Showing papers in "Journal of Manufacturing Science and Engineering-transactions of The Asme in 2020"

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Abstract: Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

Metal Binder Jetting Additive Manufacturing: A Literature Review

Abstract: Binder jetting is an additive manufacturing process utilizing a liquid-based binding agent to selectively join the material in a powder bed. It is capable of manufacturing complex-shaped parts from a variety of materials including metals, ceramics, and polymers. This paper provides a comprehensive review on currently available reports on metal binder jetting from both academia and industry. Critical factors and their effects in metal binder jetting are reviewed and divided into two categories, namely material-related factors and process-related parameters. The reported data on density, dimensional and geometric accuracy, and mechanical properties achieved by metal binder jetting are summarized. With parameter optimization and a suitable sintering process, ten materials have been proven to achieve a relative density of higher than 90%. Indepth discussion is provided regarding densification as a function of various attributes of powder packing, printing, and post-processing. A few grades of stainless steel obtained equivalent or superior mechanical properties compared to cold working. Although binder jetting has gained its popularity in the past several years, it has not been sufficiently studied compared with other metal additive manufacturing (AM) processes such as powder bed fusion and directed energy deposition. Some aspects that need further research include the understanding of powder spreading process, binder-powder interaction, and part shrinkage.

Additive Manufacturing Review: Early Past to Current Practice

Abstract: Additive manufacturing (AM) is a set of manufacturing processes that are capable of producing complex parts directly from a computer model of the part. This review provides a history of the early antecedents of these processes. In addition, the different classes of AM processes and their commercialization are presented and discussed along with their fields of use. This paper emphasizes AM processes that produce production quality parts. The review also addresses design issues and the commercial state of the art for production of polymer, metal, and ceramic parts. A main emphasis of this paper is the development and motivations for AM especially during its nascent years. The paper is written for the general readership of manufacturing professionals and researchers.

Understanding Laser Powder Bed Fusion Surface Roughness

Abstract: Surface roughness is a well-known consequence of additive manufacturing methods, particularly powder bed fusion processes. To properly design parts for additive manufacturing, a comprehensive understanding of the inherent roughness is necessary. While many researchers have measured different surface roughness resultant from a variety of parameters in the laser powder bed fusion process, few have succeeded in determining causal relationships due to the large number of variables at play. To assist the community in understanding the roughness in laser powder bed fusion processes, this study explored several studies from the literature to identify common trends and discrepancies amongst roughness data. Then, an experimental study was carried out to explore the influence of certain process parameters on surface roughness. Through these comparisons, certain local and global roughness trends have been identified and discussed, as well as a new framework for considering the effect of process parameters on surface roughness.

Chatter Stability of Machining Operations

Abstract: This paper reviews the dynamics of machining and chatter stability research since the first stability laws were introduced by Tlusty and Tobias in the 1950s. The paper aims to introduce the fundamentals of dynamic machining and chatter stability, as well as the state of the art and research challenges, to readers who are new to the area. First, the unified dynamic models of mode coupling and regenerative chatter are introduced. The chatter stability laws in both the frequency and time domains are presented. The dynamic models of intermittent cutting, such as milling, are presented and their stability solutions are derived by considering the time-periodic behavior. The complexities contributed by highly intermittent cutting, which leads to additional stability pockets, and the contribution of the tool's flank face to process damping are explained. The stability of parallel machining operations is explained. The design of variable pitch and serrated cutting tools to suppress chatter is presented. The paper concludes with current challenges in chatter stability of machining which remains to be the main obstacle in increasing the productivity and quality of manufactured parts.

Ceramic Binder Jetting Additive Manufacturing: A Literature Review on Density

Abstract: The objective of this review paper is to summarize the current status and identify the knowledge gaps in ceramic binder jetting additive manufacturing, with a particular focus on density. This paper begins with an overview of ceramic binder jetting. Then, it discusses different aspects of density, including various terminologies, measurement methods, and achieved values. Afterward, it reviews two categories of techniques to increase the part density: material preparation techniques (powder granulation, mixing powders of different sizes, using slurry feedstock, and mixing different materials) and postprocessing techniques (sintering, chemical reaction, infiltration, and isostatic pressing). Finally, it presents the knowledge gaps in the literature.

