多工序多工步制造系统切削用量的优化研究

通过对建立的多工序多工步制造系统生产时间和生产成本的数学模型的优化分析,并考虑了相应的生产条件约束限制,从而提出了基于最大生产率多工序多工步有约束制造模型切削用量优化求解思想--主目标生产效率最大,次目标生产费用最小,并给出了有效的优化算法.     

面向能效的多工步数控铣削工艺参数多目标优化模型

为实现数控平面铣削工艺参数和工步数的节能优化,对多工步数控平面铣削加工过程能量构成进行分析,引入机床、刀具和加工质量约束,综合考虑工艺参数和工步数的关系,建立了以主轴转速、进给量、背吃刀量、铣削宽度和工步数为优化变量,以能量效率和加工成本为优化目标的多工步数控平面铣削工艺参数多目标优化模型。应用基于自适应网格的多目标粒子群算法对模型进行求解。以某夹具多工步数控平面铣削工艺参数优化为例,验证了模型的有效性和实用性。     

基于STEP-NC加工工步的优化

研究了基于精英选择遗传算法的加工工步序列优化技术。传统的加工工艺路线是以工序为制造单元,而在基于STEP-NC的加工中,是以加工工步为制造单元,并且面向数控加工中心的一种加工过程,由加工工步变换所带来的零件转位、刀具更换等所消耗的辅助加工时间为最短作为优化目标,来进行加工工步序列的优化,并对加工工步序列的优化算法进行了验证。     

基于瓶颈工序的多资源多目标机械加工车间调度研究

为求解多资源约束的机械加工车间调度问题,建立了包括最大完工时间、平均流经时间、总拖期惩罚和生产成本在内的多目标优化模型,并结合免疫遗传算法和约束理论提出了一种基于瓶颈工序的机械加工车间调度算法。算法依据约束理论提出了一种基于工序的多参数级联编码方法和基于鼓-缓冲器-绳索(DBR)的四阶段解码方法,以及有效的交叉、变异操作。基于瓶颈工序的免疫操作及基于浓度的选择更新机制,保证了多目标优化问题的收敛性以及Pareto解集的多样性。仿真结果表明了该算法的可行性和有效性。     

基于改进NSGA-Ⅱ的非紧密衔接工序车间多目标调度研究

为解决实际生产中非紧密衔接工序的多目标车间调度问题,基于现有多目标调度算法(NSGA-Ⅱ)提出一种新的针对PARETO解集的评价方法。分析工序间的衔接约束,定义工序衔接系数和工序衔接过度的概念;设计了工序衔接系数的数学模型,并将其融入算法流程作为种群筛选依据;设计了新的精英保留策略,在算法迭代过程中即排除了工序衔接过度解;以最大完工时间最小和加工成本最小为目标,构建两个目标的柔性作业车间调度模型;最后以某汽车零部件制造车间的生产过程为例,将其与应用该方法之前的调度结果做了对比分析,结果表明该方法的调度结果更优且更符合生产实际,证实了该方法的可行性和有效性。     

考虑工序刚性约束的自动化装配生产线多目标优化研究

为了对具有工序刚性约束的装配线进行优化配置,提出一种基于并行工位设计和装配序列规划的自动化装配生产线多目标优化方法,主要包括:①针对工序刚性约束的自动化装配线的特殊性,搭建了以成本和生产节拍为多目标的装配线优化数学模型;②针对数学模型中决策变量的离散性特点,提出基于随机键的两段式编/解码方法,综合考虑了装配工艺中的优先关系、直接优先关系、完全分配约束和最大并行度限制等,实现了对工序刚性约束的描述;③将基于拥挤度的Pareto评价方法嵌入回溯搜索优化算法,实现了工序刚性约束下的装配线多目标优化;④通过断路器自动化装配线进行案例验证。结果表明,在装配生产线上各作业元素时间和设备单元成本固定且均已知的条件下,所提出的方法能够为该类装配线计算出最优配置方案,且将计算出的优化配置方案(成本517.5万元,节拍3.2秒/级)与现有方案(成本636.0万元,节拍5.4秒/级)对比,发现生产效率提高了40.7%,装配线成本降低了18.6%。     

航空发动机整体机匣铣-车复合加工工艺优化

针对加工过程中的多次工件转位和刀具更换对加工质量和生产效率的重要影响,在分析工步排序和合理确定辅助时间的基础上,建立了以最短辅助时间为目标函数的工步排序优化模型,利用自适应遗传算法完成工艺规划的全局优化决策,获得了最佳工步排序和最短辅助时间.优化过程中,为了使工步序列满足工艺约束规则,首先根据工艺约束规则确定工步优先关系矩阵,然后利用优先关系矩阵对工步序列进行校验和调整.通过工步优化,有效地降低了发动机机匣铣-车复合加工过程中的辅助时间,达到了提高生产效率和加工质量的目的.     

