bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Proposal of Two Different-Sized Abrasives to Shorten Polishing-Process-Time and Reduce Manufacturing Cost
Authors: Tokoroyama Takayuki , Takeno Hiroaki , Umehara Noritsugu , Murashima Motoyuki , Chiba Shogo 
Published: January 2021

Abstract
Polishing manufactures use abrasives such as diamond or other hard materials to flatten several materials. Generally, diamond abrasive is most popular and it is costly. Although, diamond abrasives are costly, manufacturer have to use it to reduce surface roughness such as high hardness ceramics and others. In the polishing process, it is unpopular to retrieve those abrasives from reservoir of after-using them. Therefore, using diamond abrasives account for majority of processing cost. One of the concept to overcome the cost problem, it is suggested that reducing high-cost diamond abrasives and replacing cheap and soft abrasives can reduce processing-cost. In this study, to reveal the possibility of such two different-sized abrasives polishing process, polishing tests were conducted to unearth an effect of two-hardness-abrasive polishing technique to a glass disk on reducing surface roughness. An average diameter of 127 μm GC, and 1.0 μm diamond abrasive were prepared as concept abrasives. As a result, the arithmetic mean roughness of the glass quickly decreased in the case of using 127 μm GC abrasive with 1.0 μm diamond abrasive compared to only GC or only diamond abrasives.
Sensor-Less Tool Wear Estimation Based on Change in Value of Identified Parameter in Cutting Force Prediction Model
Authors: Kaneko Kazuki , Nishida Isamu , Sato Ryuta , Shirase Keiichi 
Published: January 2021

Abstract
Several methods have been proposed to detect tool wear in milling operation using AE (Acoustic Emission) signals or cutting force signals. However, these methods require additional sensors such as an AE sensor or a dynamometer, which incurs additional costs. For this reason, a simple tool life estimation method based on machining time is used. In this study, a sensor-less tool wear estimation method is proposed. In this method, the parameters required for the cutting force prediction are identified continuously from the spindle motor torque signal, which can be monitored within the computer numerical controlled (CNC) machine. The tool wear progress can be estimated by the continuous change in the identified parameters during milling operation. To identify the parameters continuously, a real-time virtual milling simulation is performed in parallel with a physical milling operation. In the experimental results, it was confirmed that the identified parameter corresponding to the edge force component has linear relationship with the flank wear width of cutting edge. Thus the flank wear can be estimated without any additional sensor.
Microstructure Affected Residual Stress in Direct Metal Deposition
Authors: Mirkoohi Elham , Liang Steven Y. 
Published: January 2021

Abstract
Residual stress build-up in metal additive manufacturing (AM) is a well-known problem that can impede the applicability of the AM parts. Residual stress may cause the part to fail due to the crack or fall out of the specified dimension. Thus, in order for a part to be used in a mission-critical application, it is important to predict the stress state within the AM part accurately and rapidly. During the thermal loading, the grain size is altered at the subsurface through dynamic recrystallization (DRx) and subsequent recovery. The yield strength of the alloys is largely determined by the size of nucleated grains, and it has a substantial influence on residual stress build-up. In this work, a physics-based analytical model is proposed to predict the residual stress considering the microstructure of the additively manufactured part. A moving heat source approach is used to predict the temperature field. Due to the high-temperature gradient in this process, the material properties are considered temperature-sensitive to capture the properties gradient affected thermal distribution. The energy needed for solid-state phase change is also considered by modifying the heat capacity using the latent heat of fusion. Due to the high-temperature gradient, the thermal stress is obtained using Green’s function of stresses due to the point body load. The total stress is the combination of three main sources of stress known as body forces, normal tention, and hydrostatic stress. High thermal stress may exceed the yield strength. The yield surface is obtained by modifying the Johnson-Cook flow stress to incorporate the effect of DRx on grain size using the Hall-Patch equation. The DRx and subsequent recovery affected grain size is predicted using Johnson-Mehl-Avrami-Kolmogorov (JMAK), model. The residual stress is then predicted using incremental plasticity and kinematic hardening behavior of the metal according to the property of volume invariance in plastic deformation and in coupling with equilibrium and compatibility conditions. The predicted residual stress considering microstructure evolution is validated by measuring the residual stress via X-ray diffraction for the In718 parts manufactured via the direct metal deposition process.
Quickest Change Point Detection in Shape Inspection of Additively Manufactured Parts Under a Multi-Resolution Framework
Authors: Jin Yu , Liao Haitao , Pierson Harry 
Published: January 2021

