Optimized sequencing of CNC milling toolpath segments using metaheuristic algorithms

Intelligent selection of a short toolpath is made possible by reducing machining cycle time. Each metal cutting layer in a workpiece is composed of several entities, such as lines and arcs, which form the different cutting segments of a cutting plan. During machining, the cutter moves at controlled feed rates along various segments at a high speed in a single cutting pass. The end of a segment is bridged to the start point of the next segment by the non-cutting movement of the tool. Any two consecutive segments can be connected in eight different ways. Finding the shortest tool path at polynomial time is impossible because toolpaths are constructed in millions of ways by sequencing the segments. This paper presents an effective method that uses heuristic optimization techniques to solve this NP-hard problem, which is known as the traveling salesman problem, for segments. The proposed method adopts particle swarm optimization (PSO) and the genetic algorithm (GA) because of their capability to generate quality solutions for optimization problems. GA and PSO are implemented in the MATLABR2016b computing environment because of the platform’s flexibility and simple coding method. The optimization procedure is validated by comparing its results with those of two industry standard CAM systems, namely, Autodesk Inventor HSM and Mastercam. Using the proposed optimization method saves up to 40 % of the tool’s airtime during machining.

     

Real-time rescheduling metaheuristic algorithms applied to FMS with routing flexibility

This paper presents the results of a simulation study of a typical flexible manufacturing system that consists of seven machining centres, a loading and an unloading area, and six different part types. Owing to the existence of identical machining centres, the part types have alternative routings (their number varies between two and eight). One of the objectives of this work is to show how the following metaheuristics: ant colony optimisation, genetic algorithms, simulated annealing, tabu search, particle swarm optimisation and electromagnetism-like method, are adapted for solving the alternative routing selection problem in real time in order to reduce the congestion in the system by selecting a routing for each part among its alternative routings. The other goal is to highlight the impact of the real-time rescheduling of parts contained in the loading station on system performances when these metaheuristics are applied. The simulation results jugged by the production rate, machines and material handling utilisation rate show that for an overloaded system, the real-time rescheduling outperforms the case without rescheduling, but it has a negative impact on the work in process.     

Identification of the unbalance distribution in flexible rotors

This paper presents a new method for estimating unbalance distributions of flexible shafts and constant eccentricities of rigid disks based on the transfer matrix method for analyzing the steady-state responses of rotor-bearing systems, in which the transfer matrix of a flexible shaft is derived in a continuous sense with any spatial unbalance distribution. Rotary inertia, gyroscopic and transverse-shear effects are also included. When deflections and deflection angles of one free end are measurable, the unbalance distribution of shafts and disks can be estimated under operating conditions by the proposed method. The main advantage of this identification technique is that only the state vector of the one free end of the rotating shaft need to be measured. Justification of the method is presented by numerical simulations.     

Due window scheduling with sequence-dependent setup on parallel machines using three hybrid metaheuristic algorithms

Due-date determination problems have gained significant attention in recent years due to the industrial focus in the just-in-time philosophy. This paper considers a machine scheduling problem where jobs should be completed at times as close as possible to their respective due dates, and hence, both earliness and tardiness should be penalized. It is assumed that earliness and tardiness (ET) penalties will not occur if a job is completed within the due window. However, ET penalties will occur if a job is completed outside the due window. The objective is to determine a schedule that minimizes sum of the earliness and tardiness of jobs. To achieve this objective, three hybrid metaheuristics are proposed. The first metaheuristic is a hybrid algorithm which combines elements from both simulated annealing (SA) as constructive heuristic search and a variable neighborhood search (VNS) as local search improvement technique. The second one presents a hybrid metaheuristic algorithm which composed of a population generation method based on an ant colony optimization (ACO) and a VNS to improve the population. Finally, a hybrid metaheuristic approach is proposed which integrates several features from ACO, SA, and VNS in a new configurable scheduling algorithm. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Computational experiments conducting on 252 randomly generated problems compare the results with the VNS algorithm proposed previously and show that the procedure is capable of producing consistently good results.     

Job-shop scheduling using certain heuristic search algorithms

This paper presents a family of sub-optimal algorithms based on the A^* heuristic search algorithm, aimed at solving the general job-shop scheduling problem. A methodology is suggested for algorithms that require adjustable memory resources, by compromising algorithmic admissibility. Different possibilities for (admissible or non-admissible) heuristic estimates are suggested and dynamically weighted heuristics are introduced to the job-shop scheduling problem, with good results. Bottleneck scheduling is introduced within this problem-solving scheme, proving a base for more powerful heuristics. In terms of solution nearness to optimal and speed, the techniques investigated generally compare favourably with similar work in this field.     

Optimal tolerance design of assembly for minimum quality loss and manufacturing cost using metaheuristic algorithms

Tolerance allocation is a design tool for reducing overall cost of manufacturing while meeting target levels for quality. An important consideration in product design is the assignment of design and manufacturing tolerances to individual component dimensions so that the product can be produced economically and functions properly. The allocation of tolerances among the components of a mechanical assembly can significantly affect the resulting manufacturing costs. In this work, the tolerance allocation problem is formulated as a non-linear integer model by considering both the manufacturing cost of each component by alternate processes and the quality loss of assemblies so as to minimise the manufacturing cost. Metaheuristics techniques such as genetic algorithm and particle swarm optimisation are used to solve the model and obtain the global optimal solution for tolerance design. An example for illustrating the optimisation model and the solution procedure is provided. Results are compared with conventional technique and the performances are analysed.     

