# Optimized Design of Flexible Quick-Change System Based on Genetic Algorithm and Monte Carlo Method
## 1 Introduction  
The aerospace field is progressively exhibiting the trend of high technology, intelligence, and integration as a result of the aerospace industry's rapid development, which has increased the technological level and production capacity requirements for the aerospace manufacturing sector [1]. More and more thin-walled complex structural parts are used in the structure of aerospace products in order to meet the demanding requirements of aircraft, spacecraft, and precision instruments on the performance and reliability of parts, and the precision requirements for such thin-walled structural parts are also rising [2]. Massive thin-walled structural components are increasingly used in the aerospace industry because they may effectively reduce the number of parts, drastically lower production costs, and well meet the objectives of overall structural lightweight design [3, 4]. The requirements for multi-species and multi-process precision manufacturing necessitate numerous iterations of testing, alignment, positioning, machining, repairs, and reworks. This creates a multitude of challenges, including difficult product replacement, inefficient use of resources, high costs, and acting as a bottleneck that prevents product renewal while limiting quality and efficacy. Hence, the efficient production of massive thin-walled parts in the aerospace industry depends greatly on the precise, quick, and economical flexibility of the clamping system [4]. The zero point clamping unit can create a consistent interface between the station, the machine tool, and the process. It also efficiently combines stable clamping with high-precision positioning to guarantee productivity. With its great performance of positioning and locking in one step, decreasing downtime by external clamping, high degree of automation, and high precision, it is extensively utilized in flexible quick-change systems [5]. The current research focus is on how to choose the best number and distribution of zero point clamping systems (geometric structure, angle), as well as how to increase repetitive positioning accuracy, in order to meet the needs of multi-process manufacturing for a variety of workpieces while also achieving the requirements of economy, adaptability, and reliability [6; 7]. In the literature, the layout problem of a fixture system is primarily solved using evolutionary algorithms, ant colony algorithms (ACA), genetic algorithms, and so on. [PERSON] et al. [8] used a genetic algorithm and finite element method to optimize the optimal fixture layout of flexible sheet metal components, which improved the geometric quality of the components. [PERSON] [9; 10] both adopted a hybrid model of a genetic algorithm and a finite element to extract the optimal positions of clamping points in the fixture, respectively. [PERSON] et al. [11] used the simulated annealing algorithm (SAA) as a global optimization method to propose an optimal design method for fixture layout in the assembly process. [PERSON] et al. [12] combined evolutionary algorithms and FEM to optimize the design of the fixture. [PERSON] et al. [13] proposed the optimization of fixture layout by minimizing the maximum workpiece deformation on a two-dimensional fixture workpiece system in an end milling operation based on hybrid artificial neural network-particle swarm optimization and evolutionary techniques. [PERSON] et al. [14] achieved higher accuracy by modeling the fixture-workpiece system to position the positioner and fixture. [PERSON] et al. [15] optimized the position of the positioner by generating a response surface model of the assembly model to predict changes in the assembly. [PERSON] et al. [16] integrated genetic algorithms and Monte Carlo methods to optimize the scheduling problem of flexible job shops from various aspects. [PERSON] et al. [17] used genetic algorithms to optimize the design of the layout of the outpatient clinic. [PERSON] et al.