Abstract Powder bed fusion (PBF) is a widely used additive manufacturing (AM) technology to produce metallic parts. Understanding the relationships between process parameter settings and the quality of finished parts remains a critical research question. Developing this understating involves an intermediate step: Process parameters, such as laser power and scan speed, influence the ongoing process characteristics, which then affect the final quality of the finished parts. Conventional approaches to addressing those challenges such as powder-based simulations (e.g., discrete element method (DEM)) and voxel-based simulations (e.g., finite element method (FEM)) can provide valuable insight into process physics. Those types of simulations, however, are not well-suited to handle realistic manufacturing plans due to their high computational complexity. Thermal simulations of the PBF process have the potential to implement that intermediate step. Developing accurate thermal simulations, however, is difficult due to the physical and geometric complexities of the manufacturing process. We propose a new, meso-scale, thermal-simulation, which is built on the path-level interactions described by a typical process plan. Since our model is rooted in manufactured geometry, it has the ability to produce scalable, thermal simulations for evaluating realistic process plans. The proof-of-concept simulation result is validated against experimental results in the literature and experimental results from National Institute of Standards and Technology (NIST). In our model, the laser-scan path is discretized into elements, and each element represents the newly melted material. An element-growth mechanism is introduced to simulate the evolution of the melt pool and its thermal characteristics during the manufacturing process. The proposed simulation reduces computational demands by attempting to capture the most important thermal effects developed during the manufacturing process. Those effects include laser-energy absorption, thermal interaction between adjacent elements and elements within the underneath substrate, thermal convection and radiation, and powder melting.

Abstract This paper presents a computational framework for designing and optimizing custom compression casts/braces. Different from the conventional cast/brace design, our framework generates custom casts/braces with fitness, lightweight, and good ventilation. The computational pipeline is an end-to-end solution, directly from customer to the manufacturer, which starts from a 3D scanned human model represented by mesh and ends with the 3D printed cast/brace. Our interactive tools allows users to define and edit the 3D curves on the mesh surface, and trim the mesh surface to form the cast/brace shape using the curves. These tools are efficient and simple to use, and also they enable designing the custom casts/braces fitting to the given human body. In order to reduce the weight and improve the ventilation, we adopt the topology optimization (TO) method to optimize the cast/brace design. We extend the existing three-dimensional (3D) TO method to the mesh surface by simplifying the optimization problem to a 2D problem. Therefore, the efficiency of the TO computation is improved significantly. After the optimized cast/brace design is obtained on the mesh surface, a solid model is generated by our design interface and then sent to a 3D printer for fabrication. Simulation results show that our method can better re-disturb the stresses compared with the conventional 3D TO.

Abstract Machine learning is opening up new ways of optimizing designs but it requires large data sets for training and verification. While such data sets already exist for financial, sales and business applications, this is not the case for engineering product design data. This paper discusses our efforts in curating a large Computer Aided Design (CAD) data set with desired variety and validity for automotive body structural compositions. Manual creation of 60,000 CAD variants is obviously not viable so we examine several approaches that can be automated with commercial CAD systems such as Parametric Design, Feature Based Design, Design Tables/Catalogs of Variants and Macros. We discuss pros and cons of each method and how we devised a combination of these approaches. This hybrid approach was used in association with DOE tables. Since the geometric configurations and characteristics need to be correlated to performance (structural integrity), the paper also demonstrates automated workflows to perform FEA on CAD models generated. Key simulation results can then be associated with CAD geometry and, for example, processes using machine learning algorithms for both supervised and unsupervised learning. The information obtained from the application of such methods to historical CAD models may help to understand the reasoning behind experiential design decisions. With the increase in computing power and network speed, such datasets together with novel machine learning methods, could assist in generating better designs, which could potentially be obtained by a combination of existing ones, or might provide insights into completely new design concepts meeting or exceeding the performance requirements.

Abstract Improving design in the context of market systems requires an understanding of how consumers learn about and evaluate competing products. Marketing models frequently assume that consumers choose the product with the highest utility, which provides businesses insights into how to design and price their products to maximize profits. While recent research has shown the impacts of consumer interactions within social networks on their purchasing decisions, they typically model market systems using a top-down approach. This paper applies an agent-based modeling approach with social network models to investigate the extent to which word-of-mouth (WOM) communications are influential in changing consumer preferences and producer market performance. Using a random network, we study the effects of the number of referrals for a product and the degrees of similarity between the senders and receivers of referrals on purchase decisions. In addition, the eigenvector centrality metric is used to analyze the spread of WOM referrals. The simulation results show that the most influential consumers in the network can create significant shifts in the market share, and a statistical analysis reveals a significant change in the system-level metrics of interest for the competing firms when WOM recommendations are included. The findings incentivize producers to invest in supporting their product development efforts with rigorous social networks analysis so as to increase their market success.

