Laser Sharpening of Carbon Fiber Microelectrode Arrays for Brain Recording

Microwire microelectrode arrays (MEAs) are implanted in the brain for recording neuron activities to study the brain functioning mechanism. Among various microwire materials that had been applied, carbon fiber is outstanding due to its small footprint (6–7 μm), relatively high Young’s modulus, and low electrical resistance. Tips of microwire in MEAs are often sharpened to reduce insertion force. Currently, carbon fiber MEAs are sharpened with either torch burning, which can only give a uniform length of wires in an array, or electrical discharge machining (EDM), which requires circuit connection with each single carbon fiber. The sharp tip results from intense burning induced by a flame or spark, leading to poor repeatability and controllability of the sharp tip geometry. In this paper, a laser-based, non-contact carbon fiber sharpening method is proposed, which enables controllable and repeatable production of carbon fiber MEAs of custom electrode lengths, insulation stripping lengths, and sharpened tips. Path of laser movement is designed according to desired array pattern. Variation in tip geometry can be accomplished by changing laser output power and moving speed. Test with different laser parameters (output power and moving speed) were conducted. Tip sharpening results were evaluated and analyzed in terms of tip geometry and insulation stripping length. Results showed that to achieve the desired MEA with sharper tip and shorter insulation stripping length, a higher laser power with faster moving speed is preferred.

Data-Enabled Modeling and Performance Evaluation of Multistage Serial Manufacturing Systems With Quality Rework

In a multistage serial production line, products with defect can be repaired or reworked to ensure high product quality. This paper studies a multistage serial manufacturing system with quality rework loops. Rework is the activity to repair or repeat the work on the defect parts during manufacturing processes, and it adds to cost and cycle time. This paper introduces an event-based data-enabled mathematical model for a stochastic production line with quality rework loops. The system performance properties are analyzed and permanent production loss due to quality rework loops is identified. The mathematical model and system performance identification methodology are studied analytically through numerical case studies.

Research on Intelligent Optimization Algorithm for Multi-Objective Disassembly Line Balancing Problem

For resource reutilization and environmental protection, remanufacturing gets more and more attention in many countries. Disassembly is a critical part of traditional manufacturing industry, but the traditional disassembly operation is mainly done by workers, which is low-efficiency. Now the use of robots can improve production efficiency a lot, which involves the problem of disassembly line balancing. Due to the constraints such as product complexity and precedence relationship between tasks, when the number of tasks increases, the combination scheme between tasks increases geometrically, and conventional algorithms are difficult to solve the problems, the Disassembly Line Balancing Problem (DLBP) is generally necessary to optimize multiple objectives. In this research, the author selects a variety of intelligent optimization algorithms to resolve the complex disassembly line balancing problem in different dimensional objective space. Four representative algorithms are selected from three angles to be compared through three performance indicators. It is concluded that these algorithms have different search capabilities for different specifications and objective space. Researchers should carefully select the algorithm according to the specific disassembly problem. The appropriate algorithm should be selected according to the scale of the disassembly line problem and the number of optimization objectives in actual production practice.

Sketch-Based Tensor Decomposition for Non-Parametric Monitoring of Electrospinning Processes

Electrospinning is a promising process to fabricate functional parts from macrofibers and nanofibers of bio-compatible materials including collagen, polylactide (PLA), and polyacrylonitrile (PAN). However, the functionality of the produced parts highly rely on quality, repeatability, and uniformity of the electrospun fibers. Due to the variations in material composition, process settings, and ambient conditions, the process suffers from large variations. In particular, the fiber formation in the stable regime (i.e., Taylor cone and jet) and its propagation to the substrate plays the most significant role in the process stability. This work aims to designing a fast process monitoring tool from scratch for monitoring the dynamic electrospinning process based on the Taylor cone and jet videos. Nevertheless, this is challenging since the videos are of high frequency and high dimension, and the monitoring statistics may not have a parametric distribution. To achieve this goal, a framework integrating image analysis, sketch-based tensor decomposition, and non-parametric monitoring, is proposed. In particular, we use Tucker tensor-sketch (Tucker-TS) based tensor decomposition to extract the sparse structure representations of the videos. Additionally, the extracted monitoring variables are non-normally distributed, hence non-parametric bootstrap Hotelling T2 control chart is deployed to handle this issue during the monitoring. The framework is demonstrated by electrospinning a PAN-based polymeric solution. Finally, it is demonstrated that the proposed framework, which uses Tucker-TS, largely outperformed the computational speed of the alternating least squares (ALS) approach for the Tucker tensor decomposition, i.e., Tucker-ALS, in various anomaly detection tasks while keeping the comparable anomaly detection accuracy.

Verification of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation

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.

Spatially-Constant Material Removal Control Under Variable-Speed Robotic Sanding

During manufacturing, minor flaws in the surface of commercial airplane interior panels are often corrected using hand sanding, sometimes leading to repetitive stress injuries. Robotic sanding is an attractive option to mitigate these injuries. However, in preprogrammed automated sanding, both the sander path and the speed along that path are predetermined. Such fixed automation has limited effectiveness due to the part-to-part variability of surface condition. In addition, in typical fixed automation, a constant contact force and path speed are used to maintain constant material removal depth. Teleoperated robotic sanding allows a skilled operator to monitor the process and the condition of the surface in real time to correct the individual flaws. However, during teleoperated sanding, the path and speed along the path are inherently both time-varying and unknown a priori. The principal contribution of this work is to facilitate precision teleoperated sanding by developing a process model and control strategy that ensures constant material removal depth along the sanding path. Experimental results, with and without the proposed contact-force adjustments, for the same variable speed motion of the sander, shows 65% improvement in spatial variation of material removal with the proposed approach.