Shape and Performance Controlled Advanced Design for Additive Manufacturing: A Review of Slicing and Path Planning

Abstract: Additive manufacturing (AM) brings out a revolution of how the products are designed and manufactured. To obtain desired components, advanced design for additive manufacturing (ADfAM) is widely emphasized in geometry, material, and function design. 3D slicing and path planning, which are the critical steps of ADfAM, directly determine manufacturing process variables, shape, and performance of printed parts. For widely used planar slicing, the contradiction between accuracy and build time has attracted considerable attention and efforts, leading to various novel and optimization methods. Nevertheless, curved surfaces and slopes along the build direction constrain the surfaces to be smooth due to the inherent staircase effect of AM. Meanwhile, there is significant anisotropy of the printed piece making it sensitive to any shear (or bending) stress. Moreover, support structures for the overhang part are necessary when building along one direction, resulting in time-consuming and cost-expensive process. Due to the rapid development of 3D slicing and path planning, and various newly proposed methods, there is a lack of comprehensive knowledge. Notwithstanding, there are fewer literature reviews concerning planar slicing and filling strategy. Less attention has been paid to non-planar slicing, path planning on curved surfaces, and multi-degree of freedom (DOF) AM equipment, as well as printing under pressure. Hence, it is significant to get a comprehensive understanding of current status and challenges. Then, with suitable technologies, the printed parts with improved surface quality, minimum support structures, and better isotropy could be acquired. Finally, the recommendation for the future development of slicing and path planning is also provided.

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Abstract: Welding is a major manufacturing process that joins two or more pieces of materials together through heating/mixing them followed by cooling/solidification. The goal of welding manufacturing is to join materials together to meet service requirements at lowest costs. Advanced welding manufacturing is to use scientific methods to realize this goal. This paper views advanced welding manufacturing as a three step approach: (1) pre-design that selects process and joint design based on available processes (properties, capabilities, and costs); (2) design that uses models to predict the result from a given set of welding parameters and minimizes a cost function for optimizing the welding parameters; and (3) real-time sensing and control that overcome the deviations of welding conditions from their nominal ones used in optimizing the welding parameters by adjusting the welding parameters based on such real-time sensing and feedback control. The paper analyzes how these three steps depend on process properties/capabilities, process innovations, predictive models, numerical models for fluid dynamics, numerical models for structures, real-time sensing, and dynamic control. The paper also identifies the challenges in obtaining ideal solutions and reviews/analyzes the existing efforts toward better solutions. Special attention and analysis have been given to (1) gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) as benchmark processes for penetration and materials filling; (2) keyhole plasma arc welding (PAW), keyhole-tungsten inert gas (K-TIG), and keyhole laser welding as improved/capable penetrative processes; (3) friction stir welding (FSW) as a special penetrative low heat input process; (4) alternating current (AC) GMAW and double-electrode GMAW as improved materials filling processes; (5) efforts in numerical modeling for fluid dynamics; (6) efforts in numerical modeling for structures; (7) challenges and efforts in seam tracking and weld pool monitoring; (8) challenges and efforts in monitoring of keyhole laser welding and FSW; and (9) efforts in advanced sensing, data fusion/sensor fusion, and process control using machine learning/deep learning, model predictive control (MPC), and adaptive control.

Intelligent Maintenance Systems and Predictive Manufacturing

Abstract: With continued global market growth and an increasingly competitive environment, manufacturing industry is facing challenges and desires to seek continuous improvement. This effect is forcing manufacturers to squeeze every asset for maximum value and thereby calls for high-equipment effectiveness, and at the same time flexible and resilient manufacturing systems. Maintenance operations are essential to modern manufacturing systems in terms of minimizing unplanned down time, assuring product quality, reducing customer dissatisfaction, and maintaining advantages and competitiveness edge in the market. It has a long history that manufacturers struggle to find balanced maintenance strategies without significantly compromising system reliability or productivity. Intelligent maintenance systems (IMS) are designed to provide decision support tools to optimize maintenance operations. Intelligent prognostic and health management tools are imperative to identify effective, reliable, and cost-saving maintenance strategies to ensure consistent production with minimized unplanned downtime. This article aims to present a comprehensive review of the recent efforts and advances in prominent methods for maintenance in manufacturing industries over the last decades, identifying the existing research challenges, and outlining directions for future research.

