Journal of Manufacturing Science and Engineering

Abstract Chatter is one of the major issues that cause undesirable effects limiting machining productivity. Passive control devices, such as tuned mass dampers (TMDs), have been widely employed to increase machining stability by suppressing chatter. More recently, inerter-based devices have been developed for a wide variety of engineering vibration mitigation applications. However, no experimental study for the application of inerters to the machining stability problem has yet been conducted. This article presents an implementation of an inerter-based dynamic vibration absorber (IDVA) to the problem of chatter stability, for the first time. For this, it employs the IDVA with a pivoted-bar inerter developed in the study by Dogan et al. (2022, “Design, Testing and Analysis of a Pivoted-Bar Inerter Device Used as a Vibration Absorber, Mechanical Systems and Signal Processing,” 171, p. 108893) to mitigate the chatter effect under cutting forces in milling. Due to the nature of machining stability, the optimal design parameters for the IDVA are numerically obtained by considering the real part of the frequency response function (FRF), which enables the absolute stability limit in a single degree-of-freedom (SDOF) to be maximized for a milling operation. Chatter performance is experimentally validated through milling trials using the prototype IDVA and a flexible workpiece. The experimental results show that the IDVA provides more than 15% improvement in the absolute stability limit compared to a classical TMD.

Hakan Dogan

Neil D. Sims

David J. Wagg

Implementation of Inerter-Based Dynamic Vibration Absorber for Chatter Suppression

基于惯容器的动态振动吸收器在颤振抑制中的应用

Abstract Friction surfacing is a new variation of friction stir processing for surface property modification of metallic substrates. There is an increasing body of literature about friction surfacing by deposition of metal from a consumable tool to a solid substrate. Friction surfacing has many potential applications in joining, coating for corrosion resistance, and repair of degraded components. This article presents a review of the basic principles and latest research organized by processing techniques and variations, thermomechanical transfer and deposition of material, and finally metallurgical, mechanical, and chemical properties of the resulting deposition. Different friction surfacing processes are reviewed of novel tool–substrate configurations for material deposition for noncoating purposes like keyhole filling and joining dissimilar materials. Possible future topics of study for this area are discussed, which include deeper understanding of material transfer through metallurgy, FEM, and scale up of the technique for practical application.

Ebrahim Seidi

Scott F. Miller

Blair E. Carlson

Friction Surfacing Deposition by Consumable Tools

可耗工具摩擦熔敷沉积

There are many scientific and engineering applications of transparent glass including optics, communications, electronics, and hermetic seals. However, there has been minimal research toward the additive manufacturing (AM) of transparent glass parts. This paper describes and demonstrates a filament-fed technique for AM of transparent glass. A transparent glass filament is melted by a CO2 laser and solidifies as the workpiece is translated relative to the stationary laser beam. To prevent thermal shock, the workpiece rests on a heated build platform. In order to obtain optically transparent parts, several challenges must be overcome, notably producing index homogeneity and avoiding bubble formation. The effects of key process parameters on the morphology and transparency of the printed glass are explored experimentally. These results are compared to a low-order model relating the process parameters to the temperature of the molten region, which is critical to the quality of the deposited glass. At lower temperatures, the glass is not fully melted, resulting in index variations in the final part, while at higher temperatures, phase separation introduces bubbles and other defects into the part. The correct process avoids these issues and deposits optically transparent glass.

Junjie Luo

Luke J. Gilbert

Chuang Qu

Robert G. Landers

Douglas A. Bristow

Edward C. Kinzel

Additive Manufacturing of Transparent Soda-Lime Glass Using a Filament-Fed Process

使用丝材进给工艺的透明钠钙玻璃增材制造

Abstract The integration of mobile robots in material handling in flexible manufacturing systems (FMS) is made possible by the recent advancements in Industry 4.0 and industrial artificial intelligence. However, effectively scheduling these robots in real-time remains a challenge due to the constantly changing, complex, and uncertain nature of the shop floor environment. Therefore, this paper studies the robot scheduling problem for a multiproduct FMS using a mobile robot for loading/unloading parts among machines and buffers. The problem is formulated as a Markov Decision Process, and the Q-learning algorithm is used to find an optimal policy for the robot's movements in handling different product types. The performance of the system is evaluated using a reward function based on permanent production loss and the cost of demand dissatisfaction. The proposed approach is validated through a numerical case study that compares the proposed policy to a similar policy with different reward function and the first-come-first-served policy, showing a significant improvement in production throughput of approximately 23%.

