A digital twin-enhanced system for engineering product family design and optimization

Engineering product family design and optimization in complex environments has been a major bottleneck in today’s industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.     

Optimal design of reconfigurable parallel machining systems

A reconfigurable machining system is usually a modularized system, and its configuration design concerns the selections of modules and the determination of geometric dimensions in some specific modules. All of its design perspectives from kinematics, dynamics, and control have to be taken into considerations simultaneously, and a multidisciplinary design optimization (MDO) tool is required to support the configuration design process. This paper presents a new MDO tool for reconfigurable machining systems, and it includes the following works: (i) the literatures on the computer-aided design of reconfigurable parallel machining systems have been reviewed with a conclusion that the multidisciplinary design optimization is essential, but no comprehensive design tool is available to reconfigurable parallel machining systems; (ii) a class of reconfigurable systems called reconfigurable tripod-based machining system has been introduced, its reconfiguration problem is identified, and the corresponding design criteria have been discussed; (iii) design analysis in all of the disciplines including kinematics, dynamics, and control have been taken into considerations, and design models have been developed to evaluate various design candidates; in particular, the innovative solutions to direct kinematics, stiffness analysis for the design configurations of tripod-based machines with a passive leg, and concise dynamic modelling have been provided; and (iv) A design optimization approach is proposed to determine the best solution from all possible configurations. Based on the works presented in this paper, a computer-aided design and control tool have been implemented to support the system reconfiguration design and control processes. Some issues relevant to the practical implementation have also been discussed.     

Component-oriented decomposition for multidisciplinary design optimization in building design

The potential of Multidisciplinary Design Optimization (MDO) is not sufficiently exploited in current building design practice. I argue that this field of engineering requires a special setup of the optimization model that considers the uniqueness of buildings, and allows the designer to interact with the optimization in order to assess qualities of aesthetics, expression, and building function. For this reason, the approach applies a performance optimization based on resource consumption extended by preference criteria. Furthermore, building design-specific components serve for the decomposition and an interactive way of working. The component scheme follows the Industry Foundation Classes (IFC) as a common Building Information Model (BIM) standard in order to allow a seamless integration into an interactive CAD working process in the future. A representative case study dealing with a frame-based hall design serves to illustrate these considerations. An N-Square diagram or Design Structure Matrix (DSM) represents the system of components with the disciplinary dependencies and workflow of the analysis. The application of a Multiobjective Genetic Algorithm (MOGA) leads to demonstrable results.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

An efficient strategy for reliability-based multidisciplinary design optimization using BLISS

This paper presents an efficient reliability-based multidisciplinary design optimization (RBMDO) strategy. The conventional RBMDO has tri-level loops: the first level is an optimization in the deterministic space, the second one is a reliability analysis in the probabilistic space, and the third one is the multidisciplinary analysis. Since it is computationally inefficient when high-fidelity simulation methods are involved, an efficient strategy is proposed. The strategy [named probabilistic bi-level integrated system synthesis (ProBLISS)] utilizes a single-level reliability-based design optimization (RBDO) approach, in which the reliability analysis and optimization are conducted in a sequential manner by approximating limit state functions. The single-level RBDO is associated with the BLISS formulation to solve RBMDO problems. Since both the single-level RBDO and BLISS are mainly driven by approximate models, the accuracy of models can be a critical issue for convergence. The convergence of the strategy is guaranteed by employing the trust region–sequential quadratic programming framework, which validates approximation models in the trust region radius. Two multidisciplinary problems are tested to verify the strategy. ProBLISS significantly reduces the computational cost and shows stable convergence while maintaining accuracy.     

Scalable platforms using ant colony optimization

Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.     

Multiple-platform based product family design for mass customization using a modified genetic algorithm

A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products with the prerequisite of satisfying customers’ performance requirements. Optimization based methods are experiencing new found use in product family design to resolve the inherent tradeoff between commonality and distinctiveness that exists within a product family. This paper presents and develops a 2-level chromosome structured genetic algorithm (2LCGA) to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of the proposed approach is demonstrated through the design of a family of universal electric motors and comparison against previous work.     

Product variety optimization under modular architecture

In this paper, product variety design is discussed under an optimization viewpoint. Product variety design means the challenge to simultaneously design multiple products for achieving higher optimality beyond ordinary design methods for a single product. When the possibilities of computational optimization for product variety design under fixed product architecture are explored, optimization is demanded to determine the contents of modules and their combinations under fixed modular architecture. This indicates that product variety optimization includes three classes of optimization problems: attribute assignment, module combination, and simultaneous design of both. Following problem classification, the domains and situations of such optimization problems are investigated. Then, two typical optimization examples are demonstrated through aircraft design for simultaneous optimal attribute assignment and through design of television receiver circuits for optimal module combination, respectively. The discussion concludes with the roles of problem classification and the direction of future works.     

