Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

Novel methods for rapid assessment of tool performance in milling

The paper examines the effectiveness of two innovative techniques designed to rapidly optimize a milling application. One of them relates to quantifying the relative wear of different insert grades concurrently in a single cutting test, by mounting the inserts in the same cutter, for a quick comparative performance evaluation. Experimental results that illustrate the validity and limitation of this concept, and a scheme for enhancing the reliability of the test method are presented. The other technique refers to rapid identification of the optimum feed/tooth that corresponds to maximum tool life. This entails a test wherein individual inserts in the cutter are subject to feed/tooth that are multiples of a base value, by selectively leaving appropriate number of consecutive insert pockets unoccupied. These novel techniques complement known accelerated tool life tests, and are expedient for industries that engage short production runs, in terms of selecting a suitable insert grade for an application, and determining optimal cutting conditions for the selected grade.     

基于机器视觉的加工刀具磨损监测方法

为了对加工过程中刀具的磨损状态进行监测,针对麻花钻的磨损形式,提出基于机器视觉的加工刀具磨损监测方法. 根据磨损刀具图像的灰度分布特点,提出基于积分图加速和Turky bi-weight核函数的非局部均值去噪方法;采用单、双阈值大津法获取磨损区域的灰度区间,实现对图像的自适应对比度增强;提出基于形态学重构方法的磨损区域局部极值点提取方法,有效完成对磨损区域的检测和边界提取. 该刀具磨损检测方法成功应用于麻花钻头磨损状态的监测过程,实验结果表明,相较于目前已有的机器视觉监测方法,所提出的方法具有更高的检测精度和效率,准确地提取磨损轮廓,从而有效实现对刀具磨损状态的监测和自动化监控加工过程,达到降低人工成本和产品不合格率的目的.     

高空作业平台臂架伸缩运动的横向振动特性

为了探究高空作业平台做伸缩运动时变幅平面内的横向振动动力学特性,针对臂架的实际搭接和支承情况,将臂架等效为根部铰接、中间弹性支承且带有集中参数的变截面变长度梁. 基于牛顿第二定律建立各臂段的运动微分方程,采用模态叠加法、结合边界条件求解臂架一系列时刻的瞬态振型函数,曲线拟合时变参数以近似表示梁的振型,并依据伽辽金截断方法,得到广义坐标下的状态空间方程. 在Matlab/Simulink环境下进行动态仿真,得到伸缩过程中臂架头部的变幅平面横向振动动态响应. 结果表明,就横向振动振幅而言,搭接简化会带来15.63%计算结果误差;支承简化会加大臂架整体刚度、减小臂架振动响应,不可取。     

碳化物在高铬铸铁高应力磨料磨损中的影响

对不同载荷作用下的高铬铸铁在耐磨实验机上进行了磨料磨损试验;对在不同的碳化物对基体的保护作用与碳化物的剥离进行了相关的分析.结果表明载荷在一定值以下磨损较为缓慢,当载荷超过一定值时,磨损急剧加剧,随着载荷的增加"尺寸较小"的碳化物首先剥离基体,当载荷继续增大时,"尺寸较大"的碳化物也剥离基体并有碎裂现象.这要求高铬铸铁中的碳化物不仅要分布均匀,碳化物的尺寸也要合适.     

纳米TiN改性金属陶瓷刀片的铣削性能

制备了两种纳米TiN改性金属陶瓷可转位铣刀片ToolA和ToolB。研究了这两种新型面铣刀在切削正火态45#钢时的铣削性能及其失效机理。试验结果表明,两种试验铣刀片在Vc=294m/min,fz=0.16mm/z,ap=0.5mm条件下铣削45#钢时,均未发现“崩刃”现象,并全都以“磨损”的形式失效,刀具寿命较长。此外,在同样切削条件下,ToolA具有良好的抗氧化磨损性能,而ToolB有更高的耐机械冲击能力;高温扩散和氧化磨损成为导致纳米TiN改性铣刀片磨损失效的主要原因。     

Influence of heat treatment on tribological behaviors of novel wrought aluminum bronze

1　ＩＮＴＲＯＤＵＣＴＩＯＮＡｌｕｍｉｎｕｍｂｒｏｎｚｅｐｏｓｓｅｓｓｅｓｅｘｃｅｌｌｅｎｔｐｈｙｓｉｃａｌ,ｍｅｃｈａｎｉｃａｌａｎｄｔｒｉｂｏｌｏｇｉｃａｌｐｒｏｐｅｒｔｉｅｓ .Ｉｔｉｓａｖａｉｌａｂｌｅｆｏｒｍａｋｉｎｇｗｅａｒ ｒｅｓｉｓｔｉｎｇａｎ     

合金元素对低合金耐磨铸钢力学性能的影响

研究了低合金耐磨铸钢中台金元素C、Si、Mn、Mo，对钢的力学性能等的影响。结果表明，合金元素的合理有效搭配，可使钢在连续空冷条件下得到贝氏体为主的显微组织：钢中C、Mn含量增加，其硬度上升，冲击韧度下降，其中以Mn影响最大，C次之：Si、Mo对钢性能影响程度较小，均在研究的中等含量处取得性能的最佳值。     

深冷处理与工模具的耐磨性

热处理后的金属材料再经低落曙处理，其力学能明显提高，尤其是高碳高合金工模具钢的耐磨性提高特别显著。本文着重研究了应用面较广的Ｗ６Ｍｏ５Ｃｒ４Ｖ２钢的处理工艺制度，组织性能及其磨损机理。     

FS-1化学镀Ni-P镀层的性能研究

采用XRD与SEM分析技术、电化学阳极极化曲线测试和 摩擦磨损试验等，系统地研究了FS-1化学镀Ni-P镀层的结构和性能.结果表明：该镀层具有 优良的耐蚀和耐磨性能，热处理虽可以显著地提高镀层的耐磨性能，但使其耐蚀性能有所降 低.