Integration of dimensional quality and locator reliability in design and evaluation of multi-station body-in-white assembly processes

Research efforts have been made to develop a Quality and Reliability chain (QR chain) model to integrate manufacturing system component reliability and product quality in multi-station manufacturing processes. Based on a previously developed state space model, which captures the variation propagation throughout all stations, a general QR chain model is developed for Body-In-White (BIW) assembly processes. The effectiveness of the QR chain modeling strategy is demonstrated by thoroughly studying the relationship between locator performance and product quality in assembly processes. Based on the analytical result of system reliability obtained from the QR chain model, optimal locator wear rate assignment is further investigated. A case study is conducted to demonstrate the effectiveness and potential usage of the QR chain model for the BIW assembly system design evaluation and optimization.     

Optimal coordinate sensor placements for estimating mean and variance components of variation sources

In-process optical coordinate measuring machines offer the potential to diagnose the sources of the variations that are responsible for product quality defects. Such a sensor system can thus help manufacturers to improve product quality and reduce process downtime. The effective use of sensor data in the diagnosis of the sources of variations depends on the optimal design of the sensor system, which is often also called the problem of sensor placement. This paper addresses coordinate sensor placement for the diagnosis of dimensional variation sources in assembly processes. Sensitivity indices for the detection of the process mean and variance components are defined as the design criteria and are derived in terms of process layout and sensor deployment information. Exchange algorithms, originally developed for optimal experimental design, are revised and then used to maximize the detection sensitivity. A sort-and-cut procedure is proposed, which is able to significantly improve the algorithm efficiency of the current exchange routine. The resulting optimal sensor layout and its implications are illustrated in the specific context of a panel assembly process.     

Process-oriented tolerancing for multi-station assembly systems

In multi-station manufacturing systems, the quality of final products is significantly affected by both product design as well as process variables. Historically, however, tolerance research has primarily focused on allocating tolerances based on the product design characteristics of each component. Currently, there are no analytical approaches to optimally allocate tolerances to integrate product and process variables in multi-station manufacturing processes at minimum costs. The concept of process-oriented tolerancing expands the current tolerancing practices, which bound errors related to product variables, to explicitly include process variables. The resulting methodology extends the concept of “part interchangeability” into “process interchangeability,” which is critical due to increasing requirements related to the selection of suppliers and benchmarking. The proposed methodology is based on the development and integration of three models: (i) the tolerance-variation relation; (ii) variation propagation; and (iii) process degradation. The tolerance-variation model is based on a pin-hole fixture mechanism in multi-station assembly processes. The variation propagation model utilizes a state space representation but uses a station index instead of a time index. Dynamic process effects such as tool wear are also incorporated into the framework of process-oriented tolerancing, which provides the capability to design tolerances for the whole life-cycle of a production system. The tolerances of process variables are optimally allocated through solving a nonlinear constrained optimization problem. An industry case study is used to illustrate the proposed approach.     

Study on Quality Evaluation Model in Multi-station Assembly Processes

Based on features of dimension variation propagation in multi-station assembly processes, a new quality evaluation model of assembly processes is established. Firstly, the error source of multi-station assembly system is analyzed, the relationship of dimension variation propagation in multi-station assembly processes is studied and the state equation for variation propagation is constructed too. Then, the feature parameters which influence variation propagation and accumulation in multi-station assembly processes are found to evaluate quality characteristic of the assembly system. Through the derivation of equation on dimension variation propagation, station coefficient matrices which are combined and conversed to determine the max eigenvalue are educed. The max eigenvalue is multiplied by the weight coefficient to establish the quality evaluation model in multi-station assembly processes. Furthermore, assembly variation indexes are proposed to judge of the assembly technology process. Finally, through the practical example, the application of the model and assembly variation indexes are presented.     