General Cutting Dynamics Model for Five-Axis Ball-End Milling Operations

Abstract: Five-axis ball-end milling is used extensively to machine parts with sculptured surfaces. This paper presents the general cutting dynamics model of the ball-end milling process for machine tools with different five-axis configurations. The structural dynamics of both the tool and workpiece are considered for the prediction of chatter stability at each tool location along the tool path. The effects of tool–workpiece engagement and tool axis orientation are included in the model. By sweeping the spindle speeds, the chatter-free spindle speeds are selected followed by the prediction of forced vibrations in five-axis milling of thin-walled, flexible parts. The proposed model has been experimentally illustrated to predict the chatter stability and forced vibrations on a table-tilting five-axis computer numerical control machine tool.

Effect of Micro-Textured Tool Parameters on Forces, Stresses, Wear Rate, and Variable Friction in Titanium Alloy Machining

Abstract: Micro-textures applied to cutting tool surfaces provide certain advantages such as reducing tool forces, stresses, and temperature hence overall friction between the tool–chip contact and improving chip adherence and associated tool wear. This study explores the effect of micro-texture geometry parameters fabricated on the rake face of tungsten carbide inserts that were tested in dry turning titanium alloy Ti-6Al-4V. The effects of micro-texture geometry on the cutting forces, tool stresses, tool temperatures, tool wear rate, and variable friction coefficient were studied with 3D finite element (FE) simulations. The simulation model was validated comparing cutting forces predicted and measured. The results indicated some effects of micro-textured tool geometry parameters being significant and others are not as significant. The experiments reveal that the effects of micro-groove width, depth, and distance from cutting edge are found to be significant on cutting forces, but the spacing is not as much. The effect of increasing feed rate on cutting force and tool wear was significant and suppressed the advantages offered by micro-grooved texture tool geometry. The simulation results indicate that the effect of micro-texture parameters such as groove depth and distance from cutting edge is significant on tool temperature and wear rate. The variable nature of friction coefficient was emphasized and represented as functions of state variables such as normal stress and local temperature as well as micro-texture parameters.

Opportunities and Challenges in Metal Forming for Lightweighting: Review and Future Work

Abstract: The purposes of this review are to summarize the historical progress in the last 60 years of lightweight metal forming, to analyze the state-of-the-art, and to identify future directions in the context of Cyber-physically enabled circular economy. In honoring the 100th anniversary of the establishment of the Manufacturing Engineering Division of ASME, this review paper first provides the impact of the metal forming sector on the economy and historical perspectives of metal forming research work published by the ASME Journal of Manufacturing Science and Engineering, followed by the motivations and trends in lightweighting. To achieve lightweighting, one needs to systematically consider: (1) materials and material characterization; (2) innovative forming processes; and (3) simulation tools for integrated part design and process design. A new approach for process innovation, i.e., the Performance-Constraints-Mechanism-Innovation (PCMI) framework, is proposed to systematically seek new processes. Finally, trends and challenges for the further development in circular economy are presented for future exploration.

Overview of Laser Applications in Manufacturing and Materials Processing in Recent Years

Abstract: This article is to capture some of the important developments in the rapidly growing areas of laser-based manufacturing and materials processing and also to describe important technological issues pertaining to various laser-based manufacturing processes. The topics to be covered in this paper include more popularly used processes in industry such as laser additive manufacturing, laser-assisted machining, laser micromachining, laser forming, laser surface texturing, laser welding, and laser shock peening, although there are several additional areas of laser applications. In each section, a brief overview of the process is provided, followed by critical issues in implementing the process, such as properties, predictive modeling, and process monitoring, and finally some remarks on future issues that can guide researchers and practitioners.

Progress Toward Modeling and Optimization of Sustainable Machining Processes

Abstract: The topic of sustainable machining has in recent times emerged as a significant and impactful area of research focus as it directly deals with environmental health and protection, economic growth and prosperity, and societal wellbeing with greater health and wellness. More specifically, sustainable machining at product, process, and system levels deals with reducing negative environmental impact, offering improved energy and resource efficiency, generating a minimum quantity of wastes, providing operational safety, and offering improved personal health. This paper summarizes recent efforts by the world research community in sustainable machining with a systematic approach for the analysis of machining processes that are broadly classified as sustainable, beginning with dry machining, and then near-dry (also known as minimum quantity lubrication (MQL)) and cryogenic machining processes. The paper also extends its analysis to a hybrid mode of sustainable machining that effectively combines cryogenic and MQL machining processes for improved productivity and machining performance. While a significant part of this paper presents experimental analysis, the progress being made in modeling and optimization has also been discussed in the paper. In particular, major challenges involved in model development for practical implementation, with a view to selecting optimum cutting conditions and cutting tool selection, are primarily discussed in the paper. The need for continued modeling efforts for achieving deployable optimized conditions for sustainable machining is highly recognized, and further research is required in numerous fronts integrating the various convergent disciplines such as materials, mechanics, computational sciences, economics, environmental sciences.