Abstract
In-situ layer-by-layer inspection is essential to achieving the full capability and advantages of additive manufacturing in producing complex geometries. The shape of each inspected layer can be described by a 2D point cloud obtained by slicing a thin layer of 3D point cloud acquired from 3D scanning. In practice, a scanned shape must be aligned with the corresponding base-truth CAD model before evaluating its geometric accuracy. Indeed, the observed geometric error is attributed to systematic, random, and alignment errors, where the systematic error is the one that triggers an alarm of system anomalies. In this work, a quickest change detection (QCD) algorithm is applied under a multi-resolution alignment and inspection framework 1) to differentiate errors from different error sources, and 2) to identify the layer where the earliest systematic deviation distribution changes during the printing process. Numerical experiments and a case study on a human heart are conducted to illustrate the performance of the proposed method in detecting layer-wise geometric error.
Feasibility and Characterization of Large-Scale Additive Manufacturing With Long Fiber Reinforced Composites
Authors: Thakur Aditya R. , Leu Ming C. , Dong Xiangyang 
Published: January 2021

Abstract
A new additive manufacturing (AM) approach to fabricate long fiber reinforced composites (LFRC) was proposed in this study. A high deposition rate was achieved by the implementation of a single-screw extruder, which directly used thermoplastic pellets and continuous fiber tows as feedstock materials. Thus, the proposed method was also used as a large-scale additive manufacturing (LSAM) method for printing large-volume components. Using polylactic acid (PLA) pellets and continuous carbon fiber tows, the feasibility of the proposed AM method was investigated through printing LFRC samples and further demonstrated by fabricating large-volume components with complex geometries. The printed LFRC samples were compared with pure thermoplastic and continuous fiber reinforced composite (CFRC) counterparts via mechanical tests and microstructural analyses. With comparable flexural modulus, the flexural strength of the LFRC samples was slightly lower than that of the CFRC samples. An average improvement of 28% in flexural strength and 50% in flexural modulus were achieved compared to those of pure PLA parts, respectively. Discontinuous long carbon fibers, with an average fiber length of 20.1 mm, were successfully incorporated into the printed LFRC samples. The carbon fiber orientation, distribution of carbon fiber length, and dispersion of carbon fiber as well as porosity were further studied. The carbon fibers were highly oriented along the printing direction with a relatively uniformly distributed fiber reinforcement across the LFRC cross section. With high deposition rate (up to 0.8 kg/hr) and low material costs (&lt; $10/kg), this study demonstrated the potentials of the proposed printing method in LSAM of high strength polymer composites reinforced with long carbon fibers.
Smart Agent-Based Priority Dispatching Rules for Job Shop Scheduling in a Furniture Manufacturing Workshop
Authors: Wang Hao , Peng Tao , Tang Renzhong , Qin Wei 
Published: January 2021

Abstract
To meet the increasingly diversified demand, multi-variety and small-batch production has been widely acknowledged in manufacturing industry, which raises higher requirements for production scheduling. Priority dispatching rules have been widely used in job shop scheduling with many advantages, including low computational cost, high flexibility and good portability. However, it is usually hard to guarantee a globally optimal solution. Multi-Agent-based priority dispatching rules for scheduling are available to handle real complex and dynamic production environment, in the meanwhile hold a comprehensively satisfactory performance. In this paper, multi-agents are designed representing production jobs, production machines, and especially packaging process. A composite priority dispatching rule considering working conditions of job agent, machine agent and packaging agent is proposed, where the packaging agent plays a significant role in developing the proposed dispatching rule. Then, this priority rule is compared with four widely-applied rules in literature, FCFS, SPT, COVERT, and ATC. Four measures of performance, i.e. maximum completion time, tardiness time, equipment utilization, order waiting time, are employed. The results show that the proposed priority dispatching rule performs better overall, particularly in reducing order waiting time. Finally, an agent-based simulation model of a furniture manufacturing workshop is constructed using AnyLogic, to verify the proposed priority rule in dealing with random order arrivals.
Modeling and Simulation of the Phase Coarsening Process for Cocontinuous Polymer Blends
Authors: Shen Hangming , Yao Donggang 
Published: January 2021

