A review of surface roughness generation in ultra-precision machining

Ultra-precision machining (UPM) is capable of manufacturing a high quality surface at a nanometric surface roughness. For such high quality surface in a UPM process, due to the machining complexity any variable would be possible to deteriorate surface quality, consequently receiving much attention and interest. The general factors are summarized as machine tool, cutting conditions, tool geometry, environmental conditions, material property, chip formation, tool wear, vibration etc. This paper aims to review the current state of the art in studying the surface roughness formation and the factors influencing surface roughness in UPM. Firstly, the surface roughness characteristics in UPM is introduced. Then in UPM, a wide variety of factors for surface roughness are then reviewed in detail and the mechanism of surface roughness formation is concluded thoroughly. Finally, the challenges and opportunities faced by industry and academia are discussed and several principle conclusions are drawn.     

Effect of Surface Roughness on Droplet Bouncing in Droplet-Based Manufacturing Processes

In droplet-based manufacturing processes, such as drop-wise rapid prototyping, solder bumping and spray forming, the bouncing phenomenon adversely affects the quality of the deposit. This study investigates the effect of surface roughness on bouncing of liquid metal droplets from the substrate. An analytical model was developed to correlate the surface roughness with a non-dimensional droplet bouncing potential. In addition, an experimental study was conducted to image the deposition behavior of Pb-37wt% Sn solder droplets, 280 μm in diameter, on Au-plated substrates with a wide range of surface roughness. The high-speed image data correlate well with the model prediction that droplet bouncing increases as the surface roughness increases.     

Quantification of surface roughness of parts processed by laminated object manufacturing

Additive manufacturing (AM) technology is essentially performed using a layered manufacturing (LM) process. Because more complex 3D physical models can be efficiently fabricated without geometric limitation by the technology, a remarkable reduction in production life cycle has been achieved. However, due to the LM process, a deterioration of the surface quality of the parts processed by AM may occasionally occur, which is the primary reason that the surface problem has been a key issue in AM. In this paper, a methodology is proposed to quantify the surface roughness of the parts processed by laminated object manufacturing (LOM), which is a typical technology in AM. The surface profiles of the parts were investigated, a schematic was constructed by considering the LOM process factor geometrically, and a theoretical approach to quantify average surface roughness according to surface angle variation is presented. The expressions required for numerical computation were deduced and defined. By comparing the measured data and computed values, the proposed approach was verified. Additionally, the effects of the process variables related to surface quality were evaluated and analyzed.     

An in-process surface recognition system based on neural networks in end milling cutting operations

An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness R_a values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness R_a values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.     

面向绿色高表面质量制造的硬态车削工艺多目标参数优化

目的 为了进行硬态车削绿色制造与工艺性能协同优化研究,提出一种同时考虑碳排放量和表面粗糙度的多目标优化方法。方法 首先,通过分析硬态车削过程中切削参数、工件材料、刀具材料等因素对切削功率的影响建立碳排放目标函数,针对工件的表面粗糙度受到切削条件、工件材料、刀具材料等诸多因素的影响,利用正交试验和广义回归神经网络建立轴承硬态车削表面粗糙度目标函数。然后,考虑加工过程中机床特性和硬车实际工况等约束条件,建立以切削参数为优化变量,以碳排放量和表面粗糙度为优化目标的多目标优化模型,引入权重系数将其转化为单目标优化模型。最后,利用遗传算法对优化模型进行优化求解,深入分析切削参数对优化目标的影响。结果 在工厂实际轴承产品硬车试验中验证了优化模型的有效性,结果表明,切削速度为225 m/min、进给量为0.08 mm/r、背吃刀量为0.10 mm时,碳排放量和表面粗糙度的综合优化指标最低。相比优化前,虽然碳排放量上升了13.05%,但表面质量提升了34.44%。结论 研究结果对面向绿色制造的轴承硬车工艺参数优化提供理论方法有重要意义。     