Self-Support Topology Optimization With Horizontal Overhangs for Additive Manufacturing

Abstract: Most of the existing self-support topology optimization methods restrict the overhang inclination angle to be larger than the self-support threshold value. However, for some additive manufacturing processes, such as fused deposition modeling, horizontal overhangs with zero inclination angle could be successfully printed while the overhang size plays a key role in determining the printability. Therefore, the self-support threshold condition should be re-developed to comprehensively consider the overhang size and inclination angle. At the same time, there raises the challenges of formulating the self-support constraints based on the new threshold condition. To address this difficulty, a novel method is proposed in this work to realize the design with horizontal overhangs. To be specific, the new method employs a skeleton-based structure decomposition approach to divide the structure into components based on the connectivity condition. Then, each component will be evaluated about its self-support status based on its overhang length and inclination angle. Finally, the self-support constraint will be activated only for those components that violate the threshold condition. An excellent feature of the method is that it can be adapted to address the only inclination angle self-support condition, or the comprehensive self-support condition that simultaneously considers the overhang length and inclination angle. Therefore, the new method serves for general applications to different additive manufacturing (AM) processes. Numerical examples will be studied to demonstrate the effectiveness of the proposed method.

Additive Manufacturing of Polymer-Metal/Ceramic Functionally Graded Composite Components via Multiple Material Laser Powder Bed Fusion

Abstract: In this paper, the printing of 3D functionally graded polymer/metal, polymer/ceramic composite components via an ultrasonic vibration-assisted laser-based multiple material powder bed fusion (PBF) is reported. Components consisted of various polymer composites with different compositions according to design was realized. High concentrations (up to 90%) of solid particle additives, including soda-lime glass, aluminum oxide, and copper powders, were mixed with the polymer and printed, which was difficult or impossible to realize using conventional injection molding or standard fused filament fabrication (FFF) 3D printing. Effects of laser melting/sintering parameters and manufacturing strategy of each type of polymeric composite were investigated. A successful delivery of very difficult-to-feed fine powder mixtures such as PA11/Al2O3 with irregular powder geometry via a new configuration of ultrasonic feeding was demonstrated. Three examples of 3D functionally graded components (part of a shoe sole, a turbine blade, and components of a ball bearing) were printed to illustrate the potential applications of the method.

Industrial Sustainability: Reviewing the Past and Envisioning the Future

Abstract: Society's consumption of natural resources and the impact of industrial activities on the environment have gained increasing attention over the last several decades. This paper provides a historical perspective on the origins of the environmental movement and its connection to industrial systems. Then, recent research related to product design, process improvement and change, green manufacturing planning, and the circular economy are described. With respect to product design, topics such as material selection and component light-weighting are considered. For process-related research, efforts such as operation changes and equipment design for reduced energy consumption are discussed. For manufacturing planning, new developments in process planning and production scheduling are highlighted that consider environmental performance. The concept of circular economy is examined critically, with particular emphasis placed on closing materials loops via recycling and remanufacturing. The paper concludes with a discussion of challenges and opportunities to achieve the goal of industrial sustainability.