Abstract
Among different phase structures in immiscible polymer blends, the cocontinuous phase structure is considered to be advantageous for load transfer and achieving good mechanical properties. Due to the presence of an interpenetrating interface, phase coarsening naturally occurs during melt processing of cocontinuous polymer blends, and harness of the coarsening kinetics is important for structural control. Existing models for phase coarsening are mostly founded on the basis of scaling or dimensional analysis while computational models embodying more realistic phase geometries are demanded. In this paper, we present a two-step computational approach for prediction of the coarsening kinetics. First, a phase-field transport equation is solved to establish an initial phase geometry. Second, a moving-boundary flow model is implemented to solve the hydrodynamic problem. Case studies are presented both in 2D and in 3D domains. An empirical model on the basis of fractional calculus is also proposed to fit the computational results. Once verified by experimental data, this approach can provide an integrated tool for assisting in the processing of cocontinuous polymer blends where phase coarsening is of concern.
Inter-Cell Scheduling Optimization With Limited Transportation Capacities for Optimization of Vehicle Numbers
Authors: Liu Qiong , Ye Qin , Mei Xu 
Published: January 2021

Abstract
Inter-cell manufacturing could quickly respond to market changes and save investments on equipment. The number of vehicles in an inter-cell manufacturing system is one of important factors affecting results of inter-cell scheduling and related costs. Previous literatures on inter-cell scheduling with limited transportation capacities assumed that numbers of vehicles in a manufacturing system are fixed and simply set as numbers of manufacturing cells. However, numbers of vehicles generally are determined by decision makers and might be different in different manufacturing systems. Reducing the number of vehicles could save investments and latter operation costs. To help decision makers make decisions on the number of vehicles, it is needed to explore relationships among the number of vehicles, makespan, and total costs. An inter-cell scheduling model is proposed for an inter-cell manufacturing system with flexible routes and limited transportation capacities to optimize the number of vehicles, makespan and total costs. A Shuffled Frog Leaping Algorithm (SFLA) is designed to solve the proposed model. A four-segment coding method is proposed to encode operation sequence of parts, manufacturing cells, and machines for processing operations, and vehicle allocation. A case is used to analyze relationships among the number of vehicles, makespan, and total costs. Conclusions are yielded, which would help decision makers to make decisions on the number of vehicles.
Digital Twin Enabled Mass Personalization: A Case Study of a Smart Wetland Maintenance System
Authors: Aheleroff Shohin , Zhong Ray Y. , Xu Xun , Feng Zhenan , Goyal Pulkit 
Published: January 2021

Abstract
Mass personalization has been an essential paradigm in the Industry 4.0 era. Mass personalization has long been a question of great interest in a wide range of industries. There is a recognized need for a cost-effective approach for dealing with a high number of unique products and services. There has been substantial research undertaken on the role of Industry 4.0 for mass customization. The contribution of Digital Twin (DT) has received little attention within mass personalization. No known empirical research has focused on exploring relationships between DT and mass personalization. The principal objective of this project was to utilize DT for a significant number of unique wetlands. In this study, a Digital Twin architecture was developed and applied to address challenges in regards to a massive number of distinct wetlands. This study is based on an industry-led case with significant value, including scheduled maintenance, real-time monitoring, remote controlling, and predicting functionalities. The findings indicated that there was a positive relationship between Digital Twin capabilities and mass personalization. The present results highlight the detrimental value that Digital Twin has on mass personalization. The results represent a further step towards developing Digital Twin research and development.
Decomposition and Sequencing for a 5-Axis Hybrid Manufacturing Process
Authors: Xiao Xinyi , Joshi Sanjay 
Published: January 2021

Abstract
Hybrid Manufacturing (HM) combining Additive Manufacturing (AM) and subtractive machining (SM) technologies have recently been introduced and have the potential to address the shortcomings of AM, such as the poor surface finish and requires post-processing of the support structures. One such example of an HM machine is the DMG Mori Lasertec 65. These 5-axis HM machines allow for rapid deposition of material during additive manufacturing and address the issues of feature resolution, surface finish, and tolerances by subtractive machining. Additionally, these processes allow for the creation of complex geometries not possible with standard 5-axis machining. Process planning for HM is a reasonably complex manual task and could benefit from automation. Critical steps in process planning are the decomposition of the part into additive and subtractive features, sequencing all features and assigning the tool-paths for these features. This paper presents algorithms for decomposing the part and sequencing the additive and subtractive features in an automated manner, paving the way for a fully automated system for HM. Examples of a wide range of parts demonstrating the capability of the algorithm are presented.
Design of Stochastic Lattice Structures for Additive Manufacturing
Authors: McConaha Matthew , Anand Sam 
Published: January 2021