切削参数对车削20CrMnTi表面粗糙度的影响及优化研究

20CrMnTi是一种广泛应用于齿轮制造的材料。为提高20CrMnTi精加工的表面质量、加工效率，以车削20CrMnTi钢的表面粗糙度为研究对象，设计正交试验，在数控车床GENOS L250E上进行硬质合金刀具车削试验，探究切削参数（切削速度、进给量、背吃刀量）对表面粗糙度的影响。并通过多元回归建立切削参数与表面粗糙度的关系模型，从而构建以加工效率、表面粗糙度为目标的多目标优化模型，通过粒子群算法对切削参数进行优化。试验结果表明：使用优化后的切削参数加工可以减小表面粗糙度、提高加工效率。     

Theoretical and experimental analysis of nano-surface generation in ultra-precision raster milling

The fabrication of high-quality freeform surfaces is based on ultra-precision raster milling, which allows direct machining of the freeform surfaces with sub-micrometric form accuracy and nanometric surface finish. Ultra-precision raster milling is an emerging manufacturing technology for the fabrication of high-precision and high-quality components with a surface roughness of less than 10 nm and a form error of less than 0.2 μm without the need for any additional post-processing. Moreover, the quality of a raster milled surface is based on a proper selection of cutting conditions and cutting strategies.Due to different cutting mechanics, the process factors affecting the surface quality are more complicated, as compared with ultra-precision diamond turning and conventional milling, such as swing distance and step distance. This paper presents a theoretical and experimental analysis of nano-surface generation in ultra-precision raster milling. Theoretical models for the prediction of surface roughness are built. An optimization system is established based on the theoretical models for the optimization of cutting conditions and cutting strategy in ultra-precision raster milling. A series of experiments have conducted and the results show that the theoretical models predict well the trend of the variation of surface roughness under different cutting conditions and cutting strategies.     

Predictive modeling of surface roughness in grinding

The surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall grinding system. This paper presents the prediction of the arithmetic mean surface roughness based on a probabilistic undeformed chip thickness model. The model expresses the ground finish as a function of the wheel microstructure, the process kinematic conditions, and the material properties. The analysis includes a geometrical analysis of the grooves left on the surface by ideal conic grains. The material properties and the wheel microstructure are considered in the surface roughness prediction through the chip thickness model. A simple expression that relates the surface roughness with the chip thickness was found, which was verified using experimental data from cylindrical grinding.     

切削参数对纯铁车削表面粗糙度的影响

为提高纯铁材料精加工时的表面质量,在相同冷却润滑方式下进行不同切削参数条件下的切削试验,分析不同切削工艺参数对已加工纯铁表面质量的影响规律。结果表明:不同切削参数造成的切削温度变化是影响粗糙度的主要因素,而进给量以及切削深度对加工表面的最大轮廓高度产生了较大影响。试验结果对纯铁精加工采用合适的切削参数以提高表面质量具有重要意义。     

Selection of sampling points for accurate evaluation of flatness error using coordinate measuring machine

Dimensional inspection, in integrated manufacturing environments, requires accurate inspection while minimizing the cost and time of inspection. The selection of sampling plan—sample size and sample point locations, the method of evaluating the form error and the nature of the manufactured surfaces will play an important role in deciding the best inspection strategy to be adopted. This paper deals with the strategy for evaluation of flatness error which is one of the most commonly used form tolerances for control of manufactured surfaces. Investigations have been carried out to ascertain the influence of surface quality (surface roughness) in determining the sampling strategy for accurate determination of flatness error while inspecting on a coordinate measuring machine (CMM). The sampling plan utilizes the Hammersley sequence for point location and the flatness error is evaluated using the minimum zone method (MZM) based on computational geometry techniques. Results indicate that the surface roughness influences the accuracy of inspection and can be used as a parameter for determining an initial sample size for the determination of flatness error.