Internal Surface Quality Enhancement of Selective Laser Melted Inconel 718 by Abrasive Flow Machining

Abstract: Additive manufacturing (AM) technology enables a new way for fabricating components with complex internal surfaces. Selective laser melting (SLM), being one of the most common AM techniques, is able to fabricate complex geometries with superior material properties. However, due to the poor surface quality, the fabricated internal surfaces cannot meet the specifications for some real applications. To achieve the required internal surface condition, post-polishing process is essential. As one of the most prominent processes for finishing inaccessible surfaces with a wide range of materials, abrasive flow machining (AFM) shows great potential to polish AM internal surfaces. Hence, this paper presents an analytical and experimental study on the internal surface quality improvement of SLM Inconel 718 by AFM, aiming to verify the feasibility of AFM on internal surface quality improvement. The surface evolution process was modeled, and the effects of process parameters on surface and subsurface quality were evaluated. The results show that good surface roughness was obtained at the medium conditions of high viscosity, large particle size, low extrusion pressure, and low temperature. The surface morphology was greatly affected by the medium particle size which showed consistency with the surface evolution model that small abrasive particles are unable to overcome the width and depth of the valleys, resulting in the formation of craters. The partially melt layer was effectively removed, and no subsurface damage was induced.

Acoustic Emission Characterization of Natural Fiber Reinforced Plastic Composite Machining Using a Random Forest Machine Learning Model

Abstract: Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique structural properties. These materials are machined to create components that meet the dimensional and surface finish tolerance specifications for various industrial applications. The heterogeneous structure of these materials—resulting from different fiber orientations and their complex multiscale structure—introduces a distinct set of material removal mechanisms that inherently vary over time. This structure has an adverse effect on the surface integrity of machined NFRPs. Therefore, a real-time monitoring approach is desirable for timely intervention for quality assurance. Acoustic emission (AE) sensors that capture the elastic waves generated from the plastic deformation and fracture mechanisms have potential to characterize these abrupt variations in the material removal mechanisms. However, the relationship connecting AE waveform patterns with these NFRP material removal mechanisms is not currently understood. This paper reports an experimental investigation into how the time–frequency patterns of AE signals connote the various cutting mechanisms under different cutting speeds and fiber orientations. Extensive orthogonal cutting experiments on unidirectional flax fiber NFRP samples with various fiber orientations were conducted. The experimental setup was instrumented with a multisensor data acquisition system for synchronous collection of AE and vibration signals during NFRP cutting. A random forest machine learning approach was employed to quantitatively relate the AE energy over specific frequency bands to machining conditions and hence the process microdynamics, specifically, the phenomena of fiber fracture and debonding that are peculiar to NFRP machining. Results from this experimental study suggest that the AE energy over these frequency bands can correctly predict the cutting conditions to ∼95% accuracies, as well as the underlying material removal regimes.

A Review of Modern Communication Technologies for Digital Manufacturing Processes in Industry 4.0

Abstract: Digital manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create more lean, profitable, and data-driven manufacturing processes. This paper reviews modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to cyber-physical systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed, and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations.

Ultra-Precision Machining: Cutting With Diamond Tools

Abstract: This article is written as a tribute to Professor Frederick Fongsun Ling 1927–2014. Single-point diamond machining, a subset of a broader class of processes characterized as ultraprecision machining, is used for the creation of surfaces and components with nanometer scale surface roughnesses, and submicrometer scale geometrical form accuracies. Its initial development centered mainly on the machining of optics for energy and defense related needs. Today, diamond machining has broad applications that include the manufacture of precision freeform optics for defense and commercial applications, the structuring of surfaces for functional performance, and the creation of molds used for the replication of a broad range of components in plastic or glass. The present work focuses on a brief review of the technology. First addressed is the state of current understanding of the mechanics that govern the process including the resulting forces, energies and the size effect, forces when cutting single crystals, and resulting cutting temperatures. Efforts to model the process are then described. The workpiece material response when cutting ductile and brittle materials is also included. Then the present state of the art in machine tools, diamond tools and tool development, various cutting configurations used, and some examples of diamond machined surfaces and components are presented. A discussion on the measurement of surface topography, geometrical form, and subsurface damage of diamond machined surfaces is also included.

A Real-Time C3 Continuous Tool Path Smoothing and Interpolation Algorithm for Five-Axis Machine Tools

