Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.  

表面处理对环氧胶粘涂层剪切强度的影响

通过研究不同的表面处理方法对环氧树脂胶粘涂层粘附强度的影响,得出适当的表面处理方法能显著地提高环氧胶粘涂层的剪切强度.  

Predicting surface roughness in machining: a review

The general manufacturing problem can be described as the achievement of a predefined product quality with given equipment, cost and time constraints. Unfortunately, for some quality characteristics of a product such as surface roughness it is hard to ensure that these requirements will be met. This paper aims at presenting the various methodologies and practices that are being employed for the prediction of surface roughness.The resulting benefits allow for the manufacturing process to become more productive and competitive and at the same time to reduce any re-processing of the machined workpiece so as to satisfy the technical specifications. Each approach with its advantages and disadvantages is outlined and the present and future trends are discussed. The approaches are classified into those based on machining theory, experimental investigation, designed experiments and artificial intelligence (AI).  

高速铣削表面粗糙度的研究

通过在HSM－700型高速铣床上的正交铣削试验，联系平时实际的生产加工情况，分析高速铣削的切削加工参数对零件表面粗糙度的影响。通过分析不同铣削参数下的零件表面粗糙度和切屑变形，为高速加工切削参数的选择和表面质量的控制提供依据。  

高周疲劳条件下高强钢临界夹杂物尺寸估算

依据Murakami的“夹杂物等效投影面积模型”估算了在高周疲劳条件下一定硬度（或强度）高强钢的“临界夹杂物尺寸”．估算结果表明，随着钢硬度（或强度）的增加，“临界夹杂物尺寸”逐渐减小；“临界夹杂物尺寸”也受构件表面机加工粗糙度的影响，表面越光洁，这个尺寸也越小．从本文几种钢的实验数据以及其它已发表的数据都可以间接证明，估算的临界尺寸是合理的．  

陶瓷基板化学镀铜预处理的研究

为提高封装基板铜导体层与陶瓷基板的结合强度，研究了在氧化铝和氮化铝的基板上进行化学镀铜，对表面进行粗化和改性，经过优化工艺条件后，氧化铝与镀层的结合强度可以达到27MPa，氮化铝与镀层的结合强度可以达到22MPa。  

Study of electrical discharge machining technology for slicing silicon ingots

Silicon slicing technology is an undergoing process and its performance improvements meet the ever-challenging and versatile demands. A new attempt to apply the WEDM strategy to slice the semiconductor materials is studied. The barriers from unusual material characteristics are to be conquered to make this idea realizable. The existing WEDM technology is utilized to slice the heavy-doped silicon ingot and its feasibility is examined. The machining rate and surface roughness are measured under various current on times and servo voltages in both the water immersed and water flushing WEDM machines. If small current on time is collocated with proper off time and lower gap voltage sensitivity under automatic feed mode, the stable area machining rate of around 76 mm²/min can be attained, and the R_a value is 3.6 μm or so which is acceptable if the following polishing procedure is considered. The thickness of defects to be polished can be predicted from the SEM photographs of the cross-sections of the sliced wafers. If the wire diameter is 0.25 mm and the wafer thickness is 1 mm, the portion of material loss including the kerf and the amount to be polished is under 26%.  

In-process surface roughness prediction using displacement signals from spindle motion

A method to predict surface roughness in real time was proposed and its effectiveness was proved through experiment in this paper. To implement the proposed method in machining process, a sensor system to measure relative displacement caused by the cutting operation was developed. In this research, roughness of machined surface was assumed to be generated by the relative motion between tool and workpiece and the geometric factors of a tool. The relative motion caused by the machining process could be measured in process using a cylindrical capacitive displacement sensor (CCDS). The CCDS was installed at the quill of a spindle and the sensing was not disturbed by the cutting. The workpiece was NAK80 and TiAlN coated carbide end mills were used in the test. Model to predict surface roughness was developed. A simple linear regression model was developed to predict surface roughness using the measured signals of relative motion. Close relation between machined surface roughness and roughness predicted using the measured signals was verified with similarity of about 95%.  

表面预处理对PPS/FEP复合防腐涂层结合强度的影响

采用拉开法测定了砂纸打磨、酸洗、磷化以及喷砂等多种表面处理条件下PPS／FEP复合防腐涂层的结合强度，用扫描电镜(SEM)和表面粗糙度测试仪分析了试样经各种表面处理后的表面形貌及粗糙度。结果表明，磷化处理后试样表面具有均匀致密的显微孔隙结构，涂层结合强度最好；喷砂处理的效果仅次于磷化；砂纸打磨条件下，随表面粗化程度的增加，结合强度增加；酸洗后表面粗糙度较低，因而涂层结合强度低，但比同等粗糙度的砂纸打磨效果好。  

Development of an on-machine profile measurement system in ELID grinding for machining aspheric surface with software compensation

Efficient manufacture of dimensionally accurate optical surface on hard and brittle materials is a major concern for optoelectronic industry. Electrolytic in process dressing (ELID) grinding is proved as a reliable process to achieve this optical quality nano-surface finish on hard and brittle materials. Besides surface finish it is important to ensure dimensional accuracy by improving profile and form accuracy of the ground aspheric surface. Kinematic factors are commonly considered the reasons for the dimensional inaccuracy in a machined part. Software compensation is a direct and economical method to overcome several kinematic factors and improve the dimensional accuracy. Last, but most important, is the monitoring of achieved surface profile to ensure more accurate profile radius in the finished part. So an on-machine profile measurement system based on coordinate measuring machine (CMM) principle has been developed to check the profile radius of the ground surface. In this study software compensation was applied in ELID grinding of an aspheric surface in order to compensate the wheel wear until the measured surface profile machined on BK7 glass reaches within tolerable limit.