Abstract Long distance transportation of various fluid commodities like water, oil, natural gas liquids is achieved through a distribution network of pipelines. Many of these pipelines operates unattended in harsh environments. Therefore, pipes are often susceptible to corrosion, leakage, cracking and third party damage leading to economic and resource infrastructure losses. Thus, early detection and prevention of any further losses is very important. Although many pipeline monitoring techniques exist, the majority of them are based on single sensing modality like acoustic, accelerometer, ultrasound, pressure. This makes the existing techniques unreliable, sensitive to noise and costly. This paper describes a methodology to combine accelerometer and acoustic sensors to increase the detection fidelity of pipeline leakages. The sensors are mounted on the pipe wall at multiple locations. Vibrational and acoustic characteristics obtained from these sensors are fused together through wavelet analysis and classified using kernel SVM and Logistic Regression in order to detect small bursts and leaks in the pipe. The simulation results have confirmed the effectiveness of proposed methodology yielding 90% leak detection accuracy.

Abstract Complex engineering design tasks require teams of engineers with different skills and unique knowledge sets to work together to develop a solution. In these contexts, team communication is critical to successful design outcomes. Previous research has identified effective management of communication frequency as an important dimension of team communication leading to improved design outcomes. Organization research literature has demonstrated a curvilinear relationship in which both frequent and infrequent communication may hamper organizational performance. In contrast, recent work in engineering design research has found an inverse relationship between frequency and technical system performance for simple design tasks. This paper extends this work quantifying the impact of communication frequency on technical system performance by examining multi-disciplinary problems. Results from a multi-agent simulation on a six discipline parameter design task for minimizing the weight of a geostationary satellite are presented. Simulation results suggest that the form of relationship between frequency and performance changes significantly depending on the communication pattern. The evidence suggests that for the same design task a planned periodic communication pattern results in a curvilinear relationship, whereas for a stochastic communication pattern a less pronounced monotonic inverse relationship is found.

Abstract Bulletproof or high-class protected VIP vehicles are widely produced based on particular types of OEM ( Original Equipment Manufacturer ) commercial vehicles. The arising implications of increasing the level of protection of such vehicles have reflected several negative consequences on the vehicle directional stability. This is obviously true, since adding extra armored plates will increase both gross vehicle weight and the height of its center of gravity. Additionally, such vehicles are normally driven at higher speeds during critical evasive maneuvers. The proper selection of suitable suspension system in terms of spring, shock absorber and anti-roll bar for front and rear axles will contribute to the overall vehicle stability. This paper presents both theoretical and experimental investigation to upgrade the suspension system of a particular bulletproof vehicle in order to improve its stability during high-speed cornering maneuvers. For this purpose, six kits of nominated suspension systems have been tested in order to measure their stiffness and damping characteristics. Furthermore, for each suspension kit, the considered bulletproofed vehicle is fully assessed in CarSim ® simulation environment. In-order to check the gained simulation results, typical field tests of the fully instrumented vehicle along with the modified suspension kit. From both simulation and field tests, a suitable kit for front and rear suspension system as well as braking system has been recommended not only to carry the additional weight of the heavy armored body but also to maintain the vehicle stability within their allowable limits.

Abstract A novel structure for capacitive MEMS pressure sensors is presented that can be used for a wide range of pressure sensing applications. The sensor is designed such that its characteristic capacitance-pressure ( C-P ) response is highly linear and could cover a wide range of working pressure. A capacitive pressure sensor includes two capacitive electrodes, one patterned on the substrate and the other one suspended creating a sealed cavity. The suspended electrode acts as the pressure sensitive membrane in the device and undergoes out-of-plane deformation when there is a change in ambient pressure, resulting in a change in the device’s capacitance. The design presented in this work uses a wavy-shape membrane with controlled deformations to provide a highly linear C-P response. The wavy shape of the membrane can be fabricated using grey-scale mask and lithography. ANSYS APDL multiphysics solver is used to model and simulate the pressure sensor and optimize its response. The material used in the design and simulations of the pressure sensor is silicon carbide making this design suitable for harsh environment applications. The simulation results show that if the size and the shape of the wave form in the membrane are optimized, highly linear C-P response can be achieved and also its working pressure range can be extended.

