Multi-modal Human Fatigue Classification using Wearable Sensors for Human-Robot Teams

Our main objective of this study is to create a fatigue detection model using real-time data by using wearable sensors. The purpose of this research is to learn more about the way humans experience fatigue in a supervisory human-machine environment. The goal of this study is to evaluate machine learning algorithms that assess fatigue detection and to use robots for adapting its interactions. The environment itself consists of two different tasks to analyze Physical fatigue and Mental fatigue in two different task environments that are (i) Jigsaw puzzle-solving task, and (ii) Pick and Place task. Physical fatigue and mental fatigue are detected using wearable sensors: MYO armband and BioPac Bioharness. During the experiment, the Physiological metrics used are Heart rate, respiration rate, Heart rate variability, posture, breathing wave amplitude, and EMG. All these Physiological signals are collected simultaneously in a real-time task environment. The data collected by these physiological signals are then processed and machine learning and deep learning algorithms are used for further process in building a fatigue detection model

Computational Validation of Injection Molding Tooling by Additive Layer Manufacture to Produce EPDM Exterior Automotive Seals

During the design and development of ethylene propylene diene monomer (EPDM) exterior automotive seals, prototype components can only manufactured through production tooling platforms by either injection molding or extrusion. Consequently, tooling is expensive and has long lead times. This paper investigates whether additive layer manufacture is a viable method for producing tooling used in injection molding of exterior automotive seals in EPDM. Specifically, a novel rapid tooling is a method that combines additive layer manufacture (ALM) with epoxy reinforcement. Computational validation is performed whereby the mechanical properties of the tool are evaluated. The research has concluded that the novel tooling configuration would be suitable for prototyping purposes which would drastically reduce both costly and environmentally detrimental pre-manufacturing processes. This work has laid the foundations to implement rapid tooling technology to the injection molding of prototype EPDM parts

Contact angle measurement on micropatterned surface using sessile drop shape fit profile detection

Micro-nano patterned surfaces have significant applications in various fields as they behave differently under the effect of catalysts, magnetic energy, electronic emission/absorption, optics and biological cells. Engineering these topologies demands a better understanding of the contact angle. The current contact angle measurement techniques assume the drop to be a perfect sphere, neglect gravitational and molecular dispersion effects; thereby leading to inaccuracies. This is because the micro-machined surfaces exhibit sub-micrometre scale porosity and pattern dimensions are comparable to the droplet size, resulting in composite interfaces at micro-nano scale. In this paper, the authors assessed the adaptability of conventional measurement techniques for textured surfaces and developed an algorithm that is based on curve fitting over sessile drop after edge detection. The algorithm performs edge detection, contact point identification and curve fitting and corrects uneven surfaces and was tested on micro-patterned surfaces fabricated over three different materials: polydimethylsiloxane, polystyrene and acrylic using laser

Investigation on bacterial adhesion and colonisation resistance over laser-machined micro patterned surfaces

Micro–nano patterns created directly over solid surfaces to combat microbial activity help in preventing hospital-acquired infections. This Letter is focused on defining surface topologies by laser patterning over solid surfaces. Studies on designing surface topologies and bacterial culture have been carried out and the feasibility of micro scale features in restricting bacterial growth has been investigated. The effects of the engineered roughness index and contact angle are discussed. Contact angle measurement over patterned surfaces using a novel computer vision-based technique is demonstrated and the effect of contact angle on bacterial adhesion has been presented. The results obtained show that the designed micro scale geometries can effectively reduce the growth of bacteria on the said surfaces

Assessment of thermally induced shear stress and its effect on pattern waviness in CO2 laser ablation of birefringent polymers

This article presents the application of custom-designed poledioscope for dynamic measurement of thermally induced shear stress, as a technique for monitoring waviness of the microscale patterns created using CO2 laser, directly over optically birefringent polymers. Laser ablation experiments were conducted for three optical grade polymers: ethylene vinyl acetate, poly methyl methacrylate, and allyl diglycol carbonate under varying laser power and scanning speeds. A poledioscope, customized by incorporating beam splitter in place of rotating analyzer section of conventional polariscope,was used to assess the thermally induced shear stress on the materials in real time. The waviness of the profile ofgroove patterns was measured using a profilometer. The shear stress mapping and the profile waviness data recorded for range of laser processing parameters were further analyzed to determine that high thermally induced shear stress results in significant damage on waviness of the lased profile

Tool–workpiece contact detection in micro-milling using wireless-aided accelerometer sensor

