基于注塑模具钢研磨和抛光工序的自动化表面处理--中英文翻译

基于注塑模具钢研磨和抛光工序的自动化表面处理摘要本文研究了注塑模具钢自动研磨与球面抛光加工工序的可能性，这种注塑模具钢PDS5的塑性曲面是在数控加工中心完成的。这项研究已经完成了磨削刀架的设计与制造。 最佳表面研磨参数是在钢铁PDS5 的加工中心测定的。对于PDS5注塑模具钢的最佳球面研磨参数是以下一系列的组合：研磨材料的磨料为粉红氧化铝，进给量500毫米/分钟，磨削深度20微米，磨削转速为18000RPM。用优化的参数进行表面研磨，表面粗糙度Ra值可由大约1.60微米改善至0.35微米。 用球抛光工艺和参数优化抛光，可以进一步改善表面粗糙度Ra值从0.343微米至0.06微米左右。在模具内部曲面的测试部分，用最佳参数的表面研磨、抛光，曲面表面粗糙度就可以提高约2.15微米到0 0.07微米。关键词: 自动化表面处理 抛光 磨削加工 表面粗糙度 田口方法 一、引言塑胶工程材料由于其重要特点,如耐化学腐蚀性、低密度、易于制造,并已日渐取代金属部件在工业中广泛应用。 注塑成型对于塑料制品是一个重要工艺。注塑模具的表面质量是设计的本质要求,因为它直接影响了塑胶产品的外观和性能。 加工工艺如球面研磨、抛光常用于改善表面光洁度。研磨工具(轮子)的安装已广泛用于传统模具的制造产业。自动化表面研磨加工工具的几何模型将介绍。自动化表面处理的球磨研磨工具将得到示范和开发。 磨削速度, 磨削深度，进给速率和砂轮尺寸、研磨材料特性（如磨料粒度大小）是球形研磨工艺中主要的参数，如图1（球面研磨过程示意图）所示。注塑模具钢的球面研磨最优化参数目前尚未在文献得到确切的依据。 步距研磨高度球磨研磨进给速度工作台图1 球面研磨过程示意图进给研磨球工作台研磨深度研磨表面近年来 ，已经进行了一些研究，确定了球面抛光工艺的最优参数(图2) （球面抛光过程示意图）。 比如，人们发现, 用碳化钨球滚压的方法可以使工件表面的塑性变形减少,从而改善表面粗糙度、表面硬度、抗疲劳强度。 抛光的工艺的过程是由加工中心和车床共同完成的。对表面粗糙度有重大影响的抛光工艺主要参数，主要是球或滚子材料，抛光力， 进给速率,抛光速度,润滑、抛光率及其他因素等。注塑模具钢PDS5的表面抛光的参数优化，分别结合了油脂润滑剂，碳化钨球,抛光速度200毫米/分钟,抛光力300牛，40微米的进给量。采用最佳参数进行表面研磨和球面抛光的深度为2.5微米。 通过抛光工艺，表面粗糙度可以改善大致为40至90。 图2 球面抛光过程示意图此项目研究的目的是，发展注塑模具钢的球形研磨和球面抛光工序，这种注塑模具钢的曲面实在加工中心完成的。表面光洁度的球研磨与球抛光的自动化流程工序，如图3所示。 我们开始自行设计和制造的球面研磨工具及加工中心的对刀装置。利用田口正交法，确定了表面球研磨最佳参数。选择为田口L18型矩阵实验相应的四个因素和三个层次。 用最佳参数进行表面球研磨则适用于一个曲面表面光洁度要求较高的注塑模具。 为了改善表面粗糙, 利用最佳球面抛光工艺参数，再进行对表层打磨。PDS试样的设计与制造选择最佳矩阵实验因子确定最佳参数实施实验分析并确定最佳因子进行表面抛光应用最佳参数加工曲面测量试样的表面粗糙度球研磨和抛光装置的设计与制造图3自动球面研磨与抛光工序的流程图二、球研磨的设计和对准装置实施过程中可能出现的曲面的球研磨,研磨球的中心应和加工中心的Z轴相一致。 球面研磨工具的安装及调整装置的设计，如图4（球面研磨工具及其调整装置）所示。电动磨床展开了两个具有可调支撑螺丝的刀架。磨床中心正好与具有辅助作用的圆锥槽线配合。 拥有磨床的球接轨,当两个可调支撑螺丝被收紧时，其后的对准部件就可以拆除。研磨球中心坐标偏差约为5微米, 这是衡量一个数控坐标测量机性能的重要标准。 机床的机械振动力是被螺旋弹簧所吸收。球形研磨球和抛光工具的安装，如图5（a. 球面研磨工具的图片. b.球抛光工具的图片）所示。为使球面磨削加工和抛光加工的进行，主轴通过球锁机制而被锁定。 模柄弹簧工具可调支撑紧固螺钉磨球自动研磨磨球组件图4 球面研磨工具及其调整装置图5 a. 球面研磨工具的图片. b.球抛光工具的图片三、矩阵实验的规划3.1田口正交表利用矩阵实验田口正交法，可以确定参数的有影响程度。 为了配合上述球面研磨参数，该材料磨料的研磨球(直径10毫米),进给速率，研磨深度,在次研究中电气磨床被假定为四个因素，指定为从A到D（见表1实验因素和水平）。三个层次的因素涵盖了不同的范围特征,并用了数字1、2、3标明。挑选三类磨料,即碳化硅,白色氧化铝,粉红氧化铝来研究. 这三个数值的大小取决于每个因素实验结果。选定L18型正交矩阵进行实验，进而研究四三级因素的球形研磨过程。表1实验因素和水平因素水平123A.碳化硅白色氧化铝粉红氧化铝B.50100200C.研磨深度（m）205080D.1200018000240003.2数据分析的界定 工程设计问题,可以分为较小而好的类型,象征性最好类型,大而好类型，目标取向类型等。 信噪比(S/N)的比值，常作为目标函数来优化产品或者工艺设计。 被加工面的表面粗糙度值经过适当地组合磨削参数，应小于原来的未加工表面。 因此,球面研磨过程属于工程问题中的小而好类型。这里的信噪比（S/N）,按下列公式定义: =10 log 平方等于质量特性=10 log （1）这里，y不同噪声条件下所观察的质量特性n实验次数从每个L18型正交实验得到的信噪比（S/N）数据，经计算后，运用差异分析技术(变异)和歼比检验来测定每一个主要的因素。 优化小而好类型的工程问题问题更是尽量使最大而定。各级选择的最大化将对最终的因素有重大影响。 最优条件可视研磨球而待定。四、实验工作和结果这项研究使用的材料是PDS5工具钢(相当于艾西塑胶模具)， 它常用于大型注塑模具产品在国内汽车零件领域和国内设备。 该材料的硬度约HRC33(HS46)。 具体好处之一是, 由于其特殊的热处理前处理，模具可直接用于未经进一步加工工序而对这一材料进行加工。式样的设计和制造,应使它们可以安装在底盘，来测量相应的反力。 PDS5试样的加工完毕后，装在大底盘上在三坐标加工中心进行了铣削，这种加工中心是由钢铁公司所生产(中压型三号),配备了FANUC-18M公司的数控控制器(0.99型)。用hommelwerket4000设备来测量前机加工前表面的粗糙度,使其可达到1.6微米。 图6试验显示了球面磨削加工工艺的设置。 一个由Renishaw公司生产的视频触摸触发探头，安装在加工中心上，来测量和确定和原始式样的协调。 数控代码所需要的磨球路径由PowerMILL软件产。这些代码经过RS232串口界面，可以传送到装有控制器的数控加工中心上。加工中心数控机床电脑图6完成了L18型矩阵实验后，表2 （PDS5试样光滑表层的粗糙度）总结了光滑表面的粗糙度RA值，计算了每一个L18型矩阵实验的信噪比（S/N）,从而用于方程（1）。通过表2提供的各个数值，可以得到四种不同程度因素的平均信噪比（S/N），在图7中已用图表显示。表2 PDS5试样光滑表层的粗糙度实验序号ABCDS/N(dB)Mean111110.350.350.359.1190.350212220.370.360.388.6340.370313330.410.440.407.5970.417421230.630.650.643.8760.640522310.730.770.782.3800.760623120.450.420.397.5300.420731320.340.310.329.8010.323832130.270.250.2811.4710.267933210.320.320.329.8970.3201011220.350.390.408.3900.3801112330.410.500.436.9680.4471213110.400.390.427.8830.4031321130.330.340.319.7120.3271422210.480.500.476.3120.4831523320.570.610.534.8680.5701631310.590.550.545.0300.5601732120.360.360.358.9540.3571833230.570.530.535.2930.543控制因素信噪比图7 控制影响因素球面研磨工艺的目标，就是通过确定每一种因子的最佳优化程度值，来使试样光滑表层的表面粗糙度值达到最小。因为 log是一个减函数，我们应当使信噪比（S/N）达到最大。因此，我们能够确定每一种因子的最优程度使得的值达到最大。因此基于这个点阵式实验的最优转速应该是18000RPM，如表3（优化组合球面研磨参数）所示。