Abstract
With recent development of additive manufacturing methods, topology optimization, an increased focus on the generation of designs which maximize material efficiency by lightweighting has gained considerable interest. Lattice structures are one of the popular methods chosen by design engineers for constructing highly complex, functional geometries which are only manufacturable by additive processes. Stochastic lattices have been finding their way into additively manufactured geometries due to their strength at low volume fraction, as well as the ease of implementation with various generative design tools on the market. However, optimization of these stochastic lattices for maximizing part strength and stiffness is a research topic that has been largely overlooked. By tweaking stochastic lattice generation procedures, non-isotropic structures can be generated and these directional strength properties can be exploited. This paper describes a method for homogenizing the effective properties of non-isotropic stochastic lattices generated using stretched Voronoi tessellations, optimization of the stretching aspect ratio and angle within a part design space, and generation of the non-isotropic and smoothly graded Voronoi-based stochastic lattice structures for that design space. The method was applied to a case study of a cantilever beam with nine different Voronoi lattice configurations. Stiffness of parts designed using this procedure was found to be significantly higher than parts designed using an isotropic design.
Deformation Behavior of Grains Near Defects in Direct Metal Laser Sintered Inconel 718 During Indentation
Authors: Rifat Mustafa , Basu Saurabh 
Published: January 2021

Abstract
The present work utilizes Orientation Imaging Microscopy and Finite Element Modelling to analyse microstructure evolution in grains near defects during plane strain indentation of direct metal laser sintered Inconel 718. Defects are inevitably produced during printing of metals and they degrade the mechanical behaviour of parent components. Understanding microstructure evolution of grains present near defects can help create better predictive models of mechanical behaviour of components resulting from additive manufacturing. In this work, an ex-situ study of microstructure evolution during plane strain indentation of DMLS Inconel 718 specimens is performed. Regions that lie near volumetric porosity defects were studied. Grain Orientation Spread was utilized as a metric to quantify intra-granular deformation. It was seen that microstructure evolution of grains near defects is enhanced due to strain concentrations whereby they exhibit larger orientation spread after plastic deformation. Finite Element Analysis was used to simulate the plane strain indentation test on the specimen in which, porosity defects and roughness textures similar to those seen in the as-received specimen were programmed using the python scripting interface of Abaqus. Results from finite element analysis were compared with insights from microstructure analysis to describe evolution of microstructure during deformation near defects.
Verification of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation
Authors: Weiss Brian A. , Kaplan Jared 
Published: January 2021

Abstract
Manufacturing processes have become increasingly sophisticated leading to greater usage of robotics. Sustaining successful manufacturing robotic operations requires a strategic maintenance program. Maintenance can be very costly, especially when some manufacturers unnecessarily spend resources (i.e., time, money) to maintain their equipment. To reduce maintenance costs, manufacturers are exploring how they can assess the health of their robot workcell operations to enhance their maintenance strategies. Effective health assessment relies upon capturing appropriate data and generating intelligence from the workcell. Multiple data streams relevant to a robot workcell may be available including robot controller data, a supervisory programmable logic controller data, maintenance logs, process/part quality data, and equipment/process fault and/or failure data. This data can be extremely informative, yet the extreme volume and complexity of this data can be both overwhelming, confusing, and paralyzing. Researchers at the National Institute of Standards and Technology have developed a test method and companion sensor to assess the health of robot workcells, which will yield an additional and unique data stream. The intent is that this data stream can either serve as a surrogate for larger data volumes to reduce the data collection and analysis burden on the manufacturer or add more intelligence to assessing robot workcell health. This article will present the immediate effort focused on verifying the companion sensor. Results of the verification test process are discussed along with preliminary results of the sensor’s performance during verification testing.
Applications of Machining in Materials Manufacturing
Authors: Mann J. B. , Saei M. , Udupa A. , Puentes-Rodriguez B. Stiven , Sagapuram D. 
Published: January 2021

Abstract
The deformation conditions in machining of metals and alloys offer a unique route for materials processing with remarkable advantages over conventional deformation processes. The intense shear strain and high strain-rates in machining can be applied to form chips with controlled geometry. That is, the chip formation in machining can be used directly as a materials processing route wherein the chip becomes the product. The technical details for two of these processes — hybrid cutting-extrusion (HCE) and modulation-assisted machining (MAM) — are discussed and recent experimental results are presented. Both processes involve direct control of the shear-based deformation in machining. HCE applies an additional constraint in cutting which converts the otherwise uncontrolled chip thickness to a controlled format of specific size and shape. In HCE processing of sheet and strip, the deformed chip thickness is less than the deformed chip thickness in conventional cutting. The superimposed oscillation in MAM converts the otherwise continuous cutting process into a series of discrete cutting events. The control of the MAM and cutting conditions enable unique control of chip formation and the production of equiaxed, fiber, and platelet powder (particle) morphologies. The HCE and MAM processes demonstrate how chip control in machining can provide a route to applications opportunities in materials manufacturing.