Developing a selective assembly technique for sheet metal assemblies

Applying the concept of Digital Twin in production processes supports the manufacturing of products of optimal geometry quality. This concept can be further supported by a strategy of finding the optimal combination of individual parts to maximise the geometrical quality of the final product, known as selective assembly technique. However, application of this technique has been limited to assemblies where the final dimensions are just function of the mating parts' dimensions and this is not applicable in sheet metal assemblies. This paper develops a selective assembly technique for sheet metal assemblies and investigates the effect of batch size on the improvements. The presented method utilises a variation simulation tool (Computer-Aided Tolerancing tool) and an optimisation algorithm to find the optimal combination of the mating parts. The approach presented is applied to three industrial cases of sheet metal assemblies. The results show that using this technique leads to a considerable reduction of the final geometrical variation and mean deviation for these kinds of assemblies. Moreover, increasing the batch size reduces the amount of achievable improvement in variation but increases the amount of achievable improvement in the mean deviation.     

Fixture layout optimisation based on a non-domination sorting social radiation algorithm for auto-body parts

Auto-body compliant parts are easily deformed during clamping and welding, and fixture layout is very important to the final product quality. However, it is very difficult to design fixture layout because it needs to synchronously satisfy the requirements of assembly tolerance and gravity deformation, which are calculated by finite element analysis. This paper proposes a new method to optimise fixture scheme by a non-domination sorting social radiation algorithm (NSSRA). Firstly, unfeasible nodes are eliminated by four rules according to manufacturing experiences. Afterwards, a few groups are divided based on positions of all feasible nodes. N groups are optimised using NSSRA. Finally, the best fixture layout is generated by selecting the feasible points among the optimal groups in last step. A case study of inner hood is used to illustrate the proposed method, and the results suggest that NSSRA has better efficiency and higher accuracy than NSGA-II.     

HPRODIE: Using feature modelling and feature mapping to speed up progressive die design

For various reasons, the advances in progressive die design remain too slow compared with the increasing importance of progressive dies in modern industry. Aimed at speeding up the design process, HPRODIE, an integrated CAD/CAM system for progressive dies, was developed. In HPRODIE, three featuresthe product feature for the representation of sheet metal parts, the stamping process feature for the strip layout, and the die design feature for building the die assembly modelare introduced respectively to represent three life-cycle aspects of sheet metal parts. Feature mapping methods are employed to transfer the design information between different design stages at high-level semantics. With feature modelling and feature mapping, most of design processes can be carried out automatically. The testing results have shown that the design cycle has been cut down significantly by using this system.     

Quality design of tolerance allocation for sheet metal assembly with resistance spot weld

Tolerance directly influences the functionality of the products and the related manufacturing costs, and tolerance allocation is of great importance for improving the assembly quality. However, the information required to allocate tolerances for complex 3D assemblies is generally not available at the initial design stage. In this paper, a new quality design methodology is developed, which makes use of both original design data obtained by the response surface methodology and the extra interpolation data obtained by the Kriging method. The finite element modelling is presented for the sheet metal assembly process as no explicit relationship of the variations for key characteristic points are available. The robust tolerances can be allocated based on the quality design model. A case study with the typical assembly process of the rear compartment pan and the wheelhouse is carried out in the paper, the tolerance allocation results show that the developed quality design methodology is capable of determining the robust manufacturing tolerance before assembly, which satisfies the product requirements. This method enables a robust tolerancing scheme to be used in the sheet metal assembly process.     

Solving a fixture configuration design problem using genetic algorithm with learning automata approach

Proper fixture design is crucial to workpiece quality assurance in manufacturing. Incorrect fixture design may lead to workpiece deformation during machining. The fixture configuration design is one of the important aspects of fixture design. This paper deals with fixture layout optimization problem. The objective is to minimize the norm of all the passive contact forces satisfying Coulomb friction constraint, work-piece static equilibrium constraint and contact constraint, for the entire cutting operation. To solve this problem, the paper proposes Genetic Algorithm with Learning Automata (GALA) algorithm, which is a population based interconnected learning automata algorithm incorporating genetic operators. The algorithm enjoys the good characteristics of both GA and LA. It is validated with an example of face milling operation. The optimal layout is found to be in tune with empirical facts. Also, for the further investigation of the algorithm, it has been tested on a different problem sets and a comparative study is carried out.     

