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This paper shows how the evolutionary structural optimization (ESO) algorithm can be used to achieve a multiple criterion design for a structure in a thermal environment. The proposed thermal ESO procedure couples an evolutionary iterative process of a finite element heat conduction solution and a finite element thermoelastic solution. The overall efficiency of material usage is measured in terms of the combination of thermal stress levels and heat flux densities by using a combination strategy with weighting factors. The ESO method then works by eliminating from the structural domain under‐utilized material. In this paper, a practical design example of a printed circuit board substrate is presented to illustrate the capabilities of the ESO algorithm for thermal design optimization in multiple load environments.

The purpose of this paper is to introduce an evolutionary heuristic method for simultaneous optimization of topology and material property distribution of functionally graded (FG) structures under a prescribed loading condition.

The proposed procedure is inspired by heuristic nature of bi-directional evolutionary structural optimization (BESO) and genetic algorithm (GA). The optimization algorithm is developed in the context of minimum compliance (maximum stiffness) design problem. The problem is modeled by means of finite element method (FEM). The element-wise material volume fractions and elements’ status (i.e. existence or nonexistence in FE model) are introduced as design variables. After FE analysis, sensitivities are obtain and filtered according to BESO. Having determined sensitivities, by means of a heuristic scheme combined by GA, topology and material property distribution for the next cycle of optimization are determined by updating design variables.

The adopted method has been tested by means of several examples previously reported in literature. The comparison showed the superiority of the proposed method against its rival in terms of relative reduction in compliance, smoother material property distribution and computational cost.

The value of the described method lies in its simple (yet efficient) nature. In contrast with its only rival in literature which more relied on mathematical approach, proposed method uses a series of logic-based heuristic ideas to drive reasonable solutions but with far less computational cost.

This study presents a lifecycle cost model considering multi-level burn-in for operationally unrepairable systems including assembly and warranty costs. A numerical method to obtain system reliability under component replacement during burn-in is also presented with derived error bounds.

The final system reliability after component and system burn-in is obtained and warranty costs are computed. On failure during operation, the system is replaced with another that undergoes an identical burn-in procedure. Cost behaviours for a small and large system are shown in a numerical example.

There are more cost savings when system burn-in is conducted for a large system whereas a strategy focusing on component burn-in only can also result in cost savings for small systems. In addition, a minimum system burn-in duration is required before cost savings are achieved for these operationally unrepairable systems.

The operationally unrepairable system is a niche class of systems which small satellites fall under and no such study has been conducted before. The authors present a different approach towards the testing of small satellites for a constellation mission.

The purpose of this paper is to present a constraint and corresponding algorithm enhancing the evolutionary structural optimization (ESO) method, aiming to circumvent its structure break down problem in some special cases, such as the tie-beam problem.

A virtual soft material introduced to an element will change the stiffness of the element and may consequently change the stress distribution of that element and its neighbors. With this property, the virtual stiffness of the selected element is calculated and the threshold of the stress changes is derived. The stress threshold is used to evaluate the role of an element on the load path and therefore decide the contribution of the element to the structure. Adding this checking operation into the original ESO iterations enables validation of element removal.

The reason for structure break down with the ESO method is that the element removal criterion of ESO only works for certain optimal objectives. It cannot guarantee that the structure does not fail. The proposed operation offers a stronger and stricter constraint condition for ESO’s element removal process, preventing the structure from breaking down in some special cases.

The tests on several examples reported in the literature show that the proposed method has the same ability to achieve an optimum solution as the original ESO methods do, while avoiding incorrect deletion of structurally important elements. The benchmark tie-beam problem is solved successfully with this algorithm. The method can be used in other situations as well.

In continuation of the recent development of Evolutionary Structural Optimisation (ESO) applied to the simultaneous objective to maximise the natural frequency and to minimise the mean compliance, presents the Multicriteria ESO optimisation of two new criteria. This has been done with the use of four different multicriteria methods. Three examples have been used to verify the usefulness and capability of these methods applied to ESO in the context of the aforementioned criteria. Concluded that the ESO weighting method is proficient in presenting the designer with a range of options (of Pareto attribute) taking into account multiple criteria, and the global criterion method has the tendency to produce shapes and topologies that resemble that of the weighted 50 per cent: 50 per cent method. Likewise, the logical OR operator method produced designs that corresponded directly to those of 100 per cent stiffness weighted criteria. No clear resemblance could be concluded with the case of the logical AND operator method.

When objective decisions are to be made, statistical methods should be used based on any objective information in the form of data collected about a product or process. Statistical techniques such as control charts, process capability indices and design of experiments have been used in the manufacturing industry for many years. There are a number of practical and managerial issues related to the application of statistical techniques in studies aimed at improving process and product quality. This paper is a summary of the thoughts and discussions from a recent Internet conference on this issue. Statistical process control techniques and their role in process improvement are first discussed and some issues related to the interpretation and use of experimental design techniques are also summarised. The focus will be on continuous quality improvement using statistical techniques.

The structural optimization presented in this paper is based on an evolutionary procedure, developed recently, in which the low stressed part of a structure is removed from the structure step‐by‐step until an optimal design is obtained. Various tests have shown the effectiveness of this evolutionary procedure. The purpose of this paper is to present applications of such an evolutionary procedure to the optimal design of structures with multiple load cases or with a traffic (moving) load.

Most engineering products contain more than one component or structural element, a consideration that needs to be appreciated during the design process and beyond, to manufacturing, transportation, storage and maintenance. The allocation and design of component interconnections (such as bolts, rivets, or springs, spot‐welds, adhesives, others) usually play a crucial role in the design of the entire multi‐component system. This paper extends the evolutionary structural optimization method to the generic design problems of connection topology. The proposed approach consists of a simple cycle of a finite element analysis followed by a rule‐driven element removal process. To make the interconnection elements carry as close to uniform a load as possible, a “fully stressed” design criterion is adopted. To determine the presence and absence of the interconnection elements, the usage efficiencies of fastener elements are estimated in terms of their relative stress levels. This avoids the use of gradient‐based optimization algorithms and allows designers to readily seek an optimization of connection topology, which can be implemented in their familiar CAD/CAE design platforms. To demonstrate the capabilities of the proposed procedure, a number of design examples are presented in this paper.

Introduces a faster and improved structural optimisation method which combines fixed grid finite element analysis (FG FEA) and evolutionary structural optimisation (ESO). ESO optimises a structure by removing a few elements at every iteration. FG methods allow fast mesh generation, fast solution and fast re‐evaluation of the modified meshes. The implementation of FG into the ESO process eliminates the need for regenerating the mesh and a few arithmetic calculations replace the full regeneration of the stiffness matrix every time the structure is modified. This greatly reduces the solution time, and the examples presented in this paper demonstrate and validate the method.