Design of a Parallel Gripper Based on Topology Synthesis and Evolutionary Optimization

Random variable of thermo-mechanical coupling (TMC) structure is analyzed first, then the topology optimization model of TMC compliant mechanism is established. According to the uncertainty factors such as temperature, load, volume ratio and thermal expansion coefficient in the process of topology optimization, the robust reliability optimal design method is introduced into the topology optimization process. The various factors on the sensitivity of topology optimization process is analyzed, the best match between various factors is find out, which make the product has robust reliability for the change of the uncertainty. The residual analysis for the most sensitive factors is carried out. Finally the correctness and validity of the method is verified with an example. Introduction When we study the compliant mechanisms by adopting topological optimization method, we only need to give design domain and allocate input and output position, no need from the perspective of a known rigidity. The mechanism come from this method have a input and output relationship of optimized “force—displacement”, thus it attracts people’s attention. Topology optimization research is developing from certain topology optimization direction to reliable topology optimization direction. Jinqing Zhan proposed a reliable topology optimization method of compliant mechanism based on infrastructure method. KHARMANDA proposed a reliable topology optimization method. Zhaokun Li proposed a geometric nonlinear topology optimization research method based on reliable compliant mechanism. Their research turned out that the mechanism based on reliable topology optimization is more reasonable than the mechanism based on certain topology optimization. Yongcong Kuang build a level set function to design variable, stiffness minimization, and proposed corresponding optimization algorithm. Mingtao Cui established a monolithic compliant mechanism multi-target reliable topology optimization design dual mathematical model. This paper combines topology optimization process of compliant mechanism and robust reliability optimal design, analyzes the sensitivity of uncertainty random variables such as design domain’s size, external load and temperature in topology optimization process. Then get the best combination among these random variables. Random Variable Coupling Field Analysis Hot objects will produce thermal expansion, thus caused by the thermal deformation and displacement of combined action of mechanical load and temperature load structure called a hot solid coupling structure, the finite element balance equation :

Linkage synthesis: Topology selection fixed by dimensional constraints, study of an example

Larger deformation and higher bearing capacity is a worthwhile goal for designing the compliant mechanism. By virtue of the anisotropy, variable stiffness and superior designability, fiber reinforced composite material is effective way to solve the contradictory problem. There are several reports to design compliant mechanism with composites and improve its performances. But adding directly stress constraints in topology optimization process is very complex and difficult to establish the explicit expression of stress constraints for compliant mechanism with composites. This paper investigated the topology optimization and performance prediction of compliant mechanism with composites by neural network. Topology configurations, output displacements and maximal stress in matrix materials of compliant mechanism under different fiber laying angles were firstly implemented. Subsequently, neutral network models were built to predict the output displacement and maximal stress. Examples of compliant inverter and gripper were used to verify the proposed method.

A threshold accelerometer based on a tristable mechanism

The quality of the design has been recognized as one of the key factors to the success of the engineering project. However, engineers need to repeatedly perform design tasks, such as processing information, to produce an appropriate design result. Such a design process involves many activities and lead into one or several iterative loops. Consequently, the sequence of performing design activities should be properly planned to avoid unnecessary loops. Design Structure Matrix (DSM) is one of the widely used methods to describe the design process. By employing DSM, the relationships and the sequence of the design activities can be identified. In this research, several principles of evaluating the design process are developed. In addition, fast messy Genetic Algorithm (fmGA) is employed along with the proposed evaluation principles and DSM to determine the optimal design process. Results show, based on the proposed evaluation principles, the impact of the iteration loops can be clearly identified. Furthermore, with the proposed optimization model and its computer implementation, engineers can find the optimal design process for the large-scale design project efficiently and effectively. the proposed evaluation principles and DSM to determine the optimal design process. Results show, based on the proposed evaluation principles, the impact of the iteration loops can be clearly identified. Furthermore, with the proposed optimization model and its computer implementation, engineers can find the optimal design process for the large-scale design project efficiently and effectively.