表3 优化组合球面研磨参数因素水平白色氧化铝50mm/min20m18000rpm从田口矩阵实验获得的球面研磨优化参数，适用于曲面光滑的模具，从而改善表面的粗糙度。选择香水瓶为一个测试载体。对于被测物体的模具数控加工中心，由PowerMILL软件来模拟测试。经过精铣，通过使用从田口矩阵实验获得的球面研磨优化参数，模具表面进一步光滑。紧接着,使用打磨抛光的最佳参数，来对光滑曲面进行抛光工艺，进一步改善了被测物体的表面粗糙度。(见图 9)。模具内部的表面粗糙度用hommelwerket4000设备来测量。模具内部的表面粗糙度RA的平均值为2.15微米，光滑表面粗糙度RA的平均值为0.45微米，抛光表面粗糙度RA的平均值为0.07微米。被测物体的光滑表面的粗糙度改善了：(2.15-0.45)/2.15=79.1，抛光表面的粗糙度改善了：(2.15-0.07)/2.15=96.7。抛光表面Ra=0.07m内部表面Ra=2.15m光滑表面Ra=0.45m图8 被测物体表面粗糙度五、结论在这项工作中,对注塑模具的曲面进行了自动球面研磨与球面抛光加工，并将其工艺最佳参数成功地运用到加工中心上。 设计和制造了球面研磨装置(及其对准组件)。通过实施田口L18型矩阵进行实验，确定了球面研磨的最佳参数。对于PDS5注塑模具钢的最佳球面研磨参数是以下一系列的组合：材料的磨料为粉红氧化铝，进给量料500毫米/分钟，磨削深度20微米，转速为18000RPM。通过使用最佳球面研磨参数，试样的表面粗糙度Ra值从约1.6微米提高到0.35微米。应用最优化表面磨削参数和最佳抛光参数，来加工模具的内部光滑曲面，可使模具内部的光滑表面改善79.1，抛光表面改善96.7。鸣谢作者感谢中国国家科学理事会对本次研究的支持, NSC 89-2212-E-011-059。Automated surface finishing of plastic injection mold steel with spherical grinding and ball burnishing processesAbstractThis study investigates the possibilities of automated spherical grinding and ball burnishing surface finishing processes in a freeform surface plastic injection mold steel PDS5 on a CNC machining center. The design and manufacture of a grinding tool holder has been accomplished in this study. The optimal surface grinding parameters were determined using Taguchis orthogonal array method for plastic injection molding steel PDS5 on a machining center. The optimal surface grinding parameters for the plastic injection mold steel PDS5 were the combination of an abrasive material of PA Al2O3, a grinding speed of 18 000 rpm, a grinding depth of 20 m, and a feed of 50 mm/min. The surface roughness Ra of the specimen can be improved from about 1.60 m to 0.35 m by using the optimal parameters for surface grinding. Surface roughness Ra can be further improved from about 0.343 m to 0.06 m by using the ball burnishing process with the optimal burnishing parameters. Applying the optimal surface grinding and burnishing parameters sequentially to a fine-milled freeform surface mold insert, the surface roughness Ra of freeform surface region on the tested part can be improved from about 2.15 m to 0.07 m.Keywords Automated surface finishing Ball burnishing process Grinding process Surface roughness Taguchis method1 IntroductionPlastics are important engineering materials due to their specific characteristics, such as corrosion resistance, resistance to chemicals, low density, and ease of manufacture, and have increasingly replaced metallic components in industrial applications. Injection molding is one of the important forming processes for plastic products. The surface finish quality of the plastic injection mold is an essential requirement due to its direct effects on the appearance of the plastic product. Finishing processes such as grinding, polishing and lapping are commonly used to improve the surface finish.The mounted grinding tools (wheels) have been widely used in conventional mold and die finishing industries. The geometric model of mounted grinding tools for automated surface finishing processes was introduced in. A finishing process mode of spherical grinding tools for automated surface finishing systems was developed in. Grinding speed, depth of cut, feed rate, and wheel properties such as abrasive material and abrasive grain size, are the dominant parameters for the spherical grinding process, as shown in Fig. 1. The optimal spherical grinding parameters for the injection mold steel have not yet been investigated based in the literature.Fig.1. Schematic diagram of the spherical grinding processIn recent years, some research has been carried out in determining the optimal parameters of the ball burnishing process (Fig. 2). For instance, it has been found that plastic deformation on the workpiece surface can