Detection of tool–workpiece contact before the start of precision machining application is essential as it prevents tool breakage and aids in maintaining the accuracy of the machined workpiece. In this research, a wireless-aided three-axis accelerometer attached to a rotating micro-milling tool is used to detect tool–workpiece contact before the start of micro-milling operations. A three-axis accelerometer (ADXL345), an X-Bee pro wireless module and ATMEL328PP-U microcontroller along with other ancillaries were housed on a printed circuit board rigidly attached to a micro-milling tool using couplings. Subsequently, the micro-milling operation was conducted on three different materials, namely, aluminum, copper and brass, for three different revolutions per minute, depth of cut and feed velocity combinations. The accelerometer signals were received wirelessly in a personal computer. Impulsive change in accelerometer signal along Z-axis during machining indicated tool–workpiece contact. The depth of cut of the machined samples was measured using a profilometer. It was found that the setup was accurate in determining tool–workpiece contact at the start of micro-milling operations

Assessment of micro turning machine stiffness response and material characteristics by fuzzy rule based pattern matching of cutting force plots

In micro-nano systems technology (MNST), application of mechanical based machining operations such as micro turning, micro milling, micro EDM have shown promising trends to produce micro parts in batch scale. In order to ensure reproducibility better understanding on micro cutting process dynamics and sensitivity of machine stiffness and material characteristics becomes critical. In this paper, a methodology has been developed to assess machine stiffness and material dependent characteristics and demonstrated for micro turning operations conducted on DT-110 micro machining center. In this method, authors incorporate pattern matching algorithm to compare run data image of cutting force plots with that of reference plot. The reference plots of cutting forces v/s time were drawn from simulation run data computed from the micro turning process models. The run data plots of cutting force v/s time were drawn from the processed signal data obtained from the dynamometer during machining operation. The plots were fragmented into patterns and Euclidean distance computed between pair patterns of reference and measured cutting forces v/s time plot image represents the changes happened in machining conditions. This has been used to perform backward calculation to assess the machine stiffness response and material characteristic constants variations over machining time. In order to perform these comparative pattern error adjustments between reference and measured cutting force plots a fuzzy rule based algorithm has been developed

Pulse Electrocodeposited Ni–WC Composite Coating

In the present work, tungsten carbide (WC) particulate of average size 10 µm were electrocodeposited in the nickel metal matrix, to form metal matrix composite (MMC) coating over the EN8 steel substrate. The electrodeposition of Ni–WC particulate composite coating was carried out using the Watt's bath under the influence of varying current density and duty cycle. It was found that current density of 0.02 A/cm2 was sufficient to start the codeposition kinetics. But, good quality of electrodeposition was obtained at a current density of 0.04 A/cm2. The WC particulate entrapment and distribution of WC particles in Ni matrix according the variation in experimental parameters has been reported. The dense and compact microstructure was obtained at a current density of 0.04 A/cm2 and duty cycle of 30%. Microhardness and corrosion resistance properties of composite coating were also evaluated and reported

Tool strain–based wear estimation in micro turning using Bayesian networks

Estimation of tool wear in micro turning is important as it enhances the process fidelity and the surface quality of the job. In this work, a simple process is demonstrated that estimates the tool wear from strain data near the cutting edge of the tool tip for micro turning operations. The tool strain for tool with six different wear lengths, collected using fiber Bragg grating sensor, was preprocessed to generate a probability distribution. The strain and tool wear data were used as the training dataset. This training dataset was subjected to maximum likelihood estimation algorithm to obtain the conditional probability distribution table required for the functioning of a suitable Bayesian network. The Bayesian network was tested for estimation of tool wear using strain data as priors for three different experiments. The maximum error in tool wear estimation using this procedure was ∼6 µm

A novel approach to fabricate dye-encapsulated polymeric micro- and nanoparticles by thin film dewetting technique

A new method is reported for fabrication of polymeric micro- and nanoparticles from an intermediate patterned surface originated by dewetting of a polymeric thin film. Poly (d, l-lactide-co-glycolide) or PLGA, a biocompatible polymer is used to develop a thin film over a clean glass substrate which dewets spontaneously in the micro-/nano-patterned surface of size range 50nm to 3.5µm. Since another water-soluble polymer, poly vinyl alcohol (PVA) is coated on the same glass substrate before PLGA thin film formation, developed micro-/nano-patterns are easily extracted in water in the form of micro- and nanoparticle mixture of size range 50nm to 3.0µm. This simplified method is also used to effectively encapsulate a dye molecule, rhodamine B inside the PLGA micro-/nanoparticles. The developed dye-encapsulated nanoparticles, PLGA-rhodamine are separated from the mixture and tested for in-vitro delivery application of external molecules inside human lung cancer cells. For the first time, the use of thin film dewetting technique is reported as a potential route for the synthesis of polymeric micro-/nanoparticles and effective encapsulation of external species therein