Energy-efficient machining systems: a critical review

Sustainable machining as a critical part in sustainable manufacturing has been valued by manufacturing enterprises of all sizes. The traditional short-term financial considerations are substituted by longer-term sustainable strategies to ensure the competitiveness, and ultimately, the survival of the company. Energy-efficient machining system, which promotes sustainable machining, is the focus of this paper. The energy-efficient machining system requires a comprehensive understanding as well as optimisation of energy consumption. Literature in this field is carefully reviewed and summarised. Energy consumption models, which are regarded as the core of the energy-efficient machining systems, are grouped into four categories, i.e. theoretical, empirical, discrete event-based, and hybrid models. Then, energy optimisation methodologies and strategies are discussed for energy-efficient process planning and production scheduling. The applications such as tool condition monitoring can employ energy information as useful input. Research inspired by energy-efficient machining studies is briefly introduced. The main elements of an individual energy-efficient machining system are then summarised. Discussions, research suggestions, and future directions are given at the end.     

Coupling evaluation for material removal and thermal control on precision milling machine tools

Machine tools are one of the most representative machining systems in manufacturing. The energy consumption of machine tools has been a research hotspot and frontier for green low-carbon manufacturing. However, previous research merely regarded the material removal (MR) energy as useful energy consumption and ignored the useful energy consumed by thermal control (TC) for maintaining internal thermal stability and machining accuracy. In pursuit of energy-efficient, high-precision machining, more attention should be paid to the energy consumption of TC and the coupling relationship between MR and TC. Hence, the cutting energy efficiency model considering the coupling relationship is established based on the law of conservation of energy. An index of energy consumption ratio of TC is proposed to characterize its effect on total energy usage. Furthermore, the heat characteristics are analyzed, which can be adopted to represent machining accuracy. Experimental study indicates that TC is the main energy-consuming process of the precision milling machine tool, which overwhelms the energy consumption of MR. The forced cooling mode of TC results in a 7% reduction in cutting energy efficiency. Regression analysis shows that heat dissipation positively contributes 54.1% to machining accuracy, whereas heat generation negatively contributes 45.9%. This paper reveals the coupling effect of MR and TC on energy efficiency and machining accuracy. It can provide a foundation for energy-efficient, high-precision machining of machine tools.     

无心磨削过程碳排放量及其关键影响因素解析

准确量化制造过程中的碳排放量是实现低碳制造的前提条件。磨削是一种广为应用的精密加工方法,同时也是切削热量高、使用切削液量大、加工环境差、能量利用效率低的加工工艺。利用公理化设计找出影响低碳磨削的各层关键影响因素,综合考虑了能源、资源和废弃物对碳排放量的影响,建立了数控粗磨、精磨和光磨整个磨削过程的ERWC碳排放量定量分析模型。最后进行了实际磨削加工测量实验,并用MATLAB给出了模型计算比较结果,解析了关键影响因素。     

面向绿色制造的干式齿轮加工过程碳排放分析

为了研究干式切削齿轮加工过程碳排放特性,对齿轮加工过程的物料、能源消耗、废物处理及碳排放特性进行了分析,提出了齿轮加工过程碳排放边界条件,建立了齿轮加工过程碳排放计算模型;根据某制造企业提供的数据,将同批次齿轮的绿色干式切削和湿式切削加工过程的碳排放量进行计算与对比分析,结果显示,与湿式切削相比,干式切削齿轮加工过程碳排放总量约少55.69%,废弃物处理碳排放量少15.57%,表明面向绿色制造的干式齿轮加工的能源消耗、成本费用较低且环保,高速干式齿轮加工技术将成为未来齿轮制造业发展的主要趋势。     

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.     

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.     

