Abstract An experimental study of shear band formation in cutting of metals is made using a low melting point Bi-based alloy as a model material system. High-speed imaging is used to capture the transition in the plastic flow, from laminar to shear banded flow, as a function of cutting speed. The dynamics of shear band nucleation is captured in situ and temporal evolution of localized plastic flow during shear band growth is quantitatively analyzed using an image correlation method, particle image velocimetry (PIV). The observations show that shear band nucleation is governed by a critical shear stress criterion, and accompanied by a large drop in the flow viscosity by several orders of magnitude, analogous to the phenomenon of yielding in yield-stress (Bingham) fluids. Likewise, the displacement field around a freshly nucleated shear band evolves in a manner resembling the boundary layer formation in planar flow of a Bingham fluid with a very small viscosity. Surprisingly, temperature has little influence on shear band nucleation or growth.

Abstract Surface morphology is a critical factor to determine the surface functionality. Ultrafast pulsed laser is a powerful tool to induce multi-scale surface structures on different materials, and thus control their functionalities. This paper presents an experimental study on the micro/nano-scale surface structure formation by femtosecond laser irradiation on stainless steel. The effects of laser fluence, effective pulse number per spot, and repetition rate on the structure morphology and dimension are studied. By controlling the laser parameters, four different types of structures are obtained: (1) laser-induced periodic surface structrues (LIPSS), (2) microgrooves covered by LIPSS, (3) microgrooves/micro-holes covered by LIPSS, and (4) irregular microstructures. The formation dynamics and mechanisms of different structures are investigated.

Abstract For the scalable production of commercial products based on vertically aligned carbon nanotubes (VACNTs), referred to as CNT forests, key manufacturing challenges must be overcome. In this work, we describe some of the main challenges currently facing CNT forest manufacturing, along with how we address these challenges with our custom-built rapid thermal processing chemical vapor deposition (CVD) reactor. First, the complexity of multistep processes and reaction pathways involved in CNT growth by CVD limits the control on CNT population growth dynamics. Importantly, gas-phase decomposition of hydrocarbons, formation of catalyst particles, and catalytic growth of CNTs are typically coupled. Here, we demonstrated a decoupled recipe with independent control of each step. Second, significant run-to-run variations plague CNT growth by CVD. To improve growth consistency, we designed various measures to remove oxygen-containing molecules from the reactor, including air baking between runs, dynamic pumping down cycles, and low-pressure baking before growth. Third, real-time measurements during growth are needed for process monitoring. We implement in situ height kinetics via videography. The combination of approaches presented here has the potential to transform lab-scale CNT synthesis to robust manufacturing processes.

Downtime has been considered as the most significant contributor to system inefficiency in manufacturing industry. During a downtime, the stoppage at the downtime machine will lead to impact on system production. In addition, it will also change system buffer levels, and thus alter system future behavior, which may lead to impact on system production in future time. For each downtime, a complete evaluation of its production impact during and after the downtime is important for deciding where to allocate limited resources for improving system production. However, the evaluation of downtime impact on system future production has not been studied. Motivated by this need, we investigate how each downtime would change the buffer levels and impact system production in the future. The concept of downtime settling time is proposed to reflect such impact. Given the downtime list, an analytical method is developed to estimate this impact of each downtime. Case studies are conducted to demonstrate the accuracy and effectiveness of the proposed methods.

The quest for smarter manufacturing is motivating the need for operational decisions to be made in real-time to adapt to uncertainties. Prevailing decision-making techniques typically consider each manufacturing function in isolation to reduce the complexity, which in turn leads to sequential decision-making where prior decisions become constraints for subsequent decisions. This paper proposes a unified approach for simultaneously controlling the timing of production events, the timing of maintenance events, and physical processing capacity. Moreover, the control algorithms can be fully distributed and exploit physics-based models for processes and remaining-useful-life of machinery components in real-time decision-making. Fused Filament Fabrication (FFF) additive manufacturing process is used as an example in the paper to demonstrate the unified approach. Dynamics of the resulting unified control system is modeled using non-linear discontinuous differential equations. Computer simulations are used to illustrate dynamic interactions between production and maintenance functions. Benchmarking of the unified control approach for randomly generated datasets show superior performance compared to other commonly used scheduling heuristics by about 48%.

