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Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated with a quantity of finite element (FE) analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3dcs simulation (for dimensional analyses) and global optimization algorithms. In this paper, two global algorithms are proposed to optimize fixture locator points, which are social radiation algorithm (SRA) and GAOT, a genetic algorithm (GA) in optimization toolbox in matlab. The flowchart of fixture design includes the following steps: (1) The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. (2) The two global optimization algorithms are edited to be used to optimize fixture schemes based on matlab. (3) Since matlab macrocommands of 3dcs have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the GAOT algorithm is more suitable than SRA for generating the optimal fixture layout with excellent efficiency for engineering applications. [ABSTRACT FROM AUTHOR]

Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning (DSP) is a challenging combinatorial optimization problem due to complex constraints of many products. This paper considers partial and parallel disassembly sequence planning for solving the degrees-of-freedom in modular product design, considering disassembly time, cost, and energy consumption. An automatic self-decomposed disassembly precedence matrix (DPM) is designed to generate partial/parallel disassembly sequence for reducing complexity and improving efficiency. A Tabu search-based hyper heuristic algorithm with exponentially decreasing diversity management strategy is proposed. Compared with the low-level heuristics, the proposed algorithm is more efficient in terms of exploration ability and improving energy benefits (EBs). The comparison results of three different disassembly strategies prove that the partial/parallel disassembly has a great advantage in reducing disassembly time, and improving EBs and disassembly profit (DP). [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) processes are used to fabricate complex geometries using a layer-by-layer material deposition technique. These processes are recognized for creating complex shapes which are difficult to manufacture otherwise and enable designers to be more creative with their designs. However, as AM is still in its developing stages, relevant literature with respect to design guidelines for AM is not readily available. This paper proposes a novel design methodology which can assist designers in creating parts that are friendly to additive manufacturing. The research includes formulation of design guidelines by studying the relationship between input part geometry and AM process parameters. Two cases are considered for application of the developed design guidelines. The first case presents a feature graph-based design improvement method in which a producibility index (PI) concept is introduced to compare AM friendly designs. This method is useful for performing manufacturing validation of pre-existing designs and modifying it for better manufacturability through AM processes. The second approach presents a topology optimization-based design methodology which can help designers in creating entirely new lightweight designs which can be manufactured using AM processes with ease. Application of both these methods is presented in the form of case studies depicting design evolution for increasing manufacturability and associated producibility index of the part. [ABSTRACT FROM AUTHOR]

Energy consumption in five-axis machining of freeform surfaces can be considerably large for large-size parts. This paper presents a study on how to setup the workpiece in order to minimize the energy consumption without modifying the toolpath itself. For an arbitrary freeform workpiece, the way how it is setup on the working table highly affects the machine's kinematic behavior, which dominates the overall processing time and energy consumption. Taking into account the speed and acceleration limit of each axis of the machine, we first establish the energy consumption model as a function of the workpiece setup. However, this original model involves certain critical physically pertinent coefficients (such as the moment of inertial of a rotary table) which are usually unavailable in practice. Instead, by exploring insightful geometric characteristics of the five-axis machine, an alternative energy consumption model is established which is independent of those hard-to-obtain coefficients. A simple algorithm is then designed to optimize this model. Both computer simulations and physical cutting experiments demonstrate that, when compared with an arbitrary setup, the optimized workpiece setup is able to achieve a significant saving (as much as 50%) in both energy consumption and total machining time, both using a same tool path. [ABSTRACT FROM AUTHOR]

The design of manufacturing systems is typically decoupled from ordering policy decisions. Traditionally, the system design decision is made to minimize the total investment cost given some system capacity requirements. Then, after the system is implemented, the ordering policy decisions are made. In this paper, a coupled approach is presented whereby the manufacturing system design is jointly developed with the ordering policy to minimize the total cost of inventory' holding, setup, and equipment investment in a multiproduct system. The methodology is presented in the context of scalable-reconfigurable manufacturing systems (scalable-RMSs). First, a linear integer mathematical formulation to minimize investment cost in a single-product, multistage scalable-RMS is presented. The mathematical formulation is then extended to consider multiple products. Due to the nonlinear nature of the multiproduct formulation, an iterative algorithm is developed. Lastly, a mathematical formulation to simultaneously minimize the system investment and operating costs (i.e., the coupled approach) is presented. Given the complexity of the formulation, a genetic algorithm (GA)-based heuristic is proposed. Twenty, four instances of the problem were generated to test the proposed methodologies. Experimental results indicate that the proposed GA-based heuristic is efficient in terms of solution quality, and runtime. Further experimental results indicate that the proposed coupled approach reduces the total costs by an average of 25% over the decoupled approach. It is concluded that the coupled approach (solved with the proposed GA-based heuristic) outperforms the decoupled approach (solved to optimality) in all instances considered. [ABSTRACT FROM AUTHOR]

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques. [ABSTRACT FROM AUTHOR]