卫星有效载荷的多目标多学科设计优化研究

在卫星有效载荷系统研究中,实施多目标多学科优化的可行性设计。首先,分析了开展卫星有效载荷多学科设计优化的关键技术。建立了包含天线、转发器、数据传输、可靠性、成本和质量的多学科分析模型。然后,应用多目标遗传算法对某卫星有效载荷的可靠性和成本进行多目标设计优化,获得最优解集。最后,运用多学科协同优化结合遗传算法进行可靠性单目标设计优化。研究结果表明:有效载荷的多目标多学科设计优化全面考虑了多个学科之间的关系,设计人员可按需选择其满意的优化结果,大幅提高设计效率;协同优化方法有助于实现学科自治、并行设计,提高设计的灵活性和缩短设计周期。     

Integrating linear physical programming within collaborative optimization for multiobjective multidisciplinary design optimization

Multidisciplinary design optimization (MDO) is a concurrent engineering design tool for large-scale, complex systems design that can be affected through the optimal design of several smaller functional units or subsystems. Due to the multiobjective nature of most MDO problems, recent work has focused on formulating the MDO problem to resolve tradeoffs between multiple, conflicting objectives. In this paper, we describe the novel integration of linear physical programming within the collaborative optimization framework, which enables designers to formulate multiple system-level objectives in terms of physically meaningful parameters. The proposed formulation extends our previous multiobjective formulation of collaborative optimization, which uses goal programming at the system and subsystem levels to enable multiple objectives to be considered at both levels during optimization. The proposed framework is demonstrated using a racecar design example that consists of two subsystem level analyses — force and aerodynamics — and incorporates two system-level objectives: (1) minimize lap time and (2) maximize normalized weight distribution. The aerodynamics subsystem also seeks to minimize rearwheel downforce as a secondary objective. The racecar design example is presented in detail to provide a benchmark problem for other researchers. It is solved using the proposed formulation and compared against a traditional formulation without collaborative optimization or linear physical programming. The proposed framework capitalizes on the disciplinary organization encountered during large-scale systems design.     

一种多学科设计优化工作流验证方法

在多学科设计优化集成系统中,设计过程和优化求解算法均通过可视化工作流实现,工作流有效性验证对提高设计效率和提高系统的用户体验具有重要意义。当前验证方法主要针对办公自动和企业管理系统中的工作流验证问题,多学科设计优化集成系统中的工作流验证问题研究较少。在分析前期工作验证技术的基础上,针对以循环结构为特征的优化环,提出一种基于图论方法的,名为浓缩环(concentration-loop)的验证算法。结合发射平台数字化设计系统的设计与实现,对该算法进行了验证。     

Design space optimization using design space adjustment and refinement

To deal with large-scale problems that often occur in industry, the authors propose design space optimization with design space adjustment and refinement. In topology optimization, a design space is specified by the number of design variables, and their layout or configuration. The proposed procedure has two efficient algorithms for adjusting and refining design space. First, the design space can be adjusted in terms of design space expansion and reduction. This capability is evolutionary because the design domain expands or reduces wherever necessary. Second, the design space can be refined uniformly or selectively wherever and whenever necessary, ensuring a target resolution with fewer elements, especially for selective refinement. Accordingly, the proposed procedure can handle large-scale problems by solving a sequence of smaller problems. Two examples show the efficiency of the proposed approach.     

Modular and platform methods for product family design: literature analysis

After the industrial revolution, the literature has mentioned different principles to allow a better management of the production and product life cycle activities. For example the principle of standardization was first mentioned in the literature by an automobile engineer and placed in a real context by Henry Ford. Standardization has made possible the configuration of different products using a large set of common components. Another strategy called modularization was first mentioned in the literature in the 60s. The modularity proposed to group components of products in a module for practical production objectives. Today, modularity and standardization are promising tools in product family development because they allow to design a variety of products using the same modules of components called platforms. Using platforms allows important family design savings and easy manufacturing. In this paper we give a literature review of the platform concept with a special interest on the efficient product family development. This paper is organized as follows. Section 1 mentions the general context of modularity to develop product variety. Section 2 details the importance of product architectures in the literature for a modular design. Section 3 points on some important works that apply some modular and platform methodologies.This revised version was published in June 2005 with corrected page numbers.     