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

Throughput bottlenecks define and constrain the productivity of a production line. Prediction of future bottlenecks provides a great support for decision-making on the factory floor, which can help to foresee and formulate appropriate actions before production to improve the system throughput in a cost-effective manner. Bottleneck prediction remains a challenging task in literature. The difficulty lies in the complex dynamics of manufacturing systems. There are multiple factors collaboratively affecting bottleneck conditions, such as machine performance, machine degradation, line structure, operator skill level, and product release schedules. These factors impact on one another in a nonlinear manner and exhibit long-term temporal dependencies. State-of-the-art research utilizes various assumptions to simplify the modeling by reducing the input dimensionality. As a result, those models cannot accurately reflect complex dynamics of the bottleneck in a manufacturing system. To tackle this problem, this paper will propose a systematic framework to design a two-layer Long Short-Term Memory (LSTM) network tailored to the dynamic bottleneck prediction problem in multi-job manufacturing systems. This neural network based approach takes advantage of historical high dimensional factory floor data to predict system bottlenecks dynamically considering the future production planning inputs. The model is demonstrated with data from an automotive underbody assembly line. The result shows that the proposed method can achieve higher prediction accuracy compared with current state-of-the-art approaches.

This paper describes a novel method to analyze the effect of the fiber laser induced plasma on welding of dissimilar Al and Cu. Based on spectroscopic studies, specific wavelengths were found to be suitable for process monitoring during laser welding. For aluminum, the double peak at 394.4 and 396.1 nm was studied, for cu the 578 nm peak was studied. The characteristic peaks were observed by multiple photodiodes equipped with narrow optical filters with varying FWHM. Analyzing the plasma emissions of the process enabled to detect material changes in the plasma plume. This novel method has the advantage of higher sampling rate combined with reduced costs compared to a spectrometer and is thus able to capture the process dynamics.

Additive manufacturing, also known as three-dimensional (3D) printing, is an approach in which a structure may be fabricated layer by layer. For 3D inkjet printing, droplets are ejected from a nozzle and each layer is formed droplet by droplet. Inkjet printing has been widely applied for the fabrication of 3D biological gel structures, but the knowledge of the microscale interactions between printed droplets is still largely elusive. This study aims to elucidate the alginate layer formation process during drop-on-demand inkjet printing using high speed imaging and particle image velocimetry. Droplets are found to impact, spread, and coalesce within a fluid region at the deposition site, forming coherent printed lines within a layer. Interfaces are found to form between printed lines within a layer depending on printing conditions and printing path orientation. The effects of printing conditions on the behavior of droplets during layer formation are discussed and modeled based on gelation dynamics, and recommendations are presented to enable controllable and reliable fabrication of gel structures.

This paper presents design of a novel boring bar with a ratio of cylinder length to diameter (L/D) of 10 to suppress chatter vibration regardless of low stiffness. It is essentially difficult to decrease the compliance of the long slender structures. However, nominal compliance of the displacement along the depth of cut direction against the resultant cutting force may be regulated by giving the designated anisotropy upon the boring bar dynamics. The past research has clarified the feasibility through turning experiments by using the developed boring bar with L/D of 4. In the present study, much slenderer ones with L/D of 10 are designed, which are significantly flexible but attractive to manufacturers. Finite element analysis (FEA) is utilized to estimate dynamics of the anisotropic boring bar. Through analytical investigations, two kinds of boring bars were designed, where the compliance ratio of 1.53 or 1.88 was accomplished. Influence of several conditions on the chatter stability was investigated. Analytical investigations revealed that the chatter stability is significantly improved at a designated depth of cut by utilizing the proposed designs regardless of feed rate. In particular, the compliance ratio of 1.55 showed wider stable zone to attain chatter free boring, while chatter avoidance is impossible by use of the conventional isotropic boring bar under the same conditions.

