专用刀具在高效铣加工技术上的应用

本文着重介绍了两种转包机匣类零件高效铣削加工的工艺改进过程,对高速铣削所采用的刀具和切削参数的优化进行了详细的分析,对高效铣削中所遇到的问题和所采取的对策进行了较为详细的阐述,对数控程序、加工参数、刀具选择等方面进行研究,达到高效加工的目的,提高零件加工的效率和降低加工成本。     

基于犹豫模糊语言信息的权重信息部分可知TOPSIS方法

基于犹豫模糊集和语言变量,给出犹豫模糊语言变量的定义.采用犹豫模糊语言变量建模决策过程中的不确定信息,在考虑属性权重信息部分可知的情况下,提出了一个新的多属性决策方法.首先,利用犹豫模糊语言变量给出方案的评价值.然后,采用极大偏差法建立线性规划模型确定属性权重向量.最后,采用TOPSIS方法对方案进行排序,并且给出了具体的方案排序方法.为了说明新方法的可行性和有效性,我们将其应用到地铁壁画的评价上.实例表明新方法确实是一个简单有效的方法.     

基于机床刀具加工变形研究的铣削工艺参数优化方法

针对机床刀具变形影响加工精度问题,据铣床刀具系统动刚度测量计算及铣削力模型提出基于遗传算法的铣削工艺参数优化方法。通过阶跃响应实验获得机床刀具系统动刚度及用正交实验法可准确铣削力模型,对实验机床铣削工件工艺参数进行数学优化;开发设计在线测量刀具变形及铣削力实验装置并分别对优化前后工艺参数实验验证,结果已证明该方法的有效性、实用性。     

物流信息平台业务协同响应控制关键信息研究

为了提高基于物流信息平台发现的物流业务协同响应的效率，优化设计对应的决策支持模块，提出研究影响物流业务交易方响应决策的关键信息。通过构建物流业务协同响应的决策系统，从决策者、决策任务和环境3个方面识别出知识水平等17个潜在影响信息。应用AHP、DEMATEL及AHP-DEMATEL方法，分别求解各信息权重值来确定其重要程度，基于序号总和理论检验得到最好排序，按累积权重超过85%的信息为关键信息的原则，基于最好排序和3种方法共识的关键信息因素的交集，得到物流业务协同响应控制关键影响信息为边际收益/成本、业务匹配度等6个信息，大幅度减少物流业务控制/决策系统的设计工作量。     

基于KPCA-WOA-ELM的爆破飞石距离预测

为提高爆破飞石距离预测的精度和效率，构建了一种基于核主成分分析法（KPCA）和鲸鱼算法（WOA）优化的极限学习机（ELM）爆破飞石距离预测模型。以国内某露天煤矿爆破工程为例，选取影响爆破飞石距离的7个因素。通过KPCA对影响因素间非相关性关系进行降维，提取出包含原始信息95.76%的4个主成分作为模型输入。然后,采用WOA对ELM进制参数寻优，避免了局部最优解问题。结果表明，KPCA-WOA-ELM模型的平均相对误差、均方根误差R_MSE、决定系数R²和平均绝对误差R_MAE分别为4.271%、6.681、0.985和6.413，均优于对比模型。说明该模型可实现对爆破飞石距离的准确预测，为确定爆破作业中的爆破安全区提供依据。     

基于犹豫模糊语言术语的供应商多准则群决策研究

针对全球价值链环境下供应商科学决策问题,提出基于不确定语言术语的多准则群决策模型。首先分别提取专家的偏好信息,将偏好信息转化为犹豫模糊语言术语,引入不确定语言变量进行词计算;其次,运用包络算子融合专家的偏好信息形成犹豫模糊语言术语集,设计集成准则权重的相对贴近度进行产品供应商排序,确定最满意供应商;此外,引入信息熵求解决策过程无先验知识的多准则权重;计算结果表明：3种信息熵参数条件下最满意汽车零部件供应商选择结果完全一致,基于相对贴近度值的供应商优劣排序结果相对于信息熵参数变化不敏感;验证了所提模型可行性、有效性和稳定性,为汽车零部件供应商的实际评价与选择提供有益借鉴。     

