考虑刀具磨损的数控车削批量加工工艺参数节能优化方法

在数控车削批量加工过程中,刀具磨损的加剧会导致能量消耗显著增大.针对现有节能优化研究忽略了刀具磨损状态和工艺参数对能耗的协同影响的问题,提出一种考虑刀具磨损的数控车削批量加工工艺参数节能优化方法.首先,分析刀具磨损和工艺参数对数控车削批量加工能耗协同影响机理;基于此,以批量加工总能耗最低和完工时间最小为目标,以不同刀具磨损状态下的工艺参数为优化变量,建立了考虑刀具磨损的数控车削批量加工工艺参数节能优化模型,并采用多目标模拟退火算法进行优化求解.与经验方案相比,通过算法优化得到的加工方案总能耗降低14.9％,总时间降低9.8％,验证了该方法的有效性和实用性.     

柔性作业车间能耗优化研究

为了实现机械加工车间的节能优化运行,建立了考虑运输时间的节能柔性作业车间调度模型。使用基于动素的机床能耗模型预测生产过程中的机床能耗,并利用遗传算法实现了模型的求解。柔性作业车间环境下,该模型能够从能耗出发,为各加工任务选择合适的加工机床,同时生成优化的生产调度方案。最后,案例研究验证了建立的模型的节能效果和有效性。     

面向能耗的多刀具孔加工刀具直径及工艺参数集成优化模型

刀具直径和工艺参数对机床加工能耗影响显著。与单独优化刀具直径或单独优化工艺参数相比,开展刀具直径及工艺参数集成优化能进一步降低机床加工能耗。为实现面向低能耗的多刀具孔加工过程中刀具直径及工艺参数集成优化,首先,系统地分析了多刀具孔加工过程的加工时间和加工能耗;然后,建立以刀具直径和工艺参数为优化变量,以最小加工能耗和加工时间为优化目标的多刀具孔加工多目标集成优化模型,并采用粒子群算法对模型进行优化求解;最后,基于实际案例分析了刀具直径及工艺参数集成优化的必要性,并通过对比分析,验证了该模型的有效性和实用性。     

基于遗传算法的冲压车间节能调度优化研究

金属加工行业中,冲压成形能量消耗相对较大,因此,对冲压成形加工开展低碳节能的研究具有重要的实际意义。为解决冲压车间多目标节能调度问题,在深入分析低碳作业车间调度和某冲压车间生产现状的基础上,结合冲压生产过程中的能耗特性分布曲线,建立以最小化最大完工时间、加工总成本和加工总能耗为目标的冲压车间多目标节能调度优化模型,并设计了基于多层编码的遗传算法求解该模型。最后,结合某企业的冲压车间生产实际进行案例研究得出,冲压车间多目标节能调度优化模型在一定程度上可降低冲压车间加工总能耗,同时使得最大完工时间和成本较优,验证了模型和算法的可行性和有效性。     

考虑扰动事件的机械加工工艺参数与车间动态调度综合节能优化

随着能源消耗和环境问题的不断加剧,机械加工车间的高效节能生产越来越受到制造业的关注。传统动态调度优化时每道工序的工艺参数固定,未考虑工艺参数与车间调度之间的关联关系,限制了调度优化的潜力。为了更好地实现柔性作业车间节能增效,并快速有效地应对车间生产过程中出现的突发扰动事件,提出一种考虑扰动事件的加工工艺参数与车间动态调度综合优化方法。首先详细分析订单插入与机床故障下柔性作业车间的能耗特性,以总能耗与最大完工时间为目标,建立工艺参数与动态调度综合优化模型,然后设计一种面向扰动事件的动态决策机制,并提出改进的自适应形状估计进化算法(AGE-MOEA)进行优化求解,最后通过案例分析与算法对比,验证了所提出方法的有效性。     

考虑设备预维护的柔性作业车间调度节能优化方法

随着能源成本的飙升和环境日益恶化,实现节能生产的策略越来越受到制造企业的关注.在车间实际生产调度过程中,机床设备的故障与维护会影响车间调度方案的顺利执行,进而导致出现车间加工资源冲突、能耗增大和完工时间延长等问题.为减少实际柔性作业车间加工过程中出现由于机床故障而导致加工中断的情况,提出一种考虑设备预维护的柔性作业车间调度节能优化方法.分析考虑设备预维护的柔性作业车间能耗特性,建立考虑设备预维护的柔性作业车间调度节能优化模型,设计机床预维护策略,并在此基础上提出一种启发式框架下的调度算法对节能优化模型进行求解,通过不同方案的对比分析,验证所提方法的有效性.     