Impacting water droplets are capable of eroding soil, rock, turbine blades and even high speed aircraft. Research has shown that high velocity water droplet impingement onto a solid workpiece can strip paint, remove rust, and serve as a machining operation. This technique is different from waterjet cutting as a train of water droplets are used to transport momentum to a workpiece rather than a continuous jet. Also, no abrasive medium is used which produces an environmentally friendly process. The exploitation of this water droplet impact phenomenon in industrial applications as a means to deform and remove material does not currently exist. If the impact dynamics of water droplets can be understood and controlled, then industries would have the framework upon which they could employ this phenomenon in novel manufacturing equipment. In this paper, as a starting point, the impact force of 2.9mm diameter water droplets impacting at low velocities (∼2m/s) was studied experimentally and numerically. A piezoelectric force sensor was used to measure the transient force of impacting water droplets, while numerical simulations were used to identify the solid-fluid interaction and develop the basis for high velocity (>100m/s) liquid droplet impact dynamics. Agreement was established between experimental observations and numerical predictions for the impact velocities considered.

The productivity and efficiency of production systems are greatly influenced by their configuration and complex dynamics subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. In this paper, we develop a mathematical model of production systems with general structure (tandem line, parallel, and etc.) to estimate the status of the system (production counts and processing speeds of the stations, buffer levels and production loss) by using sensor data of disruption events. Real-time production system performance such as effective disruption events, opportunity window, and permanent production loss are identified, which is very useful in real-time control to increase overall system efficiency.

Bi-rotary milling head is one of the core components of five-axis machining center and its dynamic characteristics directly affect the machining stability and accuracy. During the five-axis machining, the milling head would change the posture continuously. To facilitate rapid evaluation and optimization of the dynamic behavior of the bi-rotary milling head within the whole workspace, a multi-rigid-body dynamic model considering the flexible joint is established. The varying stiffness of the flexible joints affected by gravity and cutting force at different swing angles is analyzed. A parametric dynamic equation with posture parameters and physical parameters is finally deduced. The sensitive structural parameters on natural frequency and its fluctuation within the workspace are obtained by the sensitivity analysis. The theoretical and experimental results show that the dynamics of the bi-rotary milling head behave pose-dependent and the varying dynamics can be positive controlled by modifying sensitive structural parameters at the design stage.

Coupled torsional-axial vibrations of the drilling tool play a significant role in the machining dynamics of the drilling process. In this paper, the torsional-axial vibrations of the drilling tool due to the warping deformation of the pretwisted flute is modeled. An enhanced receptance coupling model is developed to predict the coupled torsional-axial vibrations of the drilling tool considering the dynamics of fixture and clamping conditions. Rigid and flexible receptance coupling methods are used, with the simulation results verified through modal experiments. The proposed model is able to provide optimum drilling tool configurations to avoid undesired tool vibrations and improve hole quality.

Effective maintenance operations are essential to improve the competitiveness of manufacturing enterprises. However, the existing maintenance policies usually ignore the real-time dynamics of the system and cannot respond promptly to the demand changes in the market. This paper investigates the hidden opportunities that one machine can be stopped for maintenance during production time, while the throughput requirement in a specific horizon can still be satisfied. We define these time windows as active maintenance opportunity windows (AMOWs), and predict them based on the real-time operational data in manufacturing systems with different configurations and Bernoulli machines.

