Fast and Robust Approach to Find the Gouge-free Tool Position of the Toroidal Cutter for the Bézier Surface in Five Axis Machining

One of the approach used for tool path generation for Bézier surfaces is the Multipoint machining (MPM) approach, in which the toroidal cutter touches the machined surface at two points of contact. Multipoint machining helps in reducing the machining time by providing the tool path data that machines the surface in wider strips positioning the tool in the close proximity to the surface. The tool path generation using MPM is computationally expensive and time consuming, as it involves the solving of non-linear transcendental equations that require numerical methods. Numerical method such as Newton’s method are a time consuming and iterative process, and are not always able to give a solution. In this work, two methods, the ‘Drop, Rotate and Drop (DRD) method’ and the ‘Vertical and Circular Ray Firing (VCRF) method’, are developed, implemented and tested on bi-cubic Bézier surfaces using a Hi-Dyn tilt-rotary simultaneous five axis machining center. These methods follow the Multipoint machining approach. The DRD method limits the use of Newton’s method for convergence to the solution of two unknowns or variables. Whereas, the VCRF eliminates the use of Newton’s method for obtaining the solution and instead uses the implicit equations for firing the rays vertical or circular from the surface towards the toroidal cutter surface. Hence, the methods developed in this work give a fast and robust approach for generating tool path data for the Bézier surfaces

Subject-adjustable computational modelling for the analysis of human head–neck motions in rear-end impacts

Whiplash Associated Disorder (WAD) is a general term used to describe minor injuries to the neck, mostly as a result of a rear-end motor vehicle collision. Although the injury is defined as minor, the long-term symptoms such as neck pain, stiffness, headache, or concentration difficulties, result in high costs to the economy, healthcare services and individuals. Consequently, there has been significant amount of research undertaken to understand and prevent WAD, covering experimental and computational studies. However, whiplash injuries are difficult to detect since diagnostic tools such as X-rays, CT (Computed Tomography) scans or MRI (Magnetic Resonance Imaging) are not suitable to identify the location or the extent of the injury. Also, the injury mechanisms are not fully understood; hence mathematical criteria are used as surrogates to estimate the likelihood of injury. In the present research, a biofidelic, subject-adjustable head-and-neck model (i.e. the model is adjustable for individual subject characteristics) has been developed for rear-end impact whiplash analysis. Existing literature is used to develop the overall research framework (methodology), which has three main objectives: first to explain the importance of personalised protection investigations, second to evaluate the suitability of existing data for a subject-adjustable model, and third to define the required steps in the design of such a model.To generate the geometry of the model, previously published cascading equations capable of predicting the main vertebrae dimensions based on the subject characteristics age, gender and height are used. Also, in line with previous work, seven cervical neck segments represent the seven cervical vertebrae and all surrounding cervical tissues properties. The mass and moment of inertia properties of each segment are lumped into each respective segment. The intervertebral behaviour for two adjacent segments is described by non-linear spring-damper functions, which change according to subject specific characteristics. The model is driven by specification of the first thoracic vertebra (T1) motion.The model combines existing data and methods from different sources, utilising available data in the public domain. New procedures and techniques are incorporated to create a homogeneous model, which is adaptable to a wide range of subjects. The developed computational model is not simply a linear scaling of a master-model to other dimensions, but rather uses prediction equations to create the desired anthropometric model. The anthropometric model predictions for body part dimensions and inertia properties are successfully verified using anthropometric surveys available in the literature.Using lumped and non-linear stiffness and damping equations for the intervertebral joints, and without modelling separate muscles, the model is dynamically calibrated for different experimental sled test data available in the open domain. The joint equations and their coefficients are derived based on published joint data measurements on Post Mortem Human Subjects (PMHS); a scaling of these coefficients is applied to match the overall head-and-neck kinematics of the computational model to the experimental sled test kinematics. For each experimental study, the global head kinematics of the model was calibrated successfully to mimic the head kinematics.The model has been modelled to represent subjects with different anthropometric characteristics, involving a novel relationship between intervertebral joint coefficients and anthropometric subject specifications. The observed effect of each change of anthropometric subject characteristic is evaluated independently using time-history diagrams; then the observed effect of multiple changes of anthropometric subject characteristics is assessed using multi-dimensional response surfaces for the response’s highest magnitude.The analysis of the proposed model has revealed that existing work involving the use of lumped parameter models is not as robust as claimed. This is because existing work has always been evaluated using a low number of validation graphs, i.e. using only the graphs which gave good validation results. The proposed model has been comprehensively evaluated and its limitations are addressed. The developed model had to merge different studies (different ethnical backgrounds, different subject types, etc.) together to create an adjustable model; this is because of the limited available data. The final model is the most homogeneous model currently possible. In addition, there is also limited relevant experimental data for full validation of the model, which is not ideal. Nevertheless, reliable results for the comparison of global head kinematics compared with several experimental sled test studies have been obtained for the average male subject model. Also, using the proposed model the dynamic effects resulting from anthropometric subject differences have been evaluated; these effects are almost perfectly linear relationships for each subject characteristic change. Potential applications for the developed model are the injury assessment based on mathematical whiplash injury criteria, head-restraint optimisation to minimise injury risk and the improvement of neck biofidelity in anthropometric test devices.</div

Advances on Mechanics, Design Engineering and Manufacturing III

This open access book gathers contributions presented at the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2020), held as a web conference on June 2–4, 2020. It reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design; innovative design; and computer-aided design. Further topics covered include virtual simulation and reverse engineering; additive manufacturing; product manufacturing; engineering methods in medicine and education; representation techniques; and nautical, aeronautics and aerospace design and modeling. The book is organized into four main parts, reflecting the focus and primary themes of the conference. The contributions presented here not only provide researchers, engineers and experts in a range of industrial engineering subfields with extensive information to support their daily work; they are also intended to stimulate new research directions, advanced applications of the methods discussed and future interdisciplinary collaborations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports