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The purpose of this paper is to describe a novel design method to construct lattice structure computational models composed of a set of unit cells including simple cubic, body-centered cubic, face-centered cubic, diamond cubic and octet cubic unit cell.

In this paper, the authors introduce a new implicit design algorithm based on the computation of volumetric distance field (VDF). All the geometric components including lattice core structure and outer skin are represented with VDFs in a given design domain. This enables computationally efficient design of a computational model for an arbitrarily complex lattice structure. In addition, the authors propose a hybrid method based on the VDF and parametric solid models to construct a conformal lattice structure, which is oriented in accordance with the geometric form of the exterior surface. This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

Experimental results are shown for a variety of geometries to validate the proposed design method along with illustrative several lattice structure prototypes built by additive manufacturing techniques.

This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

The paper aims to extend the knowledge base for design and optimization of Star grain which is well known for its simplicity, reliability and efficiency. Star grain configuration is considered to be among the extensively used configurations for the past 60 years. The unexplored areas of treatment of ballistic constraints, non-neutral trace and freedom from use of generalized design equations and sensitivity analysis of optimum design point are treated in detail to bridge the gap. The foremost purpose is to expand the design domain by considering entire convex Star family under both neutral and non-neutral conditions.

This research effort optimizes Star grain configuration for use in Solid Rocket Motors with ballistic objective function (effective total impulse) and parametric modelling of the entire convex Star grain family using solid modelling module. Internal ballistics calculations are performed using equilibrium pressure method. Optimization process consists of Latinized hypercube generated initial population and Swarm Intelligence optimizer’s ability to search design space. Candidate solutions are passed to solid modelling module to simulate the burning process. Optimal design points, critical geometrical and important ballistic parameters (throat diameter, burn rate, characteristic velocity and propellant density) are then tested for sensitivities through Monte Carlo simulation.

The proposed approach takes the design of Star grain configuration to a new level with introduction of parametric modelling and sensitivity analysis, thus, offering practical optimum design points for use in various mission scenarios. The proposed design and optimization process provides essential data sets which can be useful prior to the production of large number of solid rocket motors. Results also advocate the adequacy of design from engineering perspective and practicality.

Results showed that few design parameters are sensitive to uncertainties. These uncertainties can be investigated in future by a robust design method.

Monte Carlo simulation can prove to be vital considering the production of a large number of motor units and enlightens the necessity to obtain statistical data during manufacturing.

This paper fulfils long-sought requirement on getting free from use of generalized set of equations for commonly used Star grain configurations.

The purpose of this paper is to propose a new way of prototyping surfaces, taking the mathematical background into account, without involving drawing environments.

The authors thicken surfaces from a mathematical point of view to obtain solids. Next they look for an operative procedure to build virtual models and interchange files. The authors build a sample of Enneper thickened surface by fused deposition modelling and verify the prototype by reverse engineering techniques.

The authors provide a formulation able to thicken surfaces in mathematical terms. An operative procedure generates virtual solids and interchange files in the same environment. The approximations necessary for additive fabrication, such as triangulations and mesh geometry, can be chosen at this stage.

The approach is useful at the product/process development stage, in which surfaces are delivered by theoretical analysis. At this stage a prototype can give useful advice permitting functional tests. The limitation is that, when the mathematical formulation is not available, it is difficult to translate a concept without fundamentals of differential geometry.

Approximations of drawing environments typically lead to fault models, not ready for fabrication by additive manufacturing (AM) technologies, needing empiric, not at all obvious and not rapid repair interventions. The authors' approach eliminates this stage, permitting a faster and simple managing of modifications due to functional and technological requirements, that are frequent at concept stage. This leads to a time‐to‐market reduction in the course of product/process development.

This paper extends the capability of a mathematical approach to solve surface prototyping problems. By reducing the required stages, the proposed methodology finds a theoretical and practical shorter route to direct fabrication.

This paper sets out to present the weight optimization of sampling drill‐bits which is designed for European Space Agency – Beagle 2 Mars Express mission.

In this study, two techniques, finite element analysis and numerical optimization, are employed to find out the optimal solution within the various constraints.

The result shows that the total weight of sampling drill‐bits was reduced over 76.7 percent.

The proposed methodology can also be applied in different engineering design applications and provides a total integrated solution as a generic template for design engineers.

Utilizing renewable energy and developing new energy sources are practical responses to the shortage of fossil fuels and environmental regulations for carbon dioxide emissions. The purpose of this paper is to assess the practicability of using low heating value (LHV) fuel on an annular miniature gas turbine (MGT) via numerical simulations.

The MGT used in this study is MW‐44 Mark I, whose original fuel is liquid (Jet A1). Its fuel supply system is re‐designed to use biogas fuel with LHV. The simulations, aided by the commercial code CFD‐ACE+, were carried out to investigate the cooling effect in a perforated combustion chamber and combustion behavior in an annular MGT when using LHV gas. In this study, four parameters of rotational speeds are considered. At each specific speed, various mixture ratios of methane (CH₄) to carbon dioxide (CO₂) including 90, 80, 70, and 60 percent were taken into consideration as simulated LHV fuels.

The simulation results show the chamber design can create a proper recirculation zone to concentrate the flame at the center of the chamber, and prevent the flame from expanding to cause hot spot. Furthermore, the hot gas exhausted from combustor outlet is cooled down effectively by jet flow discharged from dilution holes, which prevent turbine blade from heat damage.

Simulation results demonstrate that CFD‐ACE+ can simulate flow field performance and combustion behavior in an annular MGT precisely. The results of these CFD analyses confirm that the methane fuel can be used in such small volume of MGT and still have high performance. With the aid of the constructed combustor model, the performance of a methane‐used MGT can be realized before the experiment procedure starts.

The purpose of this paper is to develop an automatic disassembly navigation approach for human interactions in the virtual environment to achieve accurate and effective virtual assembly path planning (VAPP).

First, to avoid the error-prone human interactions, a constraint-based disassembly method is presented. Second, to automatically provide the next operable part(s), a disassembly navigation mechanism is adopted. Finally, the accurate assembly path planning can be obtained effectively and automatically by inversing the ordered accurate disassembly paths, which are obtained interactively in the virtual environment aided with the disassembly navigation matrix.

The applications present that our approach can effectively avoid the error-prone interactive results and generate accurate and effective VAPP.

There are several works that could be conducted to make our approach more general in the future: to further study the basic disassembly direction deducing rules to make the process of determining disassembly direction totally automatic, to consider the hierarchy of the parts in virtual reality system and to consider the space for assembly/disassembly tools or operators.

The approach has the following characteristics: a new approach to avoid the error-prone human interactions for accurate assembly path planning obtaining, a new constraint deducing method for determining the disassembly semantics automatically or semi-automatically is put forward and a new method for automatically identifying operable parts in VAPP is set forward.

Additive Manufacturing (AM) conventionally necessitates an intermediary slicing procedure using the standard tessellation language (STL) data, which can be computationally burdensome, especially for intricate microcellular architectures. This study aims to propose a direct slicing method tailored for digital light processing-type AM processes for the efficient generation of slicing data for microcellular structures.

The authors proposed a direct slicing method designed for microcellular structures, encompassing micro-lattice and triply periodic minimal surface (TPMS) structures. The sliced data of these structures were represented mathematically and then convert into 2D monochromatic images, bypassing the time-consuming slicing procedures required by 3D STL data. The efficiency of the proposed method was validated through data preparations for lattice-based nasopharyngeal swabs and TPMS-based ellipsoid components. Furthermore, its adaptability was highlighted by incorporating 2D images of additional features, eliminating the requirement for complex 3D Boolean operations.

The direct slicing method offered significant benefits upon implementation for microcellular structures. For lattice-based nasopharyngeal swabs, it reduced data size by a factor of 1/300 and data preparation time by a factor of 1/8. Similarly, for TPMS-based ellipsoid components, it reduced data size by a factor of 1/60 and preparation time by a factor of 1/16.

The direct slicing method allows for bypasses the computational burdens associated with traditional indirect slicing from 3D STL data, by directly translating complex cellular structures into 2D sliced images. This method not only reduces data volume and processing time significantly but also demonstrates the versatility of sliced data preparation by integrating supplementary features using 2D operations.

The purpose of this paper is to investigate the mechanical, kinematic and biological aspects that would be required for a customized upper limb exoskeleton prototype operation.

The research contained a literature survey, design, simulation, development and testing of an exoskeleton arm.

An adjustable/customizable exoskeleton arm was developed with a kinematic model to allow the desired motion. Tests were performed to determine the feasibility of the system.

The paper shows how the authors researched, designed and developed an exoskeleton arm that had similar mechanical properties to those of a biological arm. The exoskeleton must allow customization and be adaptable to the operator, without the need for major alterations.

This paper aims to propose a framework for optimizing the pose in the assembly process of the non-ideal parts considering the manufacturing deviations and contact deformations. Furthermore, the accuracy of the method would be verified by comparing it with the other conventional methods for calculating the optimal assembly pose.

First, the surface morphology of the parts with manufacturing deviations would be modeled to obtain the skin model shapes that can characterize the specific geometric features of the part. The model can provide the basis for the subsequent contact deformation analysis. Second, the simulated non-nominal components are discretized into point cloud data, and the spatial position of the feature points is corrected. Furthermore, the evaluation index to measure the assembly quality has been established, which integrates the contact deformations and the spatial relationship of the non-nominal parts’ key feature points. Third, the improved particle swarm optimization (PSO) algorithm combined with the finite element method is applied to the process of solving the optimal pose of the assembly, and further deformation calculations are conducted based on interference detection. Finally, the feasibility of the optimal pose prediction method is verified by a case.

The proposed method has been well suited to solve the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the effectiveness of the method with an example of the shaft-hole assembly.

The method proposed in this paper has been well suited to the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the method with an example of the shaft-hole assembly.

The different surface morphology influenced by manufacturing deviations will lead to the various contact behaviors of the mating surfaces. The assembly problem for the components with complex geometry is usually accompanied by deformation due to the loading during the contact process, which may further affect the accuracy of the assembly. Traditional approaches often use worst-case methods such as tolerance offsets to analyze and optimize the assembly pose. In this paper, it is able to characterize the specific parts in detail by introducing the skin model shapes represented with the point cloud data. The dynamic changes in the parts' contact during the fitting process are also considered. Using the PSO method that takes into account the contact deformations improve the accuracy by 60.7% over the original method that uses geometric alignment alone. Moreover, it can optimize the range control of the contact to the maximum extent to prevent excessive deformations.