Abstract: Local corner smoothing method is commonly adopted to smooth linear (G01) tool path segments in computer numerical control (CNC) machining to realize continuous motion at transition corners. However, because of the highly non-linear relation between the arc-length and the spline parameter, and the challenge to synchronize the tool tip position and tool orientation, real-time and high-order continuous five-axis tool path smoothing and interpolation algorithms have not been well studied. This paper proposes a real-time C3 continuous corner smoothing and interpolation algorithm for five-axis machine tools. The transition corners of the tool tip position and tool orientation are analytically smoothed in the workpiece coordinate system (WCS) and the machine coordinate system (MCS) by C3 continuous PH splines, respectively. The maximum deviation errors of the smoothed tool tip position and the tool orientation are both constrained in the WCS. An analytical synchronization algorithm is developed to guarantee the motion variance of the smoothed tool orientation related to the tool tip displacement is also C3 continuous. The corresponding real-time interpolation method is developed with a continuous and peak-constrained jerk. Simulation results verify that the maximum deviation errors caused by the tool path smoothing algorithm are constrained, and continuous acceleration and jerk of each axis are achieved along the entire tool path. Comparisons demonstrate that the proposed algorithms achieve lower amplitude and variance of acceleration and jerk when compared with existing methods. Experiments show that the proposed five-axis corner smoothing and interpolation algorithms are serially executed in real-time with 0.5-ms cycle.

On the Cutting of Metals: A Mechanics Viewpoint

Abstract: The mechanics of large-strain deformation in cutting of metals is discussed, primarily from viewpoint of recent developments in in situ analysis of plastic flow and microstructure characterization. It is shown that a broad range of deformation parameters can be accessed in chip formation—strains of 1–10, strain rates of 10–105/s, and temperatures up to 0.7Tm—and controlled. This range is far wider than achievable by any other single-stage, severe plastic deformation (SPD) process. The resulting extreme deformation conditions produce a rich variety of microstructures in the chip. Four principal types of chip formation—continuous, shear-localized, segmented, and mushroom-type—as elucidated first by Nakayama (1974, “The Formation of ‘Saw-Toothed Chip’ in Metal Cutting,” Proceedings of International Conference on Production Engineering, Tokyo, pp. 572–577) are utilized to emphasize the diverse plastic flow phenomena, especially unsteady deformation modes that prevail in cutting. These chip types are intimately connected with the underlying flow, each arising from a distinct mode and triggered by an instability phenomenon. The role of plastic flow instabilities such as shear banding, buckling, and fracture in mediating unsteady flow modes is expounded, along with consequences of the flow modes and chip types for the cutting. Sinuous flow is shown to be the reason why gummy (highly strain-hardening) metals, although relatively soft, are so difficult to cut. Synthesizing the various observations, a hypothesis is put forth that it is the stability of flow modes that determines the mechanics of cutting. This leads to a flow-stability phase diagram that could provide a framework for predicting chip types and process attributes.

Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

Abstract: When using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures.

Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion

Abstract: The powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.

Simulating Thermoelectric Effect and Its Impact on Asymmetric Weld Nugget Growth in Aluminum Resistance Spot Welding

Abstract: Resistance spot welding (RSW) of aluminum–aluminum (Al–Al) is known to be very challenging, with the asymmetric growth of the weld nugget often observed. In this article, a semicoupled electrical–thermal–mechanical finite element analysis (FEA) procedure was established to simulate the RSW of two layers of AA6022-T4 sheets using a specially designed Multi-Ring Domed (MRD) electrodes. Critical to the modeling procedure was the thermoelectric (including the Peltier, Thomson, and Seebeck effects) analyses to simulate the asymmetric nugget growth in the welding stage. Key input parameters such as the Seebeck coefficients and high-temperature flow stress curves were measured. Simulation results, experimentally validated, indicated that the newly developed procedure could successfully predict the asymmetric weld nugget growth. Simulation results also showed the Seebeck effect in the holding stage. The simulations represent the first quantitative investigation of the impact of the thermoelectric effects on resistance spot welding.

Thermal Modeling in Metal Additive Manufacturing Using Graph Theory: Experimental Validation With Laser Powder Bed Fusion Using In Situ Infrared Thermography Data

Abstract: The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history of additively manufactured parts. The graph theory approach for thermal modeling in additive manufacturing (AM) was recently published in these transactions. In the present paper, the graph theory approach is validated with in situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize the foregoing objective through the following four tasks. First, two kinds of test shapes, namely, a cylinder and cone, are made in two separate builds on a production-type LPBF machine (Renishaw AM250); the material used for these tests is stainless steel (SAE 316L). The intent of both builds is to influence the thermal history of the part by controlling the cooling time between the melting of successive layers, called the interlayer cooling time (ILCT). Second, layer-wise thermal images of the top surface of the part are acquired using an in situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds is used to validate the graph theory-predicted surface temperature trends. Fourth, the surface temperature trends predicted using graph theory are compared with results from finite element (FE) analysis. The results substantiate the computational advantages of the graph theory approach over finite element analysis. As an example, for the cylinder-shaped test part, the graph theory approach predicts the surface temperature trends to within 10% mean absolute percentage error (MAPE) and approximately 16 K root mean squared error (RMSE) relative to the surface temperature trends measured by the thermal camera. Furthermore, the graph theory-based temperature predictions are made in less than 65 min, which is substantially faster than the actual build time of 171 min. In comparison, for an identical level of resolution and prediction error, the finite element approach requires 175 min.

