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Life cycle assessment (LCA), as an assessment tool for environmental performance, is used widely in decision making of product design. However, apparently, it is not sufficient to make final decision only depending on environmental assessment results for profit-directed organizations. One of the main purposes of this paper is to propose a methodology for integration of LCA and economic analysis tool-life cycle costing (LCC) in order to get the integrated evaluation results. A framework of integration of LCA and LCC has been introduced, which is contained by four components: the definition of unity time and physical boundaries, integration of inventory analysis, integration of impact assessment, environmental and economic interpretation. However, the integrated evaluation results always indicate that the relationship between economic and environmental performance is not in balance. Optimization is as important as integration to eco-balance. An optimization method is proposed to improve the initial product design with maximizing the integrated environmental and economic benefit for mechanical product. In optimization step, product system is divided two parts with environmental subsystem and economic subsystem. The integrated evaluation results are adopted to support for the optimization. Some mathematic optimization models are set up for environmental and economic subsystems. Multidisciplinary design optimization is applied to optimize these models so that the initial mechanical product design can achieve the best environmental and economic performance result. A case study of type 4135G diesel engine is presented to validate the rationality and feasibility. [ABSTRACT FROM AUTHOR]

Complex system engineering design optimization based on simulation is a very time-consuming, even computationally prohibitive process. To relieve the computational burden, metamodels are commonly used to replace the computation-intensive simulations. In this paper, an active learning variable fidelity (VF) metamodeling approach (AL-VFM) is proposed for the purpose of integrating information from both low-fidelity (LF) and high-fidelity (HF) models. In AL-VFM, Kriging metamodel is adopted to map the difference between the HF and LF models aiming to approach the HF model on the entire domain. Besides, a general active learning strategy is introduced in AL-VFM to make full use of the already-acquired information to guide the VF metamodeling. The already-acquired information represents the location of regions where the differences between the HF and LF models are multi-model, non-smooth and have abrupt changes. Several numerical and engineering cases with different degrees of difficulty verify the applicability of the proposed VF metamodeling approach. [ABSTRACT FROM AUTHOR]

Laser brazing process with crimping butt is sensitive to welding parameters, and it is difficult to acquire a good quality of welding joint. To achieve good welding parameters (welding speed, wire feed rate, gap), this paper addresses the multi-objective optimization of bead profile, namely sum of left side and right side of bead geometry and subtraction between top width of bead and bottom width of bead profile. Back propagation neural network was used to predict goals with average error of 9.95 and 8.54 %; non-dominated sorting genetic algorithm was adopted to acquire a Pareto set, and verification experiments demonstrated that relative errors were controlled within 3.97 %. Meanwhile, the importance from welding parameters on goals was ranked by signal-noise ratio and interactions between each parameter. Therefore, a novel multi-objective optimization method was proved to be feasible and would be useful to guide the actual welding process of laser brazing. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two crucial components in a manufacturing system. The integration of the two functions has an important significance on improving the performance of the manufacturing system. However, integrated process planning and scheduling is an intractable non-deterministic polynomial-time (NP)-hard problem, and the multiple objectives requirement widely exists in real-world production situations. In this paper, a multi-objective mathematical model of integrated process planning and scheduling is set up with three different objectives: the overall finishing time (makespan), the maximum machine workload (MMW), and the total workload of machines (TWM). A multi-objective memetic algorithm (MOMA) is proposed to solve this problem. In MOMA, all the possible schedules are improved by a problem-specific multi-objective local search method, which combines a variable neighborhood search (VNS) procedure and an effective objective-specific intensification search method. Moreover, we adopt the TOPSIS method to select a satisfactory schedule scheme from the optimal Pareto front. The proposed MOMA is tested on typical benchmark instances and the experimental results are compared with those obtained by the well-known NSGA-II. Computational results show that MOMA is a promising and very effective method for the multi-objective IPPS problem. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to mitigate the computational burden caused by expensive simulation codes and employ both efficiently simplified and expensively detailed information in multidisciplinary design optimization (MDO), an effective framework combining variable fidelity metamodels (VFM) and modified BLISCO (MBLISCO) (VFM-MBLISCO) is proposed. Design/methodology/approach – The concept of the quasi-separable MDO problems is introduced to limit range of applicability about the BLISCO method and then based on the quasi-separable MDO form, the modification of BLISCO method without any derivatives is presented to solve the problems of BLISCO. Besides, an effective framework combining VFM-MBLISCO is presented. Findings – One mathematical problem conforms to the quasi-separable MDO form is tested and the overall results illustrate the feasibility and robustness of the MBLISCO. The design of a Small Waterplane Area Twin Hull catamaran demonstrates that the proposed VFM-MBLISCO framework is a feasible and efficient design methodology in support of design of engineering products. Practical implications – The proposed approach exhibits great capability for MDO problems with tremendous computational costs. Originality/value – A MBLISCO is proposed which can avoid the non-separability of the original BLISCO and an effective framework combining VFM-MBLISCO is presented to efficiently integrate the different fidelities information in MDO. [ABSTRACT FROM AUTHOR]