In order to investigate the rounding process of high precision centerless grinding in time domain reliably and predictably, a virtual machine design approach is essential particularly towards 0.1–0.3μm roundness precision. The virtual machine system presented in this paper is developed through high-level integration of the machine characteristics, workpiece rounding mechanism and their intrinsic correlation. In this system, the primary elements affecting the workpiece roundness regeneration, including kinematics, dynamics and control of the machine, grinding dynamics and machining conditions, are integrated into the high precision virtual centerless grinding to render the rounding process, evaluate the machine performance and optimize grinding strategies accordingly. An application case study on virtual machining of the targeted workpiece roundness 0.1μm is provided to present the implementation perspectives of the virtual machine system for high precision centerless grinding.

Nowadays, service-oriented manufacturing (SOM) systems (e.g., cloud manufacturing (CMfg), product service systems (PSS), etc.) have attracted more and more interesting and attention of researchers from many different fields. However, because of the complex and dynamic environment, one of the most important issues need to be addressed for the promotion and application of SOM system is the dynamic supply-demand matching and scheduling of manufacturing resource services. In this paper, the issue of supple-demand matching in the typical SOM system is carried out at first. Then the dynamics and different models facing different users and different demands are analyzed respectively. As a result, a hypernetwork-based solution framework of this issue and the cloud manufacturing platform adding with the related functions are proposed with consideration of multi-objects, statistical characteristics and evolution. Finally, some future works with big data and industrial Internet of things are pointed out in the summary.

Part-2 of this paper is focused on studying the droplet spreading and the subsequent evaporation/film-formation characteristics of the graphene oxide colloidal solutions that were benchmarked in Part-1. A high-speed imaging investigation was conducted to study the impingement dynamics of the colloidal solutions on a heated substrate. The spreading and evaporation characteristics of the fluids were then correlated with the corresponding temperature profiles and the subsequent formation of the residual graphene oxide film on the substrate. The findings reveal that the most important criterion dictating the machining performance of these colloidal solutions is the ability to form uniform, sub-micron thick films of graphene oxide upon evaporation of the carrier fluid. Colloidal suspensions of ultrasonically-exfoliated graphene oxide at concentrations < 0.5 wt% are best suited for micromachining applications since they are seen to produce such films. The use of thermally-reduced graphene oxide suspensions at concentrations < 0.5 wt% results in non-uniform films with thickness variations in the 0–5 μm range that are responsible for the fluctuations seen in the cutting force and temperatures. At concentrations ≥ 0.5 wt%, both the thermally-reduced and ultrasonically-exfoliated graphene oxide solutions result in thicker and non-uniform films that are detrimental for machining results. The findings of this study reveal that the characterization of the residual graphene oxide film left behind on a heated substrate may be an efficient technique to evaluate different graphene oxide colloidal solutions for cutting fluids applications in micromachining.

Electrohydrodynamic jet (e-jet) printing is a growing technology for high resolution (<20μm) printing. It enjoys the advantages of other additive manufacturing technologies and is compatible with a large range of materials. E-jet applications include electronic fabrication, high-resolution prototyping, and bio-medical devices. Despite the diverse range of applications, e-jet printing dynamics are sensitive to varying standoff heights and changing electric fields. As such, conventional e-jet printing generally consists of a conductive nozzle printing onto a flat, conductive substrate. To address this limitation, this paper presents an airflow assisted e-jet printhead that is shown to greatly reduce the substrate effects while providing good printing resolution (<15μm). The working principle and design challenges are provided. Experimental demonstrations validate the performance capabilities of the modified e-jet printhead.

This paper presents a preliminary investigation into a method for reducing servo delays that occur after the execution of rapid positioning commands in computer numerically controlled (CNC) machines. A model of the machine’s dynamics is used to predict the servo error resulting from a prescribed rapid positioning command. Based on this information, the reference path is extended by an amount calculated to ensure that the predicted error falls within a user-specified threshold in the shortest possible time. The proposed method is shown in simulations to significantly shorten rapid positioning time compared to the industry-standard approach of compensating for servo delays by pausing motion until servo errors fall within user-specified thresholds. However, experiments reveal the need for further research to enhance the versatility and robustness of the method.