Abstract Shear localization is the dominant chip formation mechanism in machining of high-performance metallic components, such as those made of titanium and nickel-based alloys. This paper presents an analytical thermo-mechanical model considering a new tool-chip contact mechanism due to shear localization. First, it is experimentally shown that the sticking and sliding contact lengths fluctuate with the frequency of shear localization. Second, a cutting mechanics model is developed considering the shear band formation, its rolling on the tool’s rake face, and the time-varying tool-chip contact length with experimental validation. Finally, the transient temperature at the tool-chip interface is predicted by taking the rolling phenomenon and the time-varying heat sources at the tool-chip interface into account. The proposed model shows that at the beginning of every segmentation cycle, the entire tool-chip contact length is dominated by sliding condition with negligible sticking length. When the tool advances, new workpiece material piles up in its front with an increase in the sticking length. Meanwhile, the sliding length decreases due to the drop in the load-bearing capacity of the shear band. When enough material piles up in front of the tool, a new shear band forms, and the entire contact length returns to the sliding condition. This process repeats every time a shear band occurs, causing the cyclic formation of shear bands and time-varying nature of the tool-chip contact length, therefore influencing the temperature and stress evolution at the tool-chip interface.

Abstract This paper presents formulations for hybrid casting and additive manufacturing (AM) in the density-based topology optimization. A location-based Heaviside function is introduced to represent the parting surface. The optimized part on two sides of the parting surface can be fabricated with casting, additive manufacturing, or both. Through the location-based Heaviside function and density gradient, two global constraints are formulated to remove undercuts and overhangs for casting and AM, respectively, inside the design domain. Since density gradient vanishes on the design domain boundary, two extra density-based global constraints are developed to control the overhangs and undercuts outside the design domain. Due to the smoothed parameterization of the parting surface, we are able to optimize the part and partition surface (including location and parting direction) simultaneously for hybrid casting and additive manufacturing. The proposed formulations for hybrid manufacturing processes are validated through 2D and 3D numerical examples. The proposed approach further enlarges the design space with manufacturing constraints, and has the potential to be used in the design for hybrid and multi-component manufacturing.

Abstract Lattice structure metamaterials offer a variety of unique and tailorable properties, yet industrial adoption is slowed by manufacturability and inspection-related difficulties. Despite recent advances in laser powder bed fusion additive manufacturing, the sub-millimeter features of lattices are at the edge of process capabilities and suffer from low geometric quality. To better understand their complex process-structure-property (PSP) relationships, octahedron structures were manufactured across a power spectrum, inspected, and mechanically tested. X-ray computed tomography was used to characterize lattice geometry, and demonstrated that lattice strut geometry measures, increased significantly as a function of laser power. Furthermore, lattices are shown to exhibit a direct correlation between laser power and mechanical performance metrics. Performance variations up to 60% are shown as a function of process parameters despite nominally identical geometry. Significant geometry variations are found to be the cause of performance variation, while material properties as measured by microindentation hardness are constant across the studied parameter range. PSP relationships are modeled, and the limitations of these models are explored. It was found that resulting models can predict mechanical performance based on geometric characteristics with R2 values of up to 0.86. Finally, mechanistic causes of observed performance changes are discussed.

Abstract Toward achieving control over the kerfing through macro abrasive waterjet submerged milling, there is a need (i) to understand the influence of the water column height on the kerf quality and (ii) to develop a model for the prediction of the kerf characteristics. This study performs detailed experimentation to assess the kerf quality enhancement in submerged milling relative to the in-air milling on Al-6061 alloy. From the modeling perspective, there are very limited efforts in developing a comprehensive model that includes both the jet flow dynamics and material removal models—this is the missing link. Toward this, a comprehensive model is proposed and validated for the prediction of kerf in in-air and submerged conditions by considering (i) jet dynamics and (ii) jet–material interaction. From the experimental results, it is observed that by adopting the submerged milling, the damaged region, top kerf width and edge radius got reduced by 20.3%, 13.53%, and 22.7%, respectively. However, this enhancement in the kerf quality is associated with a reduction in the centerline erosion depth (hmax) by 12.33% and a material removal rate by 24.52%. The material removal mechanism is more uniform and directed in the submerged milling, whereas in-air is random. The proposed model predicted the kerf cross-sectional profile in submerged milling and in-air with a mean absolute error of 60 µm and 57 µm, squared Pearson correlation coefficient of 0.97 and 0.99, and the hmax with a maximum error of 1.3% and 1.4%.