车削刀具几何参数低碳优化建模与求解

为了实现车削加工过程的低碳化并提高加工质量,在分析车削加工过程中能耗特征及切削温度与刀具几何参数之间关系的基础上,以刀具前角和主偏角为优化变量,考虑加工过程机床设备和加工质量等约束,建立了车削加工刀具几何参数低碳优化模型;针对所建模型,提出一种基于改进的自适应遗传算法的优化求解方法。实例分析表明,以低碳排放为优化目标、以切削温度为约束条件优化车削刀具几何参数,可使碳排放和切削温度比原刀具参数分别降低12%和17%。所建模型和方法可为制造企业优化选择刀具几何参数降低碳排放提供理论方法支持。     

基于机床加工匹配特性的混合流水车间绿色生产调度

针对混合流水车间绿色生产过程中的设备选择和调度目标匹配问题,提出基于机床加工特性的多目标调度模型和改进遗传算法。该算法建立了混合流水车间调度的时间、能耗与成本优化模型,采用模糊隶属方法描述了机床加工特性,在遗传算法求解过程中通过机床加工特性隶属度与调度目标的权重系数匹配关系,建立了自适应的交叉、变异和优势保留策略,在每一代迭代中提高在调度目标方向上的选择压力,加速收敛。通过实例分析对比了不同算法的优化结果,从而验证了模型及算法的有效性,并提出了高效、节能、经济和综合4种调度生产模式,为混合流水车间绿色生产提供了指导。     

基于动态事件的柔性作业车间重调度节能优化

为了解决机械加工过程中出现紧急任务插单或机床故障等动态事件干扰调度方案执行,导致原调度方案能耗增高、完工时间延长等问题,研究了一种考虑紧急插单和机床故障动态事件的柔性作业车间重调度节能优化方法。首先分析了动态事件下柔性作业车间的工件加工过程能耗特性,然后以总能耗最低、完工时间和鲁棒性最小为优化目标建立了面向节能的动态重调度优化模型,并提出基于多目标引力搜索算法的重调度节能优化求解方法,最后通过案例对比分析,验证了所提方法的有效性。     

面向高效节能的数控滚齿加工参数多目标优化模型

数控滚齿加工工艺是应用较为广泛的齿形加工方式,合理的加工参数选择能够显著影响加工过程能耗和加工时间。为降低滚齿加工过程能耗和加工时间,提出一种面向高速干切的高效节能数控滚齿加工参数多目标优化模型。首先,系统地分析了数控滚齿加工过程的能耗特性,建立了数控滚齿加工过程能耗模型;然后,以数控滚齿加工的滚刀转速和轴向进给量为优化变量,建立了以最低能耗和最少加工时间为目标的多目标优化模型,并采用帝国竞争算法对模型进行优化求解;最后,以某车间的齿轮加工过程为应用案例,验证了该模型的有效性和实用性。     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.     

Energy saving design of the machining unit of hobbing machine tool with integrated optimization

The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase. The optimization design is a practical means of energy saving and can reduce energy consumption essentially. However, this issue has rarely been discussed in depth in previous research. A comprehensive function of energy consumption of the machining unit is built to address this problem. Surrogate models are established by using effective fitting methods. An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models, and the parameters of the motor and structure are considered simultaneously. Results show that the energy consumption and tool displacement of the machining unit are reduced, indicating that energy saving is achieved and the machining accuracy is guaranteed. The influence of optimization variables on the objectives is analyzed to inform the design.     