be reduced by using a tungsten carbide ball or a roller, thus improving the surface roughness, surface hardness, and fatigue resistance. The burnishing process is accomplished by machining centers and lathes. The main burnishing parameters having significant effects on the surface roughness are ball or roller material, burnishing force, feed rate, burnishing speed, lubrication, and number of burnishing passes, among others. The optimal surface burnishing parameters for the plastic injection mold steel PDS5 were a combination of grease lubricant, the tungsten carbide ball, a burnishing speed of 200 mm/min, a burnishing force of 300 N, and a feed of 40 m. The depth of penetration of the burnished surface using the optimal ball burnishing parameters was about 2.5 microns. The improvement of the surface roughness through burnishing process generally ranged between 40% and 90%.Fig. 2. Schematic diagram of the ball-burnishing processThe aim of this study was to develop spherical grinding and ball burnishing surface finish processes of a freeform surface plastic injection mold on a machining center. The flowchart of automated surface finish using spherical grinding and ball burnishing processes is shown in Fig. 3. We began by designing and manufacturing the spherical grinding tool and its alignment device for use on a machining center. The optimal surface spherical grinding parameters were determined by utilizing a Taguchis orthogonal array method. Four factors and three corresponding levels were then chosen for the Taguchis L18 matrix experiment. The optimal mounted spherical grinding parameters for surface grinding were then applied to the surface finish of a freeform surface carrier. To improve the surface roughness, the ground surface was further burnished, using the optimal ball burnishing parameters.Fig. 3. Flow chart of automated surface finish using spherical grinding and ball burnishing processes2 Design of the spherical grinding tool and its alignment deviceTo carry out the possible spherical grinding process of a freeform surface, the center of the ball grinder should coincide with the z-axis of the machining center. The mounted spherical grinding tool and its adjustment device was designed, as shown in Fig. 4. The electric grinder was mounted in a tool holder with two adjustable pivot screws. The center of the grinder ball was well aligned with the help of the conic groove of the alignment components. Having aligned the grinder ball, two adjustable pivot screws were tightened; after which, the alignment components could be removed. The deviation between the center coordinates of the ball grinder and that of the shank was about 5 m, which was measured by a CNC coordinate measuring machine. The force induced by the vibration of the machine bed is absorbed by a helical spring. The manufactured spherical grinding tool and ball-burnishing tool were mounted, as shown in Fig. 5. The spindle was locked for both the spherical grinding process and the ball burnishing process by a spindle-locking mechanism.Fig.4. Schematic illustration of the spherical grinding tool and its adjustment deviceFig.5. (a) Photo of the spherical grinding tool (b) Photo of the ball burnishing tool3 Planning of the matrix experiment3.1 Configuration of Taguchis orthogonal arrayThe effects of several parameters can be determined efficiently by conducting matrix experiments using Taguchis orthogonal array. To match the aforementioned spherical grinding parameters, the abrasive material of the grinder ball (with the diameter of 10 mm), the feed rate, the depth of grinding, and the revolution of the electric grinder were selected as the four experimental factors (parameters) and