Micro/Meso-Scale Mechanical Machining 2020: A Two-Decade State-of-the-Field Review

Abstract: Micro/meso-scale mechanical machining (M4) processes are miniaturized versions of conventional machining processes such as milling, drilling, and turning, where material removal is accomplished by physical contact between the micro/meso-scale cutting tool and the workpiece. The objective of this review paper is to provide an assessment of the state-of-the-field related to M4 processes during the last two decades. Key systems-level issues related to the deployment of M4 processes including micro/meso-scale machine tool (mMT) design, sensing/calibration, cutting tools, and lubrication strategies are discussed. Emerging material systems are identified along with the specific challenges posed for the development of microstructure-based process models. The topic of micro/meso-scale machining dynamics is reviewed both in terms of recent research findings as well as unresolved challenges posed by the complexity of experimental characterization and modeling. Finally, key industry trends are discussed along with promising interdisciplinary drivers that are expected to influence this field in the upcoming decade.

Tool Path Optimization for Robotic Surface Machining by Using Sampling-Based Motion Planning Algorithms

Abstract: This paper develops a tool path optimization method for robot surface machining by sampling-based motion planning algorithms. In the surface machining process, the tool-tip position needs to strictly follow the tool path curve and the posture of the tool axis should be limited in a certain range. But the industrial robot has at least six degrees of freedom (Dof) and has redundant Dofs for surface machining. Therefore, the tool motion of surface machining can be optimized using the redundant Dofs considering the tool path constraints and limits of the tool axis orientation. Due to the complexity of the problem, the sampling-based motion planning method has been chosen to find the solution, which randomly explores the configuration space of the robot and generates a discrete path of valid robot state. During the solving process, the joint space of the robot is chosen as the configuration space of the problem and the constraints for the tool-tip following requirements are in the operation space. Combined with general collision checking, the limited region of the tool axis vector is used to verify the state's validity of the configuration space. In the optimization process, the sum of path length of each joint of the robot is set as the optimization objective. The algorithm is developed based on the open motion planning library (OMPL) which contains the state-of-the-art sampling-based motion planners. Finally, two examples are used to demonstrate the effectiveness and optimality of the method.

Modeling Dynamic Stability and Cutting Forces in Micro Milling of Ti6Al4V Using Intermittent Oblique Cutting Finite Element Method Simulation-Based Force Coefficients

Abstract: Tool breakage is a significant issue in micro milling owing to the less stiffness of the micro tool. To cope up with such limitation, precise predictions of dynamic stability, and cutting force have the utmost importance to monitor and optimize the process. In this article, dynamic stability and cutting force are predicted precisely for micro milling of Ti6Al4V by obtaining force coefficients from a novel 3D intermittent oblique cutting finite element method (FEM) simulation considering the influence of tool run out. First, the stability model is modified by incorporating the appropriate values of limiting angles obtained analytically accounting the trajectories of the flutes due to tool run out. This stability model is utilized to select chatter-free parametric combinations for micro milling tests. Next, an improved cutting force model is developed by incorporating the force coefficients obtained from oblique cutting simulation in the mechanistic model and differentiating the whole machining region into three distinct region considering size effect. The force model also considers the effect of increased edge radius of the worn tool, run out, elastic recovery, ploughing, minimum undeformed chip thickness (MUCT), and limiting angles, cumulatively. The proposed dynamic stability and cutting force models based on the oblique cutting simulation show their adequacy by predicting the stability limit and cutting force more precisely, respectively, as compared to those obtained by orthogonal cutting simulation. Besides, the proposed force model for the worn tool is found to be viable as it is closer to the experimental forces, whereas force model without the incorporation of tool wear underestimated the experimental forces.