Abstract Uncertainties of a robot manipulator include the inaccurate transformation between each coordinate, (e.g., robot base, flange, camera, tool, and workpiece) and the inherent variations within each links and mechanical components. In this work we consider the impact of harmonic drive uncertainty in peg-in-hole assembly. We investigate the accumulations of transmission errors within robot dynamic and nonlinear process in rotation. Models of manipulators with uncertainty parameters in harmonic drive systems are developed. The parameters in our harmonic drive model with uncertainty are identified through testing. The transformation metrics between each coordinate are also determined by detecting fiducial pattern and marker with well-calibrated camera. As a result we have a virtual robot model with parameters and uncertainties much closer to the real system. The simulation results show that the accuracy of hole-searching can reach 0.50 mm position error and 0.16 degree orientation error for hole locations that are not known as a priori.

Abstract In this paper, quasi-static out-of-plane compression behaviors of Miura-ori patterned sheets were investigated numerically by using finite element analysis (FEA). The simulation results show a reasonable agreement with the experimental results. In addition, the parametric analysis of the Miura-ori patterned sheets with different cell wall thicknesses, side lengths, dihedral angles and sector angles were carried out using FEA method. The influences of different parameters on the peak force and mean force were determined.

Abstract This paper aims to develop a new computationally efficient method for the dynamic modelling of a Planar Parallel Manipulator (PPM) based on the Discrete Time Transfer Matrix Method (DT-TMM). In this preliminary work, we use a 3-PRR PPM as a study case to demonstrate the major procedures and principles of employing the DT-TMM for the dynamic modelling of a PPM. The major focus of this work is to present the basic principles of the DT-TMM for the dynamic modelling of a PPM: decomposing the whole parallel manipulator to the individual components, establishing the dynamics of each component/link, linearizing the component/element dynamics to obtain the transfer matrix of each component/link, and assembling the component dynamics into the system dynamics of the PPM using the transfer matrices of all components/elements. To make the work more readable, the brief introduction of the inverse kinematics and the inverse dynamics is also included. The numerical simulations are conducted based on the 3-PRR PPM with rigid links in this preliminary research effort. The simulation results are compared with those from the model using the principle virtual work method and ADAMS software. The numerical simulation results and comparison demonstrate the effectiveness of the dynamic modelling method using DT-TMM for the PPM.

Abstract In this paper, a fractional order fuzzy proportional-integral plus differential (FOFPI+D) controller is presented for nonlinear vehicle semi-active suspension system (SAS). The control goal is to meliorate the ride quality level by minimizing the root mean square of vehicle body vertical acceleration (RMSVBVA) and maintaining suspension travel. The FOFPI+D controller is realized using non-integer differentiator operator in fuzzy proportional integral (FPI) controller plus the derivative (D) action with additional fractional differentiator. A dynamical model of four degrees–of–freedom vehicle suspension system incorporating magnetorheological dampers (MRD’s) is derived and simulated using Matlab/Simulink software. The performance of the semi-active suspension system using FOFPI+D controller is compared to MR-passive suspension system. The simulation results prove that semi-active suspension system controlled using FOFPI+D outperform and offer better comfort ride under road profiles such as random and bump.

Abstract Many researchers have proved the potential of autoparametric system in controlling stability and parametric resonance. In this paper, two different designs for auto-parametrically excited mass-spring-damper systems were studied: one system was controlled by rotational motion of the spring, and the other system was controlled by sliding motion of the spring. The theoretical models were developed to predict the behavior of the systems and also generated stability charts to analyze the systems. For each system, the numerical results from both the nonlinear equation and linearized equation were analyzed and compared. Simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results displayed that the implementation of autoparametric system could help reduce the resonance by up to 33% and amplify the resonance by up to 34%.

Abstract Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.

Abstract Magnetorheological elastomer (MRE) is a new kind of smart materials whose mechanical properties can be controlled under external magnetic field and it is mainly consist of matrix materials and magnetic particles. In this work, the natural rubber (NR)/polybutadiene rubber (BR) hybrid matrix based MRE were prepared and the compatibility of NR and BR were studied. The hybrid matrix was prepared by physical mixing method. The characterization results showed that the BR had excellent compatibility with NR. The measurement result using rheological showed that the MR effect can be increased to 44.19% by adding of BR. The dynamic thermomechanical analysis showed that the hybrid matrix formed a homogeneous system when the ratio of BR and NR is 1/9 and 3/7. The particles was mixed with matrix using physical technology. The process of mixing was analyzed by numerical simulation. The simulation result showed that the increase of diameter of particles would increase the temperature and velocity of matrix in mixing. The particles was distributed evenly at enough mixing time and the mixing time was decreased with the diameter of particles.