A generic genetic algorithm for product family design

Product family design (PFD) has been well recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. PFD essentially entails a configuration problem by “combination," where combinatorial explosion always occurs and is known to be mathematically intractable or NP-hard. Although genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems, it is difficult to adopt the traditional GA to deal with the complex data and interrelationships inherent in the PFD problem. This paper proposes a generic genetic algorithm (GGA) for PFD. A generic encoding scheme is developed to adapt to diverse PFD scenarios. A hybrid constraint-handling strategy is proposed to handle complex and distinguishing constraints at different stages along the evolutionary process. The design and implementation procedures of the GGA are discussed in detail. An application of the proposed GGA to motor family design is reported. The GGA efficiency is also tested through efficiency analysis in terms of the probability of generating feasible solutions, as well as through analysis of the GGA complexity.     

Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools

In today’s global manufacturing environment, changes are inevitable and something that every manufacturer must respond to and take advantage of, whether it is in regards to technology changes, product changes, or changes in the manufacturing processes. The reconfigurable manufacturing system (RMS) meets this challenge through the ability to rapidly and efficiently change capacity and functionality, which is the reason why it has been widely labelled the manufacturing paradigm of the future. However, design of the RMS represents a significant challenge compared to the design of traditional manufacturing systems, as it should be designed for efficient production of multiple variants, as well as multiple product generations over its lifetime. Thus, critical decisions regarding the degree of scalability and convertibility of the system must be considered in the design phase, which affects the abilities to reconfigure the system in accordance with changes during its operating lifetime. However, in current research it is indicated that conventional manufacturing system design methods do not support the design of an RMS and that a systematic RMS design method is lacking, despite the fact that numerous contributions exist. Moreover, there is currently only limited evidence for the breakthrough of reconfigurability in industry. Therefore, the research presented in this paper aims at synthesizing current contributions into a generic method for RMS design. Initially, currently available design methods for RMS are reviewed, in terms of classifying and comparing their content, structure, and scope, which leads to a synthesis of the reviewed methods into a generic design method. In continuation of this, the paper includes a discussion of practical implications related to carrying out the design, including an identification of potential challenges and an assessment of which tools that can be applied to support the design. Conclusively, further areas for research are indicated, which provides valuable knowledge of how to develop and realize the benefits of reconfigurability in industry.     

微机械陀螺的多学科设计优化建模的研究

以梳状音叉式振动微机械陀螺为例,将多学科设计优化（MDO）方法应用到微机械陀螺的优化设计中。将微机械陀螺复杂系统分解为归属不同学科的多个子系统,阐明了在多学科设计优化中各子系统之间的相互关系。建立了微机械陀螺的结构设计、机械性能、电学性能子系统和系统级的多学科设计优化模型。用已研制的MMCDO多学科混合协同设计优化算法计算,得到了满意的优化设计结果。     

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Product platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs.     

A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration

Product family design is a popular approach adopted by manufacturers to increase their product varieties in order to satisfy the needs of various markets. In recent years, because of increasing environmental concerns in societies and strict regulations of environmental protection, quite a number of manufacturers adopted remanufacturing strategy in their product development in response to the challenges. Remanufacturing of used products unavoidably involves a closed-loop supply chain system. To achieve the best outcomes, the supply chain design should be considered in product family design process. In this research, a multi-objective optimization model of integrated product family and closed loop supply chain design is formulated based on a cooperative game model for minimizing manufacturer’s total cost and maximize suppliers’ total payoffs. Since the optimization problem could be a large- scale one and involves mixed continuous-discrete variables, a new version of nondominated sorting genetic algorithm-II (NSGA-II), namely cooperative negotiation embedded NSGA-II (NSGA-CO), is proposed to solve the optimization model. Simulation tests are conducted to validate the effectiveness of the proposed NSGA-CO. The test results indicate that the proposed NSGA-CO outperforms NSGA-II in solving various scale of multi-objective optimization problems in terms of convergence. With the formulated optimization model and the proposed NSGA-CO, a case study of integrated product family and supply chain design is conducted to investigate the effects of environmental penalty, quantity of demand and marginal cost of remanufacturing on used product return rate, manufacturers’ and suppliers’ profits and joint payoff.     

Solving uncapacitated multilevel lot-sizing problems using a particle swarm optimization with flexible inertial weight

The multilevel lot-sizing (MLLS) problem is a key production planning problem in materials requirements planning (MRP) system. The MLLS problem deals with determining the production lot-sizes of various items appearing in the product structure over a given finite planning horizon to minimize the production cost, the inventory carrying cost, the back ordering cost and etc. This paper proposed a particle swarm optimization (PSO) algorithm for solving the uncapacitated MLLS problem with assembly structure. All the mathematical operators in our algorithm are redefined and the inertial weight parameter can be either a negative real number or a positive one. The feasibility and effectiveness of our algorithm are investigated by comparing the experimental results with those of a genetic algorithm (GA).     

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.