面向加工特征的工件制造过程能耗预测方法

在工件的制造过程中,具有多个特征的工件在加工时,其加工特征的能耗属性不同,为了研究在工件不同的加工特征对能耗的影响,创建了面向加工特征的工件制造过程能耗预测模型。首先,将工件看成单个加工特征的集合体,其次对工件特征切削功率的分析,创建有关切削功率的能耗数学模型;然后采用GM(1,1)模型完成工件加工时其切削功率的灰色建模,实现整个工件能耗的预测过程,最后通过对冲床顶支座的加工过程为例,说明了该模型的准确性。     

Research on selection strategy of machining equipment in cloud manufacturing

Cloud manufacturing (CM) is a new type of networked manufacturing model, which is proposed in 2010. Optimization technology is one of the key techniques for CM operation, which are used for the efficient integration of manufacturing resources. In all kinds of manufacturing resources, the machining equipment is one of the most important resources. Using optimization techniques to achieve optimal selection of machining equipment is rarely studied in the CM. In order to handle the optimization selection of machining equipment in CM, comparing with the existing resources optimal configuration, an optimal selection strategy is introduced for the machining equipment in CM. In the selection strategy, first, a multiple objective and binary integer programming model is proposed to describe the optimal selection of machining equipment in CM. Second, after analyzing the mathematical model and the real-world problem of the machining equipment selection in CM, the priority method is adopted to convert the multiple-objective problem into a single-objective problem. Third, an improved particle swarm optimization (IPSO) algorithm based on a novel encoding scheme and fitness function is presented to solve the single-objective mathematical model. Finally, the simulation experiments verify the effectiveness of the IPSO algorithm and show that the selection strategy is more objective and effective to help the client select the machining equipment in the CM than current resources optimization model. This research provides a theoretical support for the development of CM.     

An analysis of cutting-edge curves and machining performance in the Inconel 718 machining process

When considering the machining of materials used for aircraft components, the principal areas of interest usually include the manufacturing characteristics of the materials when they are machined with different cutting-edge curves, and the development of manufacturing processes that improve the machining precision, thereby reducing the time required to carry out secondary machining operations or error correction of the final component. A further area of concern is to develop manufacturing techniques that are capable of generating highly reliable aircraft components which ensure that flight safety is not compromised through component failure. This paper employs a Taguchi L9 experimental layout to investigate the optimal cutting parameters when machining Inconel 718 with the planar-type conical ball-end cutter, the S-type cutter, and the traditional conical ball-end milling cutter. The current results provide a valuable technical database for aircraft component manufacturers who are seeking to enhance their automatic manufacturing capabilities.     

面向高能效制造的数控机床发展展望

阐述了以知识为基础的高能效制造的涵义,探讨了在机床产品开发及应用中融入高能效制造理念,以提高资源效用与产品附加值。开展高能效制造技术研究将成为机床企业提高自主创新能力,增强其产品竞争力的有效途径。     

Developing a big data analytics platform for manufacturing systems: architecture,method, and implementation

Manufacturing industries have recently promoted smart manufacturing (SM) for achieving intelligence, connectedness, and responsiveness of manufacturing objects consisting of man, machine, and material. Traditional manufacturing platforms, which identify generic frameworks where common functionalities are shareable and diverse applications are workable, mainly focused on remote collaboration, distributed control, and data integration; however, they are limited to incorporating those characteristic achievements. The present work introduces an SM-toward manufacturing platform. The proposed platform incorporates the capabilities of (1) virtualization of manufacturing objects for their autonomy and cooperation, (2) processing of real and various manufacturing data for mediating physical and virtual objects, and (3) data-driven decision-making for predictive planning on those objects. For such capabilities, the proposed platform advances the framework of Holonic Manufacturing Systems with the use of agent technology. It integrates a distributed data warehouse to encompass data specification, storage, processing, and retrieval. It applies a data analytics approach to create empirical decision-making models based on real and historical data. Furthermore, it uses open and standardized data interfaces to embody interoperable data exchange across shop floors and manufacturing applications. We present the architecture and technical methods for implementing the proposed platform. We also present a prototype implementation to demonstrate the feasibility and effectiveness of the platform in energy-efficient machining.     