Abstract The development of feasible methods for the design of power-skiving tools without cutting interference is essential in ensuring the accuracy of involute internal machined gears. One of the most crucial points in obtaining interference-free and resharpenable power-skiving tools is that of determining the cutting-edge and clearance surface. The present study introduces a tilt angle during the power-skiving process to design a simple cylindrical interference-free tool shape, in which the shape of the cutting edge remains unchanged after resharpening. The relative position between the new tool center point and gear during machining is similarly unchanged after resharpening. In addition, the clearance angle between the tool and the gear can be easily adjusted simply by changing the tilt angle of the tool during power-skiving. The validity of the proposed design method is demonstrated through a simple numerical example. The simulation results confirm the feasibility of the proposed method.

Abstract Industrial robots have become a suitable alternative to machine tools due to their flexibility, low cost, and large working space. However, the compliance of the robot system makes it prone to produce large deformations and vibrations during machining, resulting in poor machining accuracy and surface quality. In order to improve the machining performance of the robot, a posture optimization method for robotic milling with the redundant degree of freedom is introduced. First, modal tests are conducted at sampled points to obtain the configuration-dependent parameters of the structural dynamics of the robotic milling system. These experimental data are combined with the inverse distance weighted (IDW) model to further predict the modal parameters at the unsampled points. Then, considering the dynamics model of the system, the optimization model based on surface location error (SLE) is proposed to obtain the optimal robotic posture. Finally, a series of experiments illustrate that pose optimization based on SLE can improve the machining accuracy and surface machining quality.

Abstract Metal additive manufacturing (AM) has been receiving unprecedented attention for its transformational role in extending the AM materials from polymers to various metals. However, various quality issues, especially porosity, significantly impacts the mechanical properties and fatigue life of the final products, which imposes barriers for the widespread adoption of metal AM processes. In this study, we use the deep learning (DL) techniques to comprehensively investigate the relationships between pore microstructure and processing parameters. Specifically, a novel hybrid deep generative prediction network (HDGPN) that leverages both variational autoencoder and generative adversarial network is proposed to characterize the complex pore microstructure with in-depth representations and predict pore morphology under arbitrary processing parameters. By visualizing the predicted pore morphology, the complicated interaction dynamics between the processing parameters and pore microstructure are directly revealed, which may guide the optimization of metal AM manufacturing processes to fabricate defect-free products. A case study of a selective laser melting (SLM) process is conducted to validate the proposed modeling and prediction framework.

Abstract A high degree of automation, especially through the use of robots, is state of the art in the automotive and electronics industries. Even though the construction sector is currently lagging behind, an increase in automation and the use of robots can be observed. To assess the potential in industrial timber construction with a focus set on subtractive machining, this paper presents the state of the art in terms of the use of industrial robots and joinery machines, compact joinery machines, and gantry-type joinery machines in regard to milling processes. The capabilities of different types of joinery machines are evaluated and compared with vertical articulated industrial robots equipped with machining spindles. An overview of the history and an assessment of various parameters in the categories: Quality, System, Machining and Economy, is given. Publications, manufacturer information, and industry findings are reviewed to answer pressing questions on automation in timber construction. Limitations such as robotic stability or the limited working space of joinery machines and the affordability of robotic systems are elaborated and CAD/CAM (computer-aided design/computer-aided manufacturing) processes are scrutinized. After the evaluation and comparison of four different systems, an approach for future research is proposed. The main findings are a very low degree of robotization and data availability within the timber industry, lower acquisition costs for industrial robots while increasing the flexibility of the workspace, and task-specific advantages of the respective systems, including machining power, potential for improvement in automation (especially CAD/CAM), and machining quality.

Abstract The purpose of this research work is to characterize and inform the design of (mechanical) property-graded bulk structures made from a single metallic alloy via a laser powder bed fusion (LPBF) process, with an end goal of creating repeatable/reproducible functionally-graded additively manufactured (FGAM) parts. This paper specifically investigates the manufacture of stainless steel (SS) 316L structures via a pulsed selective laser melting (SLM) process, and the underlying causes of property variations (within a functionally-acceptable range) through various material characterization techniques. For this, a design of experiments spanning the volumetric energy density (VED) based process parameter design space was utilized to investigate the range of functionally-acceptable physical/mechanical properties achievable in SS 316L. Five sample conditions (made via different process parameter combinations) were down-selected for in-depth microstructure analysis and mechanical/physical property characterization; these were suitably selected to impart a wide and controllable property range (209–318 HV hardness, 90–99.9% relative density, and 154–211 GPa modulus). It was observed that property variations resulted from combinations of porosity types/amounts, martensitic phase fractions, and grain sizes. Based on these findings, property-graded standard test specimens were designed and manufactured for further investigation—tensile specimens having a monotonic hardness change along its gauge length, four-point bending specimens with varying elastic moduli as a function of the distance from the neutral axis, and Moore’s rotating beam fatigue specimens with moduli variations based on the distance from the center. Altogether, this work lays the foundation for understanding and designing the local and global mechanical performance of FGAM bulk structures.

Abstract List of 2022 reviewers