Muhammad Waseem

Qing Chang

Adaptive Mobile Robot Scheduling in Multiproduct Flexible Manufacturing Systems Using Reinforcement Learning

使用强化学习的多种产品柔性制造系统中的自适应移动机器人调度

Abstract Computational modeling and simulation are employed to study a rotating susceptor vertical impinging chemical vapor deposition (CVD) reactor to predict GaN film deposition. Many metal-organic chemical vapor deposition reactor manufacturers use prior experience to design and fabricate CVD reactors without a fundamental basis for the process and information on the optimal conditions for the deposition. Through trial and error, they fine tune the gas flow parameters, heater temperatures, chamber pressure, and concentration of species gases for optimal growth. However, expensive raw precursor gas and time are wasted through this method. A computational model is an important step in the CVD reactor design and GaN growth prediction. It can be used to model and optimize the reactor to yield favorable operating conditions. In this paper, a simple geometry consisting of a rotating susceptor and flow guide is considered. The focus is on gallium nitride (GaN) thin films. The study shows how the computational model can benefit reactor design. It also presents comparisons between model prediction results and experimental data from a physical, practical, system. Commercially available software is used, with appropriate modifications, and the results obtained are discussed in detail.

Sun Wong

Yogesh Jaluria

A Numerical and Experimental Study on the Fabrication GaN Films by Chemical Vapor Deposition

Abstract This work examines the closed-loop deployment of spacecraft servicing modules onto an uncontrolled bundle of assembly components as a method of gaining custody during the in-space assembly process. Minimum energy, low-thrust relative trajectories are generated using indirect optimal control on linear dynamics over a finite time search horizon. A line search solves for the minimum energy solution in the closed subset that satisfies a keep out zone constraint. A trajectory tracking linear quadratic regulator is used in conjunction with an extended Kalman filter to closed-loop maneuver the spacecraft under nonlinear relative orbital motion dynamics. Bearing angles, range, and range rate measurements are assumed available. Launch Delta V's from the servicer spacecraft's deployment of a module and the Delta V's seen in the relative frame upon impact on the client are included, and their effect on covariance due to impact site uncertainty is investigated. Discussions regarding these GNC elements' importance within the context of the manufacturing/assembly process are included.

Ian Down

David van Wijk

Deep Parikh

Manoranjan Majji

Autonomous Satellite Servicing Infrastructure for In-Space Assembly and Manufacturing

自主卫星维护基础设施用于太空组装与制造

Abstract Manufacturers should repurpose their supply chain quickly in response to a new demand, which reflects a surge in market needs over a certain period of time. The ability to meet this demand depends on the reconfigurability of manufacturing supply chains across products; however, many industries do not have the quantitative tools for evaluating the reconfigurability. Repurposing manufacturing supply chains across industries (e.g., manufacturers from the auto and health industries) poses a unique challenge since existing suppliers should be reutilized to meet the new demand. This paper establishes a methodology that addresses the supply chain repurposing problem by identifying a new supply chain composed of pieces of existing ones to meet the new demand where cross-product reconfiguration is required. First, the supernetwork framework is used to represent the bi-level information in the manufacturing supply chain, including assembly task planning and manufacturer-supplier assignment. Next, a supernetwork similarity metric is proposed to assess the reconfigurability of existing supply chains across industries when detailed reconfiguration cost data is unavailable. Guided by the similarity metric, a nonlinear integer programming model is developed for joint decision-making in selecting assembly plans and supply chains to create a product in demand, leveraging existing supply chains of other products. The problem is solved by an evolutionary algorithm customized for the supply chain repurposing problem studied herein. The paper concludes with a case study demonstrating how five manufacturing supply chains that produce different products can be repurposed to generate an intubation ventilator system.

Arriana Nwodu

Zhengqian Jiang

Maxim Dulebenets

Weihong Guo

Hui Wang

Repurposing Supply Chains and Process Planning Across Products Using a Similarity Model Based on Supernetworks