基于MPA-SVM的煤矿抛掷爆破爆堆形态预测

为提高爆堆形态预测精度，提出了一种海洋捕食者算法（MPA）优化支持向量机（SVM）的方法，结合黑岱沟露天煤矿爆破工程数据，选取其中8个参数作为影响爆堆形态的输入参数，松散系数ξ和Weibull函数的2个控制变量α、β为输出参数，建立基于MPA-SVM的爆堆形态预测模型，并与同期使用的5个模型进行比较。结果表明:MPA-SVM的预测效果优于其他5个模型，相对误差未超过5%，3个评价指标分别为R₂（0.955,0.978,0.946），R_MSE（0.063,0.075,0.116），R_MAE（0.046,0.056,0.067），证明了MPA-SVM对爆堆形态预测的适用性，且在小样本数据条件下更具有精度优势。     

基于混合改进TOPSIS的装备供应商选择方法

针对评价值为混合信息、专家权重未知的装备供应商选择问题,提出一种新的供应商选择决策方法。将语义评价信息转化为有犹豫度差异的直觉模糊数,根据专家评价犹豫度和相似度确定专家权重,并利用混合改进TOPSIS方法集结评价值,克服了数据类型的不可公度性,使评价结果更为客观。算例表明该方法具有良好的稳定性和合理性,同时考虑了专家评价差异性和不确定性对排序结果的影响,能够为装备采购决策提供有效参考。     

五轴联动精密微铣削机床试验模态分析

五轴联动精密微铣削机床的结构性能直接影响精密三维微小零件的加工质量,为研究其结构性能,应用试验模态分析技术对五轴联动精密微铣削机床进行结构分析。采用单点激励多点响应的方法进行锤击试验,建立传递函数,经分析处理得出机床固有频率、阻尼比与振型等动态特性参数,并对识别的参数进行模态置信矩阵(MAC)校验。试验得到机床前三阶固有频率为102.44,133.97,154.86 Hz,对应阻尼比为3.27%、4.94%、2.14%;试验结果证明该五轴精密微铣削机床结构布局合理,机床固有频率有效避开微切削加工的激振频率,通过振型分析可找出机床结构薄弱环节,并提出优化建议。试验验证应用试验模态分析技术对机床动态特性分析方法的有效性。     

宽频带多重动力吸振器薄壁件铣削振动控制

薄壁零件在整个铣削加工过程中由于材料去除壁厚发生变化而导致其振动频率发生迁移,具有明显时变振动的特点。为了对整个铣削加工过程中振动的有效控制,提出了一种新型的宽频带分布式多重动力吸振器。在零件每个模态阵型敏感点位置处布置多组动力吸振器贴紧排布,令各动力吸振器固有频率不相等,以实现时变切削振动的最优控制。此外,各动力吸振器的设计参数采用全局优化方法进行设计,以壁厚连续变化过程中各敏感点频率响应函数幅值最小化为优化目标函数,通过有限元软件中的目标驱动优化模块得到各吸振器的最佳参数。最后对装有多组吸振器的薄壁零件不同壁厚不同位置点的频响幅值进行分析,验证该方法的有效性。结果表明,该动力吸振器一次安装调整即可实现零件铣削加工全过程中时变切削振动的有效控制,零件的频响函数幅值整体降低85%以上。     

A Cutting Parameter Optimization System Design Based on Mathematical Models and Databases of Parameters

To solve the problem of difficulty in selecting NC cutting parameters by the redundancy technique, a method is put forward to optimize cutting parameters based on a revolutionary mathematical model and a revolutionary cutting parameters database. By use of fuzzy inference rules, it can not only make the method itself evolved and updated, but also ensure data to be correct and feasible from the two optimization routes. Practical running and testing proved that this method can facilitate for the user to select parameters and greatly improve the processing efficiency.     

Application of grey relational analysis based on Taguchi method for optimizing machining parameters in hard turning of high chrome cast iron

High chrome white cast iron is particularly preferred in the production of machine parts requiring high wear resistance. Although the amount of chrome in these materials provides high wear and corrosion resistances, it makes their machinability difficult. This study presents an application of the grey relational analysis based on the Taguchi method in order to optimize chrome ratio, cutting speed, feed rate, and cutting depth for the resultant cutting force (F_R) and surface roughness (R_a) when hard turning high chrome cast iron with a cubic boron nitride (CBN) insert. The effect levels of machining parameters on F_R and R_a were examined by an analysis of variance (ANOVA). A grey relational grade (GRG) was calculated to simultaneously minimize F_R and R_a. The ANOVA results based on GRG indicated that the feed rate, followed by the cutting depth, was the main parameter and contributed to responses. Optimal levels of parameters were found when the chrome ratio, cutting speed, feed rate, and cutting depth were 12%, 100 m/min, 0.05 mm/r, and 0.1 mm, respectively, based on the multiresponse optimization results obtained by considering the maximum signal to noise (S/N) ratio of GRG. Confirmation results were verified by calculating the confidence level within the interval width.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-018-0231-z     