designated as factor A to D (see Table 1) in this research. Three levels (settings) for each factor were configured to cover the range of interest, and were identified by the digits 1, 2, and 3. Three types of abrasive materials, namely silicon carbide (SiC), white aluminum oxide (Al2O3, WA), and pink aluminum oxide (Al2O3, PA), were selected and studied. Three numerical values of each factor were determined based on the pre-study results. The L18 orthogonal array was selected to conduct the matrix experiment for four 3-level factors of the spherical grinding process.Table1. The experimental factors and their levels3.2 Definition of the data analysisEngineering design problems can be divided into smaller-the better types, nominal-the-best types, larger-the-better types, signed-target types, among others 8. The signal-to-noise (S/N) ratio is used as the objective function for optimizing a product or process design. The surface roughness value of the ground surface via an adequate combination of grinding parameters should be smaller than that of the original surface. Consequently, the spherical grinding process is an example of a smaller-the-better type problem. The S/N ratio, , is defined by the following equation: =10 log10(mean square quality characteristic) =10 log10where:yi : observations of the quality characteristic under different noise conditions n: number of experimentAfter the S/N ratio from the experimental data of each L18 orthogonal array is calculated, the main effect of each factor was determined by using an analysis of variance (ANOVA) technique and an F-ratio test. The optimization strategy of the smaller-the better problem is to maximize , as defined by Eq. 1. Levels that maximize will be selected for the factors that have a significant effect on . The optimal conditions for spherical grinding can then be determined.4 Experimental work and resultsThe material used in this study was PDS5 tool steel (equivalent to AISI P20), which is commonly used for the molds of large plastic injection products in the field of automobile components and domestic appliances. The hardness of this material is about HRC33 (HS46). One specific advantage of this material is that after machining, the mold can be directly used for further finishing processes without heat treatment due to its special pre-treatment. The specimens were designed and manufactured so that they could be mounted on a dynamometer to measure the reaction force. The PDS5 specimen was roughly machined and then mounted on the dynamometer to carry out the fine milling on a three-axis machining center made by Yang-Iron Company (type MV-3A), equipped with a FUNUC Company NC-controller (type 0M). The pre-machined surface roughness was measured, using Hommelwerke T4000 equipment, to be about 1.6 m. Figure 6 shows the experimental set-up of the spherical grinding process. A MP10 touch-trigger probe made by the Renishaw Company was also integrated with the machining center tool magazine to measure and determine the coordinated origin of the specimen to be ground. The NC codes needed for the ball-burnishing path were generated by PowerMILL CAM software. These codes can be transmitted to the CNC controller of the machining center via RS232 serial interface.Fig.6. Experimental set-up to determine the optimal spherical grinding parametersTable 2 summarizes the measured ground surface roughness alue Ra and the calculated S/N ratio of each L18 orthogonal array sing Eq. 1, after having executed the 18 matrix experiments. The average S/N ratio for each level of the four actors is shown graphically in Fig. 7.Table2. Ground surface roughness of PDS5 specimenExp.Inner array (control factors)Measured