Flexible job shop scheduling with tabu search algorithms

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers.     

深度优先搜索算法和A*算法在迷宫搜索中的仿真研究

本文通过仿真实验比较研究了深度优先搜索算法和三种不同启发式函数的A*算法在标准迷宫中的应用,在实验中,迷宫环境对机器人是未知的,而由于迷宫环境的特殊性--未知的迷宫环境中很少有不会碰撞的路径,从而增加了机器人搜索的难度.机器人搜索的目的是为了进行搜救,因此机器人应该要在尽量短的时间内搜索到目标物并将目标物带回.通过仿真实验对比了不同启发式函数的A*算法与深度优先搜索算法的性能,最后得出在迷宫搜索中A*算法要优于深度优先搜索算法.     

求解零空闲流水线调度问题的和声搜索算法

针对以总流经时间和最大完工时间为目标的零空闲流水线调度问题,提出了和声搜索调度算法。该算法利用和声搜索算法的优化机理,采用基于浮点数的编码方式和新设计的和声产生方法,扩展了传统和声搜索算法的求解模型,并结合快速插入邻域局部搜索算法,给出了三种混合求解策略。仿真试验表明,该算法具有高效性和优越性。     

Iterated hybrid metaheuristic algorithms for unrelated parallel machines problem with unequal ready times and sequence-dependent setup times

The problem considered in this paper involves a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and unequal ready times, and the objective is to minimize the weighted number of tardy jobs. Iterated hybrid metaheuristic algorithms are proposed to address this problem. The algorithms begin with effective initial solution generators to generate initial feasible solutions; then, hybrid metaheuristics are applied to improve the initial solutions, integrating the principles of the variable neighborhood descent approach and tabu search. If the search becomes trapped at a local optimum, a perturbation operator is developed to help the search escape. To evaluate the performance of the suggested algorithms, heuristic rules and iterated local search algorithms are examined and compared. Computational experimental results show that the proposed algorithms outperform the other heuristics.     

Heuristic search algorithms for lot streaming in a two-machine flowshop

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling.     

Identification of external forces in mechanical systems by using LifeCycle model and stress-stiffening effect

This paper presents an optimization-based inverse procedure for the determination of external loads applied to a given mechanical structure, by using information concerning the dynamic behavior of the system and its corresponding finite element model. The influence of the stress-stiffening effect on the dynamic characteristics of structural systems is used to establish a relation between the dynamic responses and the applied external forces. An optimization problem is formulated in which the objective function represents the difference between the measured modal characteristics of the loaded structure and their finite element counterparts. The loading parameters (magnitude, position and direction) assumed as being unknown, are considered as design variables. The identification procedure is illustrated by means of numerical simulations and experimental tests, in which a heuristic technique known as LifeCycle model was used.     

Features analvsis of vibration signals caused by unbalance of the HSK shank

介绍了不平衡振动的机理;分析了HSK刀柄产生不平衡的原因和振动信号的特征;阐述了信号预处理的方法;最后利用小波理论进行小波变换重构信号,更准确的提取了HSK刀柄的振动信号,为今后的科学研究和HSK综合测量系统的开发提供了很好的依据.     

HSK刀柄不平衡引起振动信号的特征分析

介绍了不平衡振动的机理;分析了HSK刀柄产生不平衡的原因和振动信号的特征;阐述了信号预处理的方法;最后利用小波理论进行小波变换重构信号,更准确的提取了HSK刀柄的振动信号,为今后的科学研究和HSK综合测量系统的开发提供了很好的依据。     

Automatic unbalance correction of rotors by sympathetic phase inversion of ultraviolet-curing resin

We developed an automatic unbalance correction technique for unbalanced rotors using a sympathetic phase inversion of ultraviolet (UV)-curing resin. Correcting the unbalanced weight during production is essential for various industrial rotors. The reduction of the cost and time of the correction process of unbalance during production is of great importance; however, the correction must have high precision, particularly for low-cost rotors such as polygon scanner motors and hard disks. In this paper we propose a new technique for correcting the unbalance of polygon scanner motors. Our system rotated a rotor on a translational oscillation table at a speed higher than that of the resonance rotation speed. The rotor exhibited runout opposing the unbalanced weight at this speed, and UV-curing resin preliminarily injected into the rotor also positioned itself opposite the unbalanced weight. UV light at this point cured the resin to immediately counter the unbalanced weight. Our experimental system confirmed the feasibility of this correction technique and reduced the unbalanced weight to 20% of that before the correction. This was in good agreement with the theoretical analysis.     

Identification of IPMC nonlinear model via single and multi-objective optimization algorithms

Ionic Polymer–Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder–Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.     

界面张力驱动的微粒自组装研究

界面张力驱动的液-液界面／液-固界面微粒自组装是一种新型的自组装方法，其主要原理是通过物理手段，使微粒在界面张力驱动作用下，聚集成二维有序结构。文中首先介绍了微粒自组装的基本原理和发展现状，而后对作者实现的亚毫米量级微粒自组装作了介绍，最后对实验结果进行了初步的理论分析。