An overview on the applications of the hesitant fuzzy sets in group decision-making: theory,support and methods

Due to the characteristics of hesitant fuzzy sets (HFSs), one hesitant fuzzy element (HFE), which is the basic component of HFSs, can express the evaluation values of multiple decision makers (DMs) on the same alternative under a certain attribute. Thus, the HFS has its unique advantages in group decision making (GDM). Based on which, many scholars have conducted in-depth research on the applications of HFSs in GDM. We have viewed lots of relevant literature and divided the existing studies into three categories: theory, support and methods. In this paper, we elaborate on hesitant fuzzy GDM from these three aspects. The first aspect is mainly about the introduction of HFSs, HFPRs and some hesitant fuzzy aggregation operators. The second aspect describes the consensus process under hesitant fuzzy environment, which is an important support for a complete decision-making process. In the third aspect, we introduce seven hesitant fuzzy GDM approaches, which can be applied in GDM under different decision-making conditions. Finally, we summarize the research status of hesitant fuzzy GDM and put forward some directions of future research.     

Hesitant fuzzy programming technique for multidimensional analysis of hesitant fuzzy preferences

The linear programming technique for multidimensional analysis of preferences (LINMAP) is the most representative method for handling the multiple criteria decision making (MCDM) problems with respect to the preference information over alternatives. This paper utilizes the main structure of LINMAP to develop a novel hesitant fuzzy mathematical programming technique to handle MCDM problems within the decision environment of hesitant fuzzy elements (HFEs). Considering the hesitancy of the decision maker, both the pair-wise comparison preference information over alternatives and the evaluation information of alternatives with criteria are represented by the HFEs. Based on the incomplete pair-wise preference judgments over alternatives, we propose the concepts of the hesitant fuzzy consistency and inconsistency indices. Furthermore, we construct a hesitant fuzzy mathematical programming model to derive the weights of criteria and the positive-ideal solution. In this hesitant fuzzy programming model both the objective function and partial constraints’ coefficients take the form of HFEs, and an effective approach based on the ranking method of HFEs is further developed to solve the new derived model. To address the incomplete and inconsistent preference structures of criteria weights, we introduce several deviation variables and establish the bi-objective nonlinear programming model. At length, we employ a green supplier selection problem to illustrate the feasibility and applicability of the proposed technique and conduct a comparison analysis to validate its effectiveness.     

Prediction of cutting force based on machining parameters on AL7075-T6 aluminum alloy by response surface methodology in end milling

Cutting forces modeling is the basic to understand the cutting process, which should be kept in minimum to reduce tool deflection, vibration, tool wear and optimize the process parameters in order to obtain a high quality product within minimum machining time. In this paper a statistical model has been developed to predict cutting force in terms of geometrical parameters such as rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Response surface methodology experimental design was employed for conducting experiments. The work piece material is Aluminum (Al 7075-T6) and the tool used is high speed steel end mill cutter with different tool geometry. The cutting forces are measured using three axis milling tool dynamometer. The second order mathematical model in terms of machining parameters is developed for predicting cutting forces. The adequacy of the model is checked by employing ANOVA. The direct effect of the process parameter with cutting forces are analyzed, which helps to select process parameter in order to keep cutting forces minimum, which ensures the stability of end milling process. The study observed that feed rate has the highest statistical and physical influence on cutting force.     

Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process

Multi-pass milling is a common manufacturing process in practical production. Parameter optimisation is of great significance since the parameters largely affect the production time, quality, cost and some other process performance measures. However, the parameter optimisation of the multi-pass milling process is a nonlinear constrained optimisation problem. It is very difficult to obtain satisfactory results by the traditional optimisation methods. Therefore, in this paper, a new optimisation technique based on the electromagnetism-like mechanism (EM) algorithm is proposed to solve the parameter optimisation problem in a multi-pass milling process. The EM algorithm is a population based meta-heuristic algorithm for unconstrained optimisation problems. As the parameter optimisation problem is a constrained problem, the proposed approach handles the constraints of the problem by improving the charge calculation formula combined with the feasibility and dominance rules at the same time. This paper also puts forward flexible cutting strategies to simultaneously optimise the depth of cut for each pass, cutting speed and feed to improve solutions. A case study is presented to verify the effectiveness of the proposed approach. The results show that the proposed method is better than other algorithms and achieves significant improvement.     

Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process

The machining factors affecting the tool wear and surface finish produced in the end milling process are generally the cutting speed, the feed rate, the depth of cut, etc. This paper describes a study that identifies the influence of the machining parameters on the groove width and the surface roughness average for the end-milling of high-purity graphite under dry machining conditions. The experiments are based on an orthogonal arrays and grey relational analysis method is then applied to determine an optimal machining parameter setting. The dimensional accuracy of the groove width and the surface roughness average are selected as the quality targets. In this study, the feed rate is the most significant controlled factors for the machining process according to the weighted sum grade of the Δ and the R_a.     

基于BP神经网络和FPA的高速干切滚齿工艺参数低碳优化决策

为解决高速干切滚齿工艺参数决策中存在的主观依赖性强和用时较长的问题,并实现滚齿加工低碳化,提出一种基于实例推理和优化算法的高速干切滚齿工艺参数低碳优化决策方法。利用反向传播(back propagation,BP)神经网络构建加工效果评价值的预测模型,通过改进K-means聚类算法获取待决策工艺问题的相似实例抽取集,以此构建待优化工艺参数约束,再运用花朵授粉算法(flower pollination algorithm,FPA),以碳耗最小为优化目标,获取待决策工艺问题的最优工艺参数。以某企业高速干切滚齿机为例,验证了该方法的可行性和有效性。使用该方法生成的工艺参数,加工效果更好,碳耗更低,可避免对工艺手册或个人经验的依赖,提高决策效率。研究结果有利于高速干切滚齿机的低碳运行,对机械制造企业实现低碳制造具有一定的参考意义。     

Tool wear mechanisms in axial ultrasonic vibration assisted milling in-situ TiB2/7050Al metal matrix composites

The in-situ TiB₂ particle reinforced aluminum matrix composites are materials that are difficult to machine, owing to hard ceramic particles in the matrix. In the milling process, the polycrystalline diamond (PCD) tools are used for machining these materials instead of carbide cutting tools, which significantly increase the machining cost. In this study, ultrasonic vibration method was applied for milling in-situ TiB₂/7050Al metal matrix composites using a TiAlN coated carbide end milling tool. To completely understand the tool wear mechanism in ultrasonic-vibration assisted milling (UAM), the relative motion of the cutting tool and interaction of workpiecetool-chip contact interface was analyzed in detail. Additionally, a comparative experimental study with and without ultrasonic vibration was carried out to investigate the influences of ultrasonic vibration and cutting parameters on the cutting force, tool life and tool wear mechanism. The results show that the motion of the cutting tool relative to the chip changes periodically in the helical direction and the separation of tool and chip occurs in the transverse direction in one vibration period, in ultrasonic vibration assisted cutting. Large instantaneous acceleration can be obtained in axial ultrasonic vibration milling. The cutting force in axial direction is significantly reduced by 42%-57%, 40%-57% and 44%-54%, at different cutting speeds, feed rates and cutting depths, respectively, compared with that in conventional milling. Additionally, the tool life is prolonged approximately 2-5 times when the ultrasonic vibration method is applied. The tool wear pattern microcracks are only found in UAM. These might be of great importance for future research in order to understand the cutting mechanisms in UAM of in-situ TiB₂/7050Al metal matrix composites.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00294-2     

前混合磨料水射流对HTPB推进剂的切割 工艺参数优化研究

利用前混合磨料水射流，对高、低两种燃速的HTPB推进剂开展切割试验，着重研究不同切割条件下工艺参数对切割效率的影响，进而为工艺参数优化提供理论依据。选取切割速度（v）、出口压力（p）、磨料浓度比（T）和靶距（L）4个工艺参数为主要影响因素，以最大切割深度（H）作为切割效率的衡量指标，分别通过单因素试验和正交试验进行分析，进而完成工艺参数的优化。试验结果表明，最大切割深度随切割速度的增加而减小，且单位时间内的切割面积存在最佳值；随着出口压力的增加，最大切割深度在特定范围内近似线性增加，并逐渐趋于平缓；磨料浓度比与靶距和最大切割深度均存在最佳对应关系。正交试验结果表明，切割速度对指标影响较为显著，靶距等3个工艺参数的影响相对较小。该研究可为前混合磨料水射流作为HTPB推进剂的工程化处废技术提供理论支持。