Metamodels are often used in engineering design optimization problems with expensive simulations to save computational cost. But these metamodels often face 'curse-of-dimensionality' when used in approximating high dimensional problems. Therefore, a new high dimensional model representation (HDMR) by combining Cut-HDMR with an enhanced RBF based on ensemble model is proposed. The developed HDMR, termed as ERBF-HDMR, sufficiently utilizes advantages of RBF and ensemble model in the modeling process. It can naturally explore and exploit the linearity/nonlinearity and correlations among variables of underlying problems, which are unknown or computationally expensive. Besides, to improve the efficiency of the ERBF-HDMR, an adaptive sampling method is proposed to add new sample points. Moreover, a mathematical function is used to illustrate the modeling principles and procedures of the adaptive ERBF-HDMR. And a comprehensive comparison between the adaptive ERBF-HDMR and other different Cut-HDMRs in literature has been made on eleven numerical examples with a wide scope of dimensionalities to show the prediction ability of different HDMRs. Finally, the proposed HDMR is used in the structural design optimization of the bearings of an all-direction propeller with the aim of reducing vibration. [ABSTRACT FROM AUTHOR]

Purpose – Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues. Design/methodology/approach – In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously. Findings – The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate. Originality/value – The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem. [ABSTRACT FROM AUTHOR]

For engineering product design under uncertainty, it is important to guarantee the reliability and safety, especially for complex and coupled systems design problems, using multidisciplinary design optimisation (MDO). Due to the different cognitive levels to various uncertain parameters, it is necessary to include the mixed uncertain parameters such as the random parameters and fuzzy parameters into consideration simultaneously. The design problem under hybrid uncertainty of randomness and fuzziness presents a challenge. However, little attention has been paid to solve hybrid uncertainty problems until now. This study constructs the formulation of fuzzy random uncertainty-based MDO (FRMDO), and proposes a method to solve the FRMDO problems within the framework of sequential optimisation and reliability assessment (SORA), called the FRMDO–SORA approach. The FRMDO–SORA approach can be applied both in single-level and multi-level MDO methods, and can effectively solve MDO problems, where fuzzy, random and fuzzy-random uncertainties coexist. Case studies are given to demonstrate the efficiency and feasibility of the proposed FRMDO–SORA approach. [ABSTRACT FROM AUTHOR]

In this paper, a generalised collaborative optimisation (GCO) method is proposed in the field of multidisciplinary design optimisation (MDO). In this method, the concept of generalised multidisciplinary variables is put forward and summarised as four types to provide more comprehensive interdisciplinary compatibility constraints than traditional collaborative optimisation. The linear normalisation method is adopted in GCO to remove inconsistencies caused by the range discrepancies between design variables. The effectiveness of GCO is demonstrated by its successful application in the design of a gear reducer. Furthermore, to undertake the computational and organisational challenges in MDO, an effective approach combining kriging and GCO (KGCO) is presented. In this approach, kriging metamodels are employed to reduce the computational cost by replacing expensive simulation and analysis codes. System-level compatibility constraints are approximated by kriging metamodels to remove the non-smoothness of the original quadratic constraint forms. The feasibility and computing efficiency of the KGCO approach are illustrated by solving the gear reducer design problem. Through its innovative and successful application in the design of a small waterplane area twin hull ship, it is further demonstrated that the KGCO approach is a feasible and efficient design methodology in support of the design of engineering products. [ABSTRACT FROM AUTHOR]

Kriging approximation has been widely used in reliability-based design optimization (RBDO) to replace the complex black-box performance functions. In this paper, a new local approximation method using the most probable point (LMPP) is proposed to improve the accuracy and efficiency of RBDO methods using Kriging model. In the LMPP, the concept of local sampling region is used and the most probable point (MPP) is chosen as the sampling center. The size of the local region is determined by target reliability and the linearity of probability constraint around MPP. Rather than fitting the Kriging model for all the probabilistic constraints, the new method uses the MPP to find feasible constraints, and only these feasible constraints are accurately approximated, which can significantly improve the optimization efficiency. Importance Sampling method using the MPP obtained above as sampling center is utilized to perform reliability analysis and reliability sensitivity calculation. A numerical example, a honeycomb material design problem and a box girder design application are used to demonstrate the computational capability of the LMPP method. The comparison results demonstrate that RBDO using the proposed method is very effective. [ABSTRACT FROM AUTHOR]