Integrated planning for product module selection and assembly line design/reconfiguration

To take full advantage of product modularity, modular product design and assembly system design/sreconfiguration have to be simultaneously addressed. The emerging reconfigurable production and flexible assembly techniques have made such an integrated approach possible. As such, this paper proposes an integrated approach to product module selection and assembly line design/reconfiguration problems. It further suggests that quality loss functions be used in a generic sense to quantify non-comparable and possibly conflicting performance criteria involved in the integrated problem. The complexity of the problem precludes the use of commercial software for solving meaningful sized problems in polynomial time. A genetic algorithm is therefore developed to provide quick solutions. An example problem is solved to illustrate the application of the proposed approach. Based on 72 randomly generated test problems, ANOVA analysis is further carried out to investigate the effects of genetic algorithm parameters. The convergence behaviour of the search processes is also examined by solving large problems with different numbers of operations and product modules.     

产品操作界面元素布局多目标优化设计

目的针对产品设计中操作界面布局设计时存在的随意性、不确定性大等问题,使用多目标优化设计的方法,寻找界面元素的最优布置,以提升界面的使用舒适性和人机交互效率。方法在分析了工效学准则和界面布局美度评价准则的基础上,确立了层次性、相关性、简洁性和舒适性四个界面布局基本原则,并依据原则构建了界面元素布局多目标优化数学模型,在此模型的基础上采用改进的遗传算法,建立基于遗传算法的界面元素布局多目标优化方法。结果给出产品操作界面布局设计的基本原则,提出一种基于遗传算法的产品操作界面元素布局的多目标优化方法及流程。结论提出的布局原则和优化方法能较好地协助设计师获得满足设计需求的布局方案,实例结果表明了理论模型的合理性与遗传算法对于界面元素布局多目标优化问题求解的有效性。     

Machining fixture layout optimisation under dynamic conditions based on evolutionary techniques

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.     

用二叉树遍历展开凸多面体外表面

在工程实践中，同一种凸多面体其外表面不同的展开方式将影响生产与施工的可行性与经济性。借助改进的二叉树遍历算法并引入随机数，可以生成凸多面体外表面的各种不同的展开方式，为从中筛选最优方案，指导生产施工提供了帮助。在此，将介绍以最简单的正凸多面体为研究对象编制出的试验程序，从而证明该算法应用于凸多面体外表面展开的可行性。     

The effects of forming history on sheet metal assembly

In this study a simulation-based sensitivity study is performed in order to investigate the influence of the forming history on the properties of an assembly. In the study the assembly properties are predicted by sequentially simulating the manufacturing process chain of a sheet metal assembly. Several simulations of the assembly stage are performed in which different combinations of forming histories are transferred from the forming stage. It is found that the forming history affects the properties of the assembly and that the residual stress state is the most influential history variable. This demonstrates the importance of utilising the complete final mechanical state of each manufacturing step as the initial condition for the subsequent step in the manufacturing process chain in order to achieve accurate predictions of the assembly properties. Furthermore, if more reliable predictions can be made concerning the manufacturability of a product and its in-service behaviour, more design alternatives can be evaluated during product development while a considerably smaller number of physical prototypes are needed.     

汽车板精益成形技术

建立了面向零件成形特征的合理选材方法,发明了高效的拉延筋阻力混合优化设计方法,构建了变压边力控制实验平台。从降低成形质量对材料和工艺过程波动敏感性的角度出发,研究面向材料和工艺参数随机波动的成形质量的稳健控制。形成了基于“合理选材、工艺优化、稳健设计”思想的汽车板精益成形技术体系。通过在宝钢股份和多家汽车厂10多年的成功应用,支持宝钢汽车板的市场占有率稳步达到50％,有效地推动了国产汽车板使用技术的发展。     

A novel approach to include sustainability concepts in classical DFMA methodology for sheet metal enclosure devices

Nowadays sustainable design is a mandatory requirement in the product development process. For this reason, design methodologies are addressed to establish a close relationship between environmental, social and economic impact indicators and product features from early design stages, especially in those features related to its manufacturing. In this paper, the design for manufacturing and assembly—DFMA methodology is adapted to sheet metal enclosure devices, integrating functional and component relationships to minimize particular sustainability indicators such as energy consumption, carbon footprint, number of parts, required amount of material, assembly time and manufacturing costs. Savings with the proposed method are achieved following specific sub-tasks oriented to define new simplified product components, considering changes in manufacturing processes and re-defining mechanical connections between parts. Traditional DFMA approaches consider manufacturing and assembly issues related to a reduction of product complexity and economic savings. The proposed method aims to examine the benefits in life cycle stages such as raw material consumption, service, maintenance, upgrading and end of life—EOL. The methodology is validated through a redesign of a sheet metal industrial clock, in which the sustainability impacts are calculated from a comparison of an existent product vs. a new product development. The implementation of the method in the case study demonstrate reductions of more than 25% in product mass, consumed energy and CO₂ footprint, and more than 50% in theoretical assembly time and product complexity. Sustainability indicators of the proposed method are selected from literature analysis and taking into account attributes of sheet metal enclosure devices.     