数控加工技术在馈线生产中的应用

本文着重阐述了数控加工技术在雷达产品馈线生产中的应用 ,结合现代雷达对元器件的结构、材料要求和现代制造业的发展趋势 ,论述数控加工技术的先进性和重要性以及发展方向。     

雕塑曲面体混流式叶片的多轴联动数控加工编程技术

转轮叶片是水轮机能量转换的关键部件,也是最难加工的零件,目前多轴联动数控加工是解决该类大型雕塑曲面零件最有效的加工方法。多轴联运数控加工编程则是实现其高精度的高效率加工的最重要环节。本文介绍混流式水轮叶片五轴联运数控加工大型雕塑曲面编程中涉及到转轮叶片三维造型、刀位轨迹计算、切削仿真、机床运动碰撞仿真、后置变换等关键技术。通过对这些技术的链接和研究,实现了大型叶片的多轴联动加工。     

面向快速制造的特种加工技术进展

特种加工是对传统机械加工方法的有力补充和延伸。在已有的特种加工工艺不断完善和定型的同时，新的特种加工技术也不断涌现出来，正在形成面向快速制造的特种加工技术新体系。本文分析总结了特种加工技术的构成及其最新的研究进展，并介绍了面向快速制造的特种加工技术新体系。     

高速加工及在线测量在挤压分流模精加工中的应用

利用高速加工中心及在线测量的高效、精密制造工艺,使铝型材挤压分流模的加工精度由±0.02mm提高到±0.008mm。     

磁场辅助加工的研究现状及其发展趋势

将高密度能量场(声、光、电、热、磁等)引入加工区域,辅助或直接形成材料去除是先进制造技术发展的一个重要方向。磁场辅助加工是能量场加工中出现较早的一个技术,其原位吸热和主动控制残余应力的特点,预示了这一技术在材料加工领域的巨大应用前景。从加工过程的磁致效应、工具/工件磁化加工、磁场辅助加工基本理论三个方面对磁场辅助加工的发展现状进行了综述,并对其未来的发展方向进行了展望。     

硬质合金孔加工刀具的技术进展

简要介绍了现代制造技术中常用孔加工刀具的种类、硬质合金刀具的材料与制备.以钻头为例,分析了刀具参数、刃磨、刃口钝化技术对切削性能的影响,综述了硬质合金孔加工刀具技术的发展趋势.     

液冷高功率铁氧体移相器的制造工艺

文中介绍了高功率铁氧体移相器的功能需求、组织结构及制造工艺特点,着重论述液冷移相器的制造工艺方案、制造工艺流程和关键工艺技术,通过工艺攻关,突破液冷移相器多项生产制造难题,实现细小管道液冷移相器的深孔加工和液冷流道无堵塞、无渗漏焊接,利用该工艺攻关成果研制的移相器制造成本低、生产效率高,已在某相控阵雷达中小批量装备使用。     

一种面向机械车间柔性工艺路线的加工任务节能调度方法*

大量研究表明机械车间消耗了大量能量,因此降低机械车间的能耗是实现可持续制造的策略之一。现有机械车间节能调度研究主要针对给定的或者具有部分柔性的工艺路线,缺乏对机械车间任务工艺路线多柔性的节能调度研究。针对机械车间任务柔性工艺路线对机械车间调度能耗的影响特性,提出一种面向机械车间柔性工艺路线的节能调度方法。首先,分析了面向机械车间柔性工艺路线的加工任务调度的能耗特性;基于此,构建了节能调度模型,该模型是以任务加工总能耗、加工完成时间、机床负载为目标。进一步提出了一种改进的Q学习算法对该模型进行求解获得其Pareto解。最后通过案例验证了提出模型的节能效果及算法的可行性。     

Knowledge-based system for assignment of parts to machine cells

This paper presents a decision tool for assignment of parts to machining cells. A modelling system and associated expert modules: analyser, synthesiser and recogniser have been developed to perform the automatic and dynamic assignment of parts directly from a feature-based CAD modelling system to manufacturing cells. It is based on the knowledge acquired from design features of parts and manufacturing processes. Feature-based representation of components structure and related machining operations is presented. The proposed algorithm allows the integration of CAD and CAM. Some elements of expert systems, pattern recognition and finite-state automata are applied in this research. Symbolic strings containing the design and machining attributes of components are analysed using knowledge rules and syntactic pattern recognition techniques, then cell inference and part assignment are carried out. A case study is included to illustrate the feasibility of the above approaches. This research, provides some useful contributions for both CAD and CAM integration as well as parts assignment for cellular manufacturing.