforcing fibers and their impacts on changing the cutting forces (with an estimated R-squared value over 90%). Next, a model-agnostic explainable machine learning approach is implemented to decipher this CNN black-box model by discovering the underlying mechanisms of relating the reinforcing elements/fibers’ microstructures. The presented xml approach extracts physical descriptors from the in-process monitoring microscopic images and finds the causality of the fibrous structures’ heterogeneity to the resultant machining forces. The results suggest that, for the heterogeneous fibers, the tightly and evenly bounded fiber elements (i.e., with lower aspect ratio, lower eccentricity, and higher compactness) strengthen the material and thereafter play a significant role in increasing the cutting forces during NFRP machining. Therefore, the presented framework of the explainable machine learning approach opens an opportunity to discover the causality of material microstructures on the resultant process dynamics and accurately predict the cutting behaviors during material removal processes.