Earthquake protection of structures with nonlinear viscous dampers optimized through an energy-based stochastic approach

The process of sheet metal forming is characterized by various process parameters. Accurate prediction of springback is essential for the design of tools used in sheet metal forming operations. In this paper, an evolutionary algorithm is presented that is capable of handling single/multiobjective, unconstrained and constrained formulations of optimal process design problems. To illustrate the use of the algorithm, a relatively simple springback minimization problem (hemispherical cup-drawing) is solved in this paper, and complete formulations of the algorithm are provided to deal with the constraints and multiple objectives. The algorithm is capable of generating multiple optimal solutions in a single run. The evolutionary algorithm is combined with the finite element method for springback computation, in order to arrive at the set of optimal process parameters. To reduce the computational time required by the evolutionary algorithm due to actual springback computations via the finite element method, a neural network model is developed and integrated within the evolutionary algorithm as an approximator. The results clearly show the viability of the use of the evolutionary algorithm and the use of approximators to derive optimal process parameters for metal forming operations.

Electro-thermal-compliant mechanisms are mechanisms onto which electric voltage is applied as input. This mechanism is based on the thermal expansion properties of the materials. This study considered all of the convection effects according to layouts produced after each topology optimization process. A new material mixing method based on bi-directional evolutionary structural optimization (BESO) was suggested to obtain optimal topologies for compliant mechanisms composed of two materials. Reliability-based topology optimization (RBTO) based on BESO was also applied to the topology optimizations of the mechanisms. Performance measurement approach (PMA), which estimates has probabilistic constraints that are formulated in terms of the reliability index, was adopted to evaluate the probabilistic constraints. The procedure has been tested in numerical applications, and the results were compared with those obtained by the level set and solid isotropic material with penalization (SIMP) methods for validation of the proposed approaches.

Transfer moulding is the most common process for the encapsulation of electronic packages in semiconductor manufacturing. Quality of the moulding is affected by a large number of mould design parameters and process parameters. Currently, the parameters setting is performed by experienced engineers in a trial and error manner and often the optimal setting can not be obtained. In the face of global competition, the current practice is inadequate. In this research, a process optimisation system for transfer moulding of electronic packages is described which involves design of experiments (DOE) techniques, artificial neural networks (ANNs), multiple regression analysis and the minimax method. The system is aimed to determine the optimal mould design parameters and process parameter settings of transfer moulding of electronic packages for multiobjective problem. Implementation of the optimisation system has demonstrated that the time for the determination of optimal mould design parameters and process parameters setting can be greatly reduced and the parameters setting recommended by the system can contribute to the good quality of moulded packages without relying on experienced engineers.

A methodology for distributed knowledge modeling will be presented in this article, in order to contribute to a better decision making during design problems analysis. An optimal design process refers to setting up coherent numerical models and it requires a well structured problem definition. However, a lack of downstream information in early stages of product development leads to a complicated elicitation task that became an issue due to nowadays distributed environments. A Multi-Agent approach is proposed to support the distributed knowledge elicitation process. A set of agents will guide members from the distributed design team, throughout the product life cycle, to extract relevant information and analyze it. The interaction among agents will highlight potential incoherencies during the modeling process, in order to enable partners to avoid inconsistent information. A coherent knowledge base is then constructed and ready to be used to create models to be analyzed by traditional inference engines such as optimization solvers, constraint satisfaction programming, etc.

In this paper, an optimal design process for high-rise building structures is presented based on cost-effectiveness criterion. Then, an optimal design of a complex high-rise building based on cost-effectiveness criterion is carried out by using the design process. A minimum life cycle cost and corresponding elastic inter story drift considering initial cost and damage cost as two separate objective functions are obtained by using genetic algorithm. Finally, fitting curves of life cycle cost, initial cost, damage cost and corresponding performance indexes are interpolated by using one-dimensional linear interpolation considering elastic inter story drift ratio as control performance indexes. And lots of life cycle cost designs and corresponding performance indexes of the separated objective function are given. The results show that the optimal design process is practical and optimal results are diverse, and can be chosen flexibly by owners.

