In-process Surface Roughness Prediction Research Articles

Development of Surface Roughness Prediction by Utilizing Dynamic Cutting Force Ratio

This paper presents the development of the in-process surface roughness prediction in the CNC turning process of the plain carbon steel with the coated carbide tool by utilizing the dynamic cutting force ratio. The dynamic cutting forces are measured to analyze the relation between the surface roughness and the cutting conditions. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with six factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the dynamic cutting force ratio. The dynamic cutting force ratio can be calculated and obtained by taking the ratio of the corresponding time records of the area of the dynamic feed force to that of the dynamic main force. The relation between the dynamic cutting force ratio and the surface roughness can be proved by the obtained frequency of them in frequency domain which are the same frequency. The proposed model has been proved by the new cutting tests with the high accuracy of 91.04% by utilizing the dynamic cutting force ratio.

In-process Surface Roughness Prediction Using Heat Generation Rate of Workpiece Surface in Turning Operation

As manufacturing technology has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance of machining processes. The focus of present study is to predict surface roughness using the surface temperature of a turning workpiece with the aid of an infrared temperature sensor. Relationship between the workpiece surface temperature and the cutting parameters and also between the surface roughness and cutting parameters were found out for indirect measurement of surface roughness through the surface temperature of the workpiece, and then correlate these temperatures to the surface roughness. A 33 full factorial design was used in order to get the output data uniformly distributed all over the ranges of the input parameters. Regression equations to get the relation between different response variables (Surface roughness and workpiece surface temperature) and the input parameters (speed, feed, and depth of cut) were found out using statistical analysis software. The experimental results show that the workpiece surface temperature can be sensed and used effectively as an indicator of the cutting performance. Thus, it is possible to increase machine utilization and decrease production cost in an automated manufacturing environment.

Workpiece Surface Temperature for In-process Surface Roughness Prediction using Response Surface Methodology

As manufacturing technology has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance of machining processes. The focus of present study is to predict surface roughness using the workpiece surface temperature of a turning workpiece with the aid of an infrared temperature sensor. Relationship between the workpiece surface temperature and the cutting parameters and also between the surface roughness and cutting parameters were found out for indirect measurement of surface roughness through the surface temperature of the workpiece. A 33 full factorial design was used in order to get the output data uniformly distributed all over the ranges of the input parameters. Response Surface Method (RSM) and analysis of variance (ANOVA) are used to get the relation between different response variables (Surface roughness and workpiece surface temperature) and the input parameters (speed, feed and depth of cut). Based on variance analysis for the second order RSM model, most influential design variable is feed rate and depth of cut on surface roughness and workpiece surface temperature respectively and the experimental results show that the workpiece surface temperature can be sensed and used effectively as an indicator of the cutting performance.

In-process surface roughness prediction system using cutting vibrations in turning

Machining is a complex process in which many variables can affect the desired results. Among them, surface roughness is a widely used index of a machined product quality and, in most cases, is a technical requirement for mechanical products since, together with dimensional precision, it affects the functional behavior of the parts during their useful life, especially when they have to be in contact with other materials. In-process surface roughness prediction is, thus, extremely important. In this work, an in-process surface roughness estimation procedure, based on least-squares support vector machines, is proposed for turning processes. The cutting conditions (feed rate, cutting speed, and depth of cut), parameters of tool geometry (nose radius and nose angle), and features extracted from the vibration signals constitute the input information to the system. Experimental results show that the proposed system can predict surface roughness with high accuracy in a fast and reliable way.

The development of an in-process surface roughness adaptive control system in turning operations

This research shows the development of an in-process surface roughness adaptive control (ISRAC) system in turning operations. An artificial neural network (ANN) was employed to establish two subsystems: the neural network-based, in-process surface roughness prediction (INNSRP) subsystem and the neural network-based, in-process adaptive parameter control (INNAPC) subsystem. The two subsystems predicted surface roughness and adapted feed rate using data from not only cutting parameters (such as feed rate, spindle speed, and depth of cut), but also vibration signals detected by an accelerometer sensor. The INNSRP subsystem predicted surface roughness during the finish cutting process with an accuracy of 92.42%. The integration of the two subsystems led to the neural-networks-based surface roughness adaptive control (INNSRAC) system. The 100% success rate for adaptive control of the test runs proved that this proposed system could be implemented to adaptively control surface roughness during turning operations.

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%.

A fuzzy-nets-based in-process surface roughness prediction system in turning operations

A Fuzzy-Nets-based in-process surface roughness prediction (FISRP) system was developed to predict surface roughness in turning operations in a real time fashion. The input variables of the FISRP system were machining parameters, such as feed rate, spindle speed, depth of cut, and machining vibration per revolution. An accelerometer was employed to gather real-time vibration signals. Two groups of data were collected for two cutter bits with nose radii of 0.016 and 0.031 inches, respectively. Fuzzy nets theory was implemented to use the experimental data in developing the system for real-time prediction. The fuzzy nets theory is a five-step learning procedure for developing a knowledge base to predict surface roughness in real time. This FISRP system was tested to have an average prediction accuracy of 95.70%.

An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling Operations

Surface roughness is influenced by the machining parameters and other uncontrollable factors resulting from the cutting tool in end milling operations. To perform the in-process surface roughness prediction (ISRP) system accurately, the uncontrollable factors must be monitored. In this paper, an empirical approach using a statistical analysis was employed to discover the proper cutting force to represent the uncontrollable factors in end milling operations. Furthermore, an in-process neural network-based surface roughness prediction (INN-SRP) system was developed. A neural network associated with sensing technology was applied as a decision-making system to predict the surface roughness for a wide range of machining parameters. The good accuracy of the results for a wide range of machining parameters indicates that the system is suitable for application in industry.

STATISTICAL ASSISTED FUZZY-NETS BASED IN-PROCESS SURFACE ROUGHNESS PREDICTION (S-FN-IPSRP) SYSTEM IN END MILLING OPERATIONS

ABSTRACT A statistical assisted fuzzy-nets (S-FN) based in-process surface roughness prediction (IPSRP) system using resultant cutting forces and cutting parameters has been developed. This system receives the instant cutting force signals and cutting parameters to predict surface roughness in a real time fashion. A statistical assisted fuzzy-nets learning procedure has been developed to construct the fuzzy rule bank for the proposed prediction model. The proposed S-FN-IPSRP system was developed using real time experimental data and the system also has been tested using 30 testing data sets in real time. The results demonstrated that the system has an average 94% accuracy rate.

Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations

A fuzzy-nets based in-process surface roughness prediction (FN-ISRP) system based on the fuzzy-nets training scheme has been developed for predicting the surface roughness generated in milling operations while the machining process is taking place. In addition to the