基于TOC的轴加工工艺均衡优化

任何生产系统中的瓶颈工序就好比是一个链条中最薄弱的环节，它制约着生产产出，属于制造系统中“关键因素”。基于此，以配气轴工序生产现状为研究对象，提出并实施了基于TOC约束理论对配气轴加工制造工序进行动态均衡的优化方案。结合配气轴加工工艺识别出瓶颈工序，通过分析瓶颈工序作业内容，对标基准节拍，制定并实施优化方案，从而消除瓶颈工序影响，以实现工序间生产均衡并提高成品生产产量。     

基于遗传算法的加工中心工步排序优化方法

在加工中心加工复杂零件时，工步多，其顺序直接影响加工质量和生产效率。在分析工步排序原则和典型工艺路线的基础上，建立了一个加工中心工步排序优化模型；将遗传算法应用到工步排序优化中，对算法的应用机理进行了详细讨论，提出了一种启发式基因重组方法；并示例验证了工步排序优化方法。     

知识窗

正工步(step of an operation;operation element)加工表面、切削刀具、主轴转速和进给量都保持不变所进行的工作,称为一个工步,是工序的一个组成部分。一个工序可以有一个或多个工步。工艺性(processability)所设计的零、部件,在保证其使用性能的前提下,根据具体的生产条件,能否用生产率高,劳动量小多材料消耗少和生产成本低的方法制造出来的特性。     

Multi-objective optimization of machining parameters considering energy consumption

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product.     

Robot actuation using air motors

In view of the high investment and tooling cost of a CNC machining centre, the cutting and idle times should be optimised by considering the tool consumption and the non-machining time cost components. In this paper, we propose a detailed mathematical model for the operation of a CNC machine tool which includes the system characterisation, the cutting conditions and tool life relationship, and related constraints. This new module will be a part of an overall computer-aided process planning system to improve the system effectiveness and to provide consistent process plans. A hierarchical approach is presented for finding tool-operation assignments, machining conditions, appropriate tool magazine organisation and an operations sequence which results in the minimum production cost.     

小型工件加工过程中切削参数的影响分析

切削参数的选择对加工效率、加工质量和生产成本均有重要影响。通过对复合机床加工小型工件的工序分析,计算出工序中不同切削参数作用下产生的切削力以及切削热;利用ABAQUS建立有限元模型,分析切削热、切削力对工件形变的影响,最终实现切削参数的合理选择。     

An economic machining process model with interval parameters

The primary objective of a machining economics model is to determine the optimal cutting parameters that minimize production costs while satisfying some design constraints. When the parameters in a machining economics model have interval values, the associated problem becomes an interval machining economics problem, and the objective value will also have interval value; that is, lying in a range. This paper develops a solution method that is able to derive the interval unit production cost of a machining economic model with interval parameters. A pair of two-level machining economics problems is formulated to calculate the upper bound and lower bound of the unit production cost. Based on the duality theorem, the two-level machining economics problem is transformed into the one-level conventional geometric program. Solving the corresponding pair of geometric programs produces the interval of the unit production cost. The results indicate that the cost interval contains more information for making decisions.     

加工中心一次走刀立铣切削参数优化的研究

研究了加工中心一次走刀立铣的切削用量优化。基于最大生产率和最低加工成本建立了优化数学模型，并使用遗传算法进行优化求解，最后通过实验验证了该优化方法具有比手册数据更好的经济性、安全性。     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

数控加工中心刀具信息集成技术的研究与开发

本文研究开发的数控加工中心机床刀具信息集成技术，是一个集成化的管理系统软件，可以实现加工中心模块式组合刀具库的管理、多标准刀具系统刀具及切削参数选择的功能，解决了工艺设计中的自动配刀、刀具管理集成和工艺文件自动输出等问题。     

Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques

Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for turning operations with constant diameters only. All Computer Numerical Control (CNC) machines produce the finished components from the bar stock. Finished profiles consist of straight turning, facing, taper and circular machining.This research work concentrates on optimizing the machining parameters for turning cylindrical stocks into continuous finished profiles. The machining parameters in multi-pass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost.In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint, tool tip temperature, etc. Due to high complexity of this machining optimization problem, six non-traditional algorithms, the genetic algorithm (GA), simulated annealing algorithm (SA), Tabu search algorithm (TS), memetic algorithm (MA), ants colony algorithm (ACO) and the particle swarm optimization (PSO) have been employed to resolve this problem. The results obtained from GA, SA,TS, ACO, MA and PSO are compared for various profiles. Also, a comprehensive user-friendly software package has been developed to input the profile interactively and to obtain the optimal parameters using all six algorithms. New evolutionary PSO is explained with an illustration .     

Optimization of multi-pass turning using particle swarm intelligence

This paper proposes a methodology for selecting optimum machining parameters in multi-pass turning using particle swarm intelligence. Often, multi-pass turning operations are designed to satisfy several practical cutting constraints in order to achieve the overall objective, such as production cost or machining time. Compared with the standard handbook approach, computer-aided optimization procedures provide rapid and accurate solutions in selecting the cutting parameters. In this paper, a non-conventional optimization technique known as particle swarm optimization (PSO) is implemented to obtain the set of cutting parameters that minimize unit production cost subject to practical constraints. The dynamic objective function approach adopted in the paper resolves a complex, multi-constrained, nonlinear turning model into a single, unconstrained objective problem. The best solution in each generation is obtained by comparing the unit production cost and the total non-dimensional constraint violation among all of the particles. The methodology is illustrated with examples of bar turning and a component of continuous form.