Multiobjective robust optimization method for drawbead design in sheet metal forming

It is recognized that fracture and wrinkling in sheet metal forming can be eliminated via an appropriate drawbead design. Although deterministic multiobjective optimization algorithms and finite element analysis (FEA) have been applied in this respect to improve formability and shorten design cycle, the design could become less meaningful or even unacceptable when considering practical variation in design variables and noises of system parameters. To tackle this problem, we present a multiobjective robust optimization methodology to address the effects of parametric uncertainties on drawbead design, where the six sigma principle is adopted to measure the variations, a dual response surface method is used to construct surrogate model and a multiobjective particle swarm optimization is developed to generate robust Pareto solutions. In this paper, the procedure of drawbead design is divided into two stages: firstly, equivalent drawbead restraining forces (DBRF) are obtained by developing a multiobjective robust particle swarm optimization, and secondly the DBRF model is integrated into a single-objective particle swarm optimization (PSO) to optimize geometric parameters of drawbead. The optimal design showed a good agreement with the physical drawbead geometry and remarkably improve the formability and robust. Thus, the presented method provides an effective solution to geometric design of drawbead for improving product quality.     

Application of a Genetic Algorithm to Computer-aided Bending Sequence Planning

Optimal design of the bending sequence is a key link in sheet metal free bending sequence planning,and it has an important influence on simplifying operation and guaranteeing bending precision. Bending sequence must meet the requirements for not only no collision interference of the work piece and the mold,but also working efficiency and working precision,so bending point choice,molds select,turnover and turn round of sheet metal must be considered in each bending step. In this paper,a genetic algorithm is used to design bending sequence. The interference identification is used to determine coding,exchange and mutation of the genetic algorithm. The genetic algorithm is developed to calculate the current optimal feasible solution of the bending sequence,and then the influence of initial population and evolution generations of this method on the result is analyzed by example verifications. The results prove that a global optimal solution can be obtained while the bending point number was less than 10,and optimal bending sequence which is similar to the global optimal solution can be calculated while the bending point number was more than 10. The results converge gradually to the global optimal solution with the increase of the initial population and evolution generations. As to 18 points bending work-piece,with the initial population size 150 and the evolution generations 100,we can obtain the satisfying solution.     

Automated synthesis of modular fixture designs using an evolutionary search algorithm

Fixture design is a complex problem that requires a designer to ensure that a workpiece is located deterministically, totally restrained and sufficiently supported during a manufacturing process. The use of modular fixtures, while presenting an opportunity to improve the responsiveness of a manufacturing system, adds to the complexity of the fixture design problem. The complexity is a result of the large number of fixture elements in a modular fixture system and the constraints of specified locations in which fixture elements can be placed in a grid-based modular system. This paper presents an evolutionary search algorithm that aids a fixture designer by exploring the large number of possible fixture designs and suggesting an appropriate one. The algorithm can explore the large solution space using a flexible and generic representation and it considers fixture layout and fixture configuration constraints concurrently in arriving at appropriate solutions. The initial results of the algorithm are promising.     

Design of optimal process parameters for non-isothermal forging

This paper presents a state space model and an optimal design scheme for non-isothermal metal forming processes. By selecting nodal velocity and temperature as the state variables, a non-isothermal state equation with coupled deformation and thermal terms is established. Based on this state space model, a control design scheme is developed to obtain the optimal die velocity and initial die temperature which will ensure that the effective strain-rate and temperature satisfy the design requirements. A titanium alloy engine disk forging is used to demonstrate two design examples. The results show that the proposed model and design scheme behave well for different design requirements.