surface roughness value (Ra)ResponsenoABCDS/N(dB)Mean111110.350.350.359.1190.350212220.370.360.388.6340.370313330.410.440.407.5970.417421230.630.650.643.8760.640522310.730.770.782.3800.760623120.450.420.397.5300.420731320.340.310.329.8010.323832130.270.250.2811.4710.267933210.320.320.329.8970.3201011220.350.390.408.3900.3801112330.410.500.436.9680.4471213110.400.390.427.8830.4031321130.330.340.319.7120.3271422210.480.500.476.3120.4831523320.570.610.534.8680.5701631310.590.550.545.0300.5601732120.360.360.358.9540.3571833230.570.530.535.2930.543Fig.7. Plots of control factor effectsThe goal in the spherical grinding process is to minimize the surface roughness value of the ground specimen by determining the optimal level of each factor. Since log is a monotone decreasing function, we should maximize the S/N ratio. Consequently, we can determine the optimal level for each factor as being the level that has the highest value of . Therefore, based on the matrix experiment, the optimal abrasive material was pink aluminum oxide; the optimal feed was 50 mm/min; the optimal depth of grinding was 20 m; and the optimal revolution was 18 000 rpm, as shown in Table 3.The optimal parameters for surface spherical grinding obtained from the Taguchis matrix experiments were applied to the surface finish of the freeform surface mold insert to evaluate the surface roughness improvement. A perfume bottle was selected as the tested carrier. The CNC machining of the mold insert for the tested object was simulated with Power MILL CAM software. After fine milling, the mold insert was further ground with the optimal spherical grinding parameters obtained from the Taguchis matrix experiment. Shortly afterwards, the ground surface was burnished with the optimal ball burnishing parameters to further improve the surface roughness of the tested object (see Fig. 8). The surface roughness of the mold insert was measured with Hommelwerke T4000 equipment. The average surface roughness value Ra on a fine-milled surface of the mold insert was 2.15 m on average; that on the ground surface was 0.45 m on average; and that on burnished surface was 0.07 m on average. The surface roughness improvement of the tested object on ground surface was about (2.150.45)/2.15 = 79.1%, and that on the burnished surface was about (2.150.07)/2.15 = 96.7%.Fig.8. Fine-milled, ground and burnished mold insert of a perfume bottle5 ConclusionIn this work, the optimal parameters of automated spherical grinding and ball-burnishing surface finishing processes in a freeform surface plastic injection mold were developed successfully on a machining center. The mounted spherical grinding tool (and its alignment components) was designed and manufactured. The optimal spherical grinding parameters for surface grinding were determined by conducting a Taguchi L18 matrix experiments. The optimal spherical grinding parameters for the plastic injection mold steel PDS5 were the combination of the abrasive material of pink aluminum oxide (Al2O3, PA), a feed of 50 mm/min, a depth of grinding 20 m, and a revolution of 18 000 rpm. The surface roughness Ra of the specimen can be improved from about 1.6 m to 0.35 m by using the optimal spherical grinding conditions for surface grinding. By applying the optimal surface grinding and burnishing parameters to the surface finish of the freeform surface mold insert, the surface roughness improvements were measured to be ground surface was about 79.1% in terms of ground surfaces, and about 96.7% in terms of burnished surfaces. Acknowledgement The authors are grateful to the National Science Council of the Republic of China for supporting this research with grant NSC 89-2212-E-011-059.