A Study on Micro-Milling Process of Multi-Directional Carbon Fiber Reinforced Plastic Composite Using Nano-Solid Dry Lubrication

In this research, the micro-milling process using nano-solid dry lubrication is studied for machining multidirectional carbon fiber reinforced plastic (MD-CFRP). For the nano-solid dry lubrication, two kinds of graphene nanoplatelets and multiwall carbon nanotubes are used as nanoparticles. The workpiece is an MD-CFRP composite in which 10 plies of prepreg are laminated and it consists of four carbon fiber orientations — 0°, 45°, 90°, 135°. After a series of micro-milling experiments, the workpiece surface quality and tool wear are investigated. Overall, it is found that the nano-solid dry lubrication can improve the surface quality and reduce the tool wear. In particular, larger graphene nanoplatelets (xGnP H-5) are more advantageous than smaller graphene nanoplatelets (xGnP C-750). In addition, multiwall carbon nanotubes having a tube-shape structure are less effective than graphene nanoplatelets having a two-dimensional thin sheet shape for enhancing the micro-milling performances, which may be due to better lubrication effect with the graphene nanoplatelets’ sliding phenomenon at the cutting region.

Expulsion Intensity Monitoring and Modeling in Resistance Spot Welding Based on Electrode Displacement Signals

Weld expulsion is one of the most common welding defects during resistance spot welding (RSW) process especially for high strength steels (HSS). In order to control and eventually eliminate weld expulsion in production, accurate assessment of the expulsion severity should be the first step and is urgently required. Among the existing methods, real-time monitoring of RSW-related process signals has become a promising approach to actualize the online evaluation of weld expulsion. However, the inherent correlation between the process signals and the expulsion intensity is still unclear. In this work, a commonly used process signal, namely the electrode displacement and its instantaneous behavior when expulsion occurs are systematically studied. Based upon experiments with various electrodes and workpieces, a nonlinear relation between the weight of expelled metal and the sudden displacement drop accompanied by the occurrence of weld expulsion is observed, which is mainly influenced by electrode tip geometry but not by material strength or sheet thickness. The intrinsic relationship between this specific signal feature and the magnitude of expulsion is further explored through geometrical analysis, and a modified analytical model for online expulsion evaluation is finally proposed. It is shown that the improved model could be applied to domed electrodes with different tip geometries and varying workpieces ranging from low carbon steel to HSS. The error of expulsion estimation could be limited within ±20.4 mg (±2σ) at a 95% confidence level. This study may contribute to the online control of weld expulsion to the minimum level.

Numerical Simulation and Experimental Validation of Pure Water Jet Machining of Ti-6Al-4V

The most common problem when machining titanium using traditional metal cutting processes is that tools rapidly wear out and need to be replaced. This study examines the ability of a pure water jet to machine Ti-6Al-4V via simulations using ABAQUS’s Smoothed Particle Hydrodynamics (SPH). These simulations are then validated experimentally at two pressures, 138 MPa and 317 MPa. Using a Maxiem water jet built by Omax, experiments are conducted by creating a series of 5 lines that are 5 inches (127 mm) long placed 0.5 inches (12.7 mm) apart on a 1 mm thick Ti-6Al-4V workpiece. Predictive modeling is also conducted using the two additional pressures 400 MPa and 621 MPa as well as three orifice diameters 0.254 mm, 0.3556 mm, and 0.4572 mm. The simulations are validated at both pressures and had a percent error less than 2.6% which were within the standard deviation of the experimental results. The predictive modeling indicates that the pressures above 317 MPa create a near identical percent increase from the orifice diameter but the kerf has a more noticeable decrease in width of cut as the pressure increases. The 138 MPa has the smoothest surface profile compared to the other pressures. The volume of removed material decreases as the pressure increases but the material removal rate (MRR) increases as the pressure increases. This is due to the velocity of the water increasing as the pressure increases causing a lower run time. The 621 MPa is the best pressure to machine Ti-6Al-4V as it has a better MRR than the other pressures used in the predictive modelling.

Study of Effect of Repetition Rate on Laser Shock Peening Using Finite Element Modeling

A two-dimensional finite element model is developed to simulate the interaction between metal samples and laser-induced shock waves. Multiple laser impacts are applied at each location to increase plastically affected depth and compressive stress. The in-depth and surface residual stress profiles are analyzed at various repetition rates and spot sizes. It is found that the residual stress is not sensitive to repetition rate until it reaches a very high level. At extremely high repetition rate (100 MHz), the delay between two shock waves is even shorter than their duration, and there will be shock wave superposition. It is revealed that the interaction of metal with shock wave is significantly different, leading to a different residual stress profile. Stronger residual stress with deeper distribution will be obtained comparing with lower repetition rate cases. The effect of repetition rate at different spot sizes is also studied. It is found that with larger laser spot, the peak compressive residual stress decreases but the distribution is deeper at extremely high repetition rates.