高速干切滚齿机床能耗分布特性及其预测模型

针对高速干切滚齿机床的能耗优化问题,对变工艺参数下的机床能耗分布特性及其预测模型进行了研究.首先,以机床能耗元件为研究对象,建立了高速干切滚齿机床功率模型;然后,基于机床电路分布原理,通过滚齿实验对所建模型进行了分析验证,揭示了变工艺参数与机床不同部位的功率和能耗之间的关系;最后,构建了高速干切滚齿机床能耗预测模型.     

Optimization of machining economics and energy consumption in face milling operations

Metal cutting (or machining) is one important aspect of the manufacturing system. Selecting optimal cutting conditions for machining is then a crucial process planning task for manufacturing. Traditionally, solving such machining problems was only focused on economic objectives such as maximizing profit or minimizing production time requirement. In the recent decade, however, minimizing energy consumption in manufacturing processes has attracted increased attention due to increasing energy costs and concern with greenhouse gas emissions. Energy loss could be avoided by carefully selecting cutting parameters. This paper develops a multi-objective mathematical model to minimize unit production costs along with energy consumption for face milling operations. In addition, an evolutionary strategy (ES)-based optimization approach is used to identify optimal cutting conditions for the proposed model.     

An energy consumption prediction approach of die casting machines driven by product parameters

Die casting machines, which are the core equipment of the machinery manufacturing industry, consume great amounts of energy. The energy consumption prediction of die casting machines can support energy consumption quota, process parameter energy-saving optimization, energy-saving design, and energy efficiency evaluation; thus, it is of great significance for Industry 4.0 and green manufacturing. Nevertheless, due to the uncertainty and complexity of the energy consumption in die casting machines, there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration. To fill this gap, this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters. Firstly, the system boundary of energy consumption prediction is defined, and subsequently, based on the energy consumption characteristics analysis, a theoretical energy consumption model is established. Consequently, a systematic energy consumption prediction approach for die casting machines, involving product, die, equipment, and process parameters, is proposed. Finally, the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products. The results show that the prediction accuracy of production time and energy consumption reached 91.64% and 85.55%, respectively. Overall, the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.     

蜗杆砂轮磨齿加工参数优化

以蜗杆砂轮磨削加工20CrMnTi齿轮为研究对象,选择均匀设计试验法,研究磨削参数（砂轮线速度vs、砂轮沿齿轮轴向进给速度vw、磨削厚度ap）对齿面粗糙度的影响。采用二级逐步回归方法建立磨削参数与齿面粗糙度的回归模型,构建了以加工效率、齿面粗糙度为多目标的优化模型,采用粒子群优化算法对磨削参数进行了优化。试验结果表明,使用优化后的磨削参数加工可以提高加工效率、减小齿面粗糙度。     

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.     

Optimization of cutting parameters for energy saving

Correct selection of cutting parameters is one of effective approaches to achieve optimum machining process, including reducing energy consumption. For the close relationship between cutting parameters and energy consumption in machining process, energy consumed is modeled and to be reduced based on analyzing the energy consumption in this paper. According to the different requirements in roughing process and finishing process, corresponding multi-objective optimization functions are formulated considering energy consumption. Taking the optimization of milling operations on aluminum alloy as an example, experiments are carried out to analyze the energy consumption and production rate with sets of optimized/un-optimized cutting parameters for different objectives. The experimental results show that the objectives of low consumed energy and high production rate can be simultaneously achieved by optimization of cutting parameters.     

基于低能耗的平面端铣削粗/精加工参数全局多目标优化

以平面端铣削粗/精加工过程为研究对象,根据变频调速铣床主轴电机运行特点,提出分段空载功率函数模型,构建铣床粗/精加工总能耗模型及总生产率模型,并通过相应实验对模型进行完善。结合实际生产建立粗/精加工过程约束条件。选取企业实际生产案例,采用遗传算法对选定加工特征的粗/精端铣削过程进行全局多目标优化,并与传统只针对粗加工或精加工的局部铣削参数优化进行比较。实验结果表明,与优化前相比,局部优化后的粗/精加工总能耗下降了12.51%,全局优化后的粗／精加工总能耗下降了15.04%;局部优化后总加工时间缩短了18.48%,全局优化后总加工时间缩短了21.26%。