Abstract Parameter identification as known as a significant issue is investigated in this paper. The research focus on online identifying unknown parameters of uncertain fractional-order chaotic and hyperchaotic systems, which shows great potential in recent applications. Up to now, most of the existing online identification methods only focus on integer-order systems, thus, it’s necessary to expand these fundamental results to uncertain fractional-order nonlinear dynamic systems and adopt an effective optimizer to deal with the model uncertainties. Motivated by this consideration, this research introduces an efficient optimizer to offline and online parameter identification of the fractional-order chaotic and hyperchaotic systems through non-Lyapunov way. For problem formulation, a multi-dimensional optimization problem is converted into from the problem of parameter identification, where both systematic parameters and fractional derivative orders are considered as independent unknown parameters to be estimated. The experimental results illustrate that SHADE is significantly superior to the other compared approaches. In this case, online identification is conducted via SHADE, the simulation results further indicate that success-history based adaptive differential evolution (SHADE) algorithm is capable of detecting and determining the variations of parameters in uncertain fractional-order chaotic and hyperchaotic systems, and also is supposed to be a successful and potentially promising method for handling the online identification problems with high efficiency and effectiveness.

Abstract This paper presents an optimal motion control scheme for a mechatronic actuator based on a dielectric elastomer membrane transducer. The optimal control problem is formulated such that a desired position set-point is reached with minimum amount of driving energy, characterized via an accurate physical model of the device. Since the considered actuator is strongly nonlinear, an approximated approach is required to practically address the design of the control system. In this work, an Adaptive Dynamic Programming based algorithm is proposed, capable of minimizing a cost function related to the energy consumption of the considered system. Simulation results are presented in order to assess the effectiveness of the proposed method, for different set-point regulation scenarios.

Abstract The research hereby introduces a novel approach to reduce tooth bending stress using a parametric numeric simulation. This Finite Element Method (FEM) is used to determine optimal design variables for an asymmetric root profile of a helical gear defined by a rational cubic Bezier curve. The gear is first modelled using a machine design software and later implemented into a 3D computer aided design (CAD) package to modify the root spline geometry using a script. A nonlinear relationship exists between the design variables and tooth bending stress. Additionally, certain trends exist between the design variables to exhibit a more optimal root profile. The simulation results show that the proposed method is feasible as the general optimization process results in significant bending stress reduction. The numerical simulation demonstrates that bending stress can be reduced by as much as 10.75% by the proposed approach.

Abstract The DCTs have increased in prevalence for achieving power uninterrupted shifting and pre-shift process significantly influences the DCTs shift quality. Research on the multistage and nonlinear characteristics of the gear preselect process is not comprehensive so that there are shortcomings such as high impact, long synchronization time and poor economy. In view of above detrimental phenomenon, control parameters optimization is conducted in order to realize fast, smooth and economic pre-shifting on the basis of analyzing the sensitivity of the factors affecting pre-shift process. Considering the engagement of sleeve, synchro ring and dog gear, the multi-body dynamics theory is applied to establish an accurate synchronizer dynamics model. Based on the model, simulations are conducted to confirm factors like sleeve mass, cone angle fluctuating the pre-shift quickness and smoothness, sorting structure parameters according to factors sensitivity. Furthermore, the formula of energy loss characteristics relating to two control parameters which are pre-shift force and pre-shift trigger time is obtained, deriving from exploring the hydraulic loss caused by pre-shift force and the drag torque energy loss created by pre-shift trigger time. The optimal synchronizer structure parameters are obtained by adopting multi-objective optimization method. Simulation results indicate the optimal control parameters improve pre-shift comprehensive performance including quickness, smoothness and economy compared with conventional scheme.

Abstract In order to improve the fuel economy of vehicles equipped with a dual clutch transmission, this paper proposes a real-time gearshift schedule optimization method based on dynamic programming (DP) and future vehicle speed prediction. The global condition information is necessary for DP algorithm, which makes it difficult to be applied to the real-time control of vehicles. Therefore, BP neural network optimized by genetic algorithm (GA-BP) is utilized to predict future speed information in the research, and the results of speed prediction are introduced into DP problem solving process to realize real-time application of DP optimization in gear decision-making. Simulation results on a fuel vehicle with seven-speed dual clutch transmission using different gear decision-making methods under multiple driving cycles are presented. The results indicate that compared with the case of an empirical economy gearshift strategy, additional fuel can be saved. Furthermore, computational effort for the proposed method is little enough, which guarantees the real-time performance of DP gearshift schedule optimization.