High birefringence photonic bandgap fiber with elliptical air holes

In this paper, we proposed a high birefringence photonic bandgap fiber (PBGF) with elliptical air holes in cladding and circular air hole in core. The forbidden gaps of the honeycomb structure are calculated when the circular air holes are deformed to the elliptical shape. And the birefringence of the PBGFs is investigated by using a full-vector finite element method (FEM) and the numerical results are presented.     

The influence of Johnson–Cook material constants on finite element simulation of machining of AISI 316L steel

In literature, five different sets of work material constants used in the Johnson–Cook's (J–C) constitutive equation are implemented in a numerical model to describe the behaviour of AISI 316L steel. The aim of this research is to study the effects of five different sets of material constants of the J–C constitutive equation in finite-element modelling of orthogonal cutting of AISI 316L on the experimental and predicted cutting forces, chip morphology, temperature distributions and residual stresses. Several experimental equipments were used to estimate the experimental results, such as piezoelectric dynamometer for cutting forces measurements, thermal imaging system for temperature measurements and X-ray diffraction technique for residual stresses determination on the machined surfaces; while an elastic–viscoplastic FEM formulation was implemented to predict the local and global variables involved in this research. It has been observed that all the considered process output and, in particular the residual stresses are very sensitive to the J–C's material constants.     

Small volume PCR in PDMS biochips with integrated fluid control and vapour barrier

In this paper we demonstrate a new method for microfabricating PDMS devices that controls vapour diffusion, thereby reducing water loss at elevated temperatures and greatly increasing the reliability of the PCR. In the past, the vapour and liquid diffusion properties of the PDMS material in microfluidic devices have impaired performance. We show that this water loss is primarily due to vapour diffusion from the PDMS biochip and by implanting a polyethylene vapour barrier layer in the PDMS, the overall fluid loss was almost eliminated (reduced by a factor of 3). We have also developed a procedure to ensure irreversible bonding between the PDMS and the implant. With this improved microfabrication method we demonstrate the feasibility and advantages of performing small volume PCR genetic amplification (i.e. with less than 2 μl of PCR sample) within a PDMS–glass hybrid biochip. Diaphragm pumps and pinch-off valves were integrated in the system and these enabled fluid retention during the amplification stage and will facilitate higher levels of on-chip automation.     

Relation between wear and tooth width modification in spur gears

In this experimental study, width modification of a spur gear was investigated to fix instantaneous pressure changes along single meshing area on the gear profile. In this gear, variable pressure distribution caused by the single and double teeth meshing and the radius of curvature along the active gear profile was approximately kept constant by maintaining a constant ratio of applied load to the tooth width (F/b) on every point. Hence, Hertz pressure distribution along the gear profile was approximately equally achieved. Other factors affecting wear were kept constant during the experiment. The specimen used was made from AISI 4140 steel. Finally, the amount of wear in the teeth profiles between the modified and unmodified gears was compared. And wear depth of modified gear along the meshing area was almost uniform.     

Design of composite data acquisition gateway based on 5G network

The integrated data collection system hasn't changed much over the years. Their growth is dominated by digital filtering, and they get a new avionics bus. Time division multiplexing is still used to communicate with the system's data collector. These schemes make use of commands and data buses. While this method works, it has many drawbacks. These shortcomings are overwhelming the strict system architecture, system bandwidth limits, and expertise to only obtain an avionics bus, otherwise system bandwidth and one-way flow of data and control. This lead for high-end video recorders. To quietly monitor our actions and valuable information provides important warnings. However, it is usually completed by a device with this small processing power, which tracks the device, stores the data of the user's data, and is limited in processing. Therefore, it is important to store and process user data in the cloud to track 5 G network activity. This article proposes a simple 5G network gateway solution for custom user monitoring equipment. Collects charge data on Network 5G and Charging-aware multi-mode based routing protocol (CMRP). The CMRP routing mechanism is not yet stable, but the changes depend on the state of the energy charge sensor. This cannot be complemented by energy-efficient sensors and routing protocols that consume less energy.     

The ability of eye-tracking metrics to classify and predict the perceived driving workload

Traffic safety is directly related to the mental and physical condition of the driver. Performing regular secondary tasks while driving is an additional activity that dissipates attention and adds to the drivers' workload. Identifying driver fatigue and workload based on gaze behavior is one way to ensure a safe driving experience. The purpose of this paper is to classify and predict driving perceived workload using a set of eye-tracking metrics (gaze fixation, duration, pointing, and pupil diameter). The ability of eye-tracking metrics to predict driving workload has been investigated. As a result, frustration, performance, and temporal load showed a correlation with gaze metrics. Gaze point, duration, fixation, and pupil diameter significantly influence driving workload.Relevance to industry: Results will supply the specialists in eye-tracking/sensor technologies and traffic safety with new knowledge to improve the design of the driving performance and safety monitoring systems and efficiency of the driving process.     

Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

Accurate positioning of a drilling and riveting cell for aircraft assembly

Modern aircraft assembly demands assembly cells or machines with higher machining efficiency and accuracy. Thus, a dual-machine drilling and riveting cell is developed in this paper. We firstly discuss its physical design, as well as the automatic drilling and riveting process. With the automatic drilling and riveting cell, drilling and riveting production line of aircraft panels can be expected. The frame chain of the drilling and riveting cell is constructed to link the assembly cell to its task space, which is the kinematics base. System calibrations, including task space calibration, the sensor calibration of an orientation alignment unit, the floating calibration of the implicit hand-eye relationship, are explored. For high positioning accuracy, a multi-sensor servoing method is proposed for cell positioning. An orientation-based laser servoing strategy, which uses the feedback of the orientation errors measured by laser displacement sensors, is used to align drilling direction and camera shooting direction. Besides, A single-camera-based visual servoing is applied to align the tool center point (TCP) to reference holes, to obtain their coordinates for drilling position modification. Experiments of multi-sensor servoing for cell positioning are performed on an automatic drilling and riveting machine developed for the panel assembly of an aircraft in China. With the cell positioning method, the automatic drilling and riveting cell can approximately achieve an accuracy of 0.05 mm, which can adequately fulfill the requirement for the assembly of the aircraft.