Recently, customers are demanding for hot rolled strip products to have tight oxide scales on the surfaces. Therefore, high finishing rolling temperature, low coiling temperature and fast finishing rolling speed have to be used to obtain tight oxide scale, which is different from conventional controlled rolling. In order to ensure the mechanical properties at the same time, a framework consisting of the Bayesian neural network and multi-objective particle swarm optimization has been established to determine the optimal hot strip rolling parameters. Due to excellent generalization ability, the Bayesian neural network was employed to develop the model for the prediction of mechanical properties of hot rolled automotive beam steels. The accuracy between the measured and predicted values was within ±30 MPa and ±4% for strength and elongation, respectively, providing a reliable model for the optimal process design. By applying multi-objective particle swarm optimization, the optimized hot rolling process was obtained for the production of hot rolled automotive beam steel with “Tight Oxide Scale”. Industrial trials have been carried out, which showed good agreement with the optimized hot strip rolling processes. It has been theoretically and practically proven that the optimal process design framework can effectively locate the optimal processing window for hot strip rolling.

A dynamic industrial design optimization requires high-quality optimization algorithms as well as adaptive representations to find the global solution for a given problem. For adapting the representation to changing environments or to new input we utilize the concept of evolvability, which in our interpretation consists of three criteria: variability, regularity, and improvement potential, where regularity and improvement potential characterize conflicting goals between exploration and exploitation. Our goal is the efficient adaptation of the representation according to a given preference weight between regularity and improvement potential. We propose a combination of two heuristics, Lloyd sampling and orthogonal least squares sampling, to initialize the adaptation process for a given preference weight. We show that this initialization improves the convergence speed of the adaptation process as well as the resulting fitness. We then realize a stepwise design optimization procedure by alternating the adaptation of the representation with optimization of the design. During the design optimization process we extract information which we exploit in the next adaptation phase. We show that an intermediate preference weight, balancing between regularity and improvement potential, allows to exploit this information and is robust to erroneous initial information. Thereby, we increase the performance of the whole design optimization process.

Recent advancement in medical engineering demands a prefect solution for various problems. In this work, we proposed compliant forceps with the novel serpentine flexure. Compliant mechanisms are flexible mechanisms which are jointless in nature, which transfer force and displacement from input link to output link through an elastic body deformation. The compliant mechanism provides an optimum solution for many micro applications, which is suitable for solving many problems in medical engineering. Forceps require a limited level of parallel movement to hold the required amount of tissues. In traditional forceps, ‘U’ shaped flexure restricts the parallel motion. Hence, this work the proposed Forceps uses serpentine flexure to overcome the parallel motion problems. Here, a parallel Serpentine flexure-based forceps is developed. The mathematical modelling for serpentine flexures is developed. A generic equation is been developed to estimate the stiffness of the serpentine flexure model, stiffness is estimated for two different types of flexure model. The finite element model (FEM) of serpentine flexures is developed using ANSYS and stiffness is validated. Mathematical and FEM results are promising with minimum variations. Finally, forceps are developed using leverage mechanism concepts with the serpentine flexure hinges and FEM is performed. FEM results of forceps are promising.

The core design elements of the motor for the co-robot joint are miniaturization and high torque. In this paper, a dual rotor axial-flux permanent magnet (DRAFPM) motor is proposed to improve the performance of robot joints. DRAFPM motors have the advantage of reducing iron loss and minimizing volume because they can remove stator yoke. When designing a motor with the same volume, it can be designed to increase the height of the fixed ruler by the thickness of the yoke of the fixed ruler and increase the number of turns. In the case of existing axial-flux permanent magnet (AFPM) motors, the shape of the three-dimensional structure is limited by radial laminating of stator. Therefore, considering the production of 3D printing, the shape of stator shoe is designed. The optimal design problem of DRAFPM motor consists of real and integer design variables. In addition, due to the structural characteristics of DRAFPM motors, 3D finite element analysis (FEA) is required, so it takes a long time to interpret. Therefore, this paper proposes an efficient optimal design process to optimize the remaining real design variables after prioritizing the integer design variables. The proposed optimization process is applied to the DRAFPM motor for robot joints, and the optimal design plan satisfying the design function is derived from various design variables to prove the validity of the optimization process.