基于超网络相似模型的跨产品供应链再利用与工艺规划

Abstract Thermal insulation materials reduce heat transfer and are typically made from materials like fiberglass, foam, or mineral wool, which are engineered to trap air and hinder heat conduction and convection. The traditional manufacturing processes of thermal insulation materials are often energy-intensive and result in significant greenhouse gas emissions. In the current global drive for sustainability, these energy-intensive manufacturing processes raise environmental concerns and need to be addressed. In this work, with the objective of addressing both material sustainability and manufacturing sustainability, we present an additive manufacturing strategy to fabricate biomass materials for thermal insulation applications. Firstly, we propose to use biomass materials, such as wheat straw, as the primary feedstock materials for manufacturing. Such biomass materials offer the unique capacity to sequester carbon dioxide during their growth, and when incorporated into thermal insulation structures, they effectively capture and store carbon inside the structure. Concurrently, our pursuit of manufacturing process sustainability is driven by using a cost-effective additive manufacturing technology to fabricate durable thermal insulation structures. In the presented work, we first demonstrate the formulation of a 3D-printable ink using chopped straw fibers. We conduct comprehensive rheological characterizations to reveal the shear-thinning properties and the printability of the straw fiber ink. Utilizing the direct ink writing (DIW) process, the straw fiber material is deposited into 3D structures. Following bulk material characterization tests, including microstructure, mechanical, and thermal tests. We unveil the low thermal conductivity and robust mechanical properties.

Zipeng Guo

Licheng Liang

Jason Armstrong

Shenqiang Ren

Chi Zhou

ADDITIVE MANUFACTURING OF WHEAT STRAW FIBERS FOR SUSTAINABLE THERMAL INSULATION APPLICATION

小麦秸秆纤维增材制造及其在可持续隔热应用中的研究

Abstract Advanced manufacturing processes are often based on complex multiphysics phenomena that are either poorly understood or are computationally too expensive to simulate in the context of process design, control or planning. Traditionally, simplified physics models with prescribed heuristics or purely data-driven surrogate models are used as alternatives in such applications. The concept of physics-informed machine learning (PIML) has been shown to have unique advantages over both of these alternatives in various fields of complex system analysis. In this paper, a new physics-informed machine learning (PIML) approach is presented to model the geometry of the cut produced by a Magnetically-assisted Laser Induced Plasma Micro Machining (M-LIPMM) process. This PIML architecture uses a neural network to auto-adapt the parametric boundary condition and physical properties used in a simplified finite difference based physics model (of 2D heat conduction), as a function of the inputs namely the laser settings. This network also estimates the scaling and shifting parameters used by a convolutional neural network that takes the temperature profile predicted by the simplified heat conduction model to predict the width and depth of the machined cut. Trained on physical experiment data, the PIML approach compares favorably to a pure data-driven neural network model in extrapolation tests, while also providing physical insights (that the latter cannot). The PIML approach also provides an 85% better accuracy overall compared to the simplified physics model with heuristic settings.

Manaswin Oddiraju

Jeremy Cleeman

Rajiv Malhotra

Souma Chowdhury

A Differentiable Physics-Informed Machine Learning Approach to Model Laser-based Micro-Manufacturing Process

一种可微分的物理信息机器学习方法用于建模基于激光的微制造过程

Abstract Chatter vibrations in machining degrade the surface quality, cause premature tool and machine failures, and reduce the productivity. The dynamic interference between the cutting tool and the wavy part surface damps the machining process in the presence of vibrations. Machining process damping improves the chatter stability especially for difficult-to-cut materials and is even more pronounced via optimized cutting edge geometries. However, there is not any analytical model that can consider arbitrary edge profiles in modeling the process damping. This study introduces a new generalized analytical model to predict the process damping forces for any two-dimensional cutting edge geometries by taking the vibration parameters, work material properties, cutting conditions and cutting edge geometry into account. That is achieved by discretizing the tool-workpiece contact using a series of springs with a non-linear Winkler foundation and by employing a material constitutive model to describe the behavior of the deformed springs beyond elasticity. The process damping force is calculated from the contact pressure between the edge and the work material and linearized with an equivalent viscous damper dissipating the same energy. The proposed model has been verified experimentally and numerically for different tool geometries. It is demonstrated that the model can eliminate the time-intensive experimental and numerical identification of process damping coefficients and can digitalize the design phase of cutting tools by rapidly evaluating their machining dynamics performance in place of physical tests.

Jonathan Theraroz

Oguzhan Tuysuz

A Generalized Machining Process Damping Model for Orthogonal Cutting

正交切削的广义加工过程阻尼模型

Abbreviation	J. Manuf. Sci. Eng.
Journal Impact	2.44
Quartiles(Global)	ENGINEERING, MECHANICAL(Q2)

ISSN	1087-1357, 1528-8935
h-index	113

Publication Information	Publisher: American Society of Mechanical Engineers (ASME)，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1960，Proportion of original research papers: 96.84%，Self Citation Rate:4.20%， Gold OA Rate: 2.34%
Average review cycle	网友分享经验：平均14.0个月
Average recruitment ratio	网友分享经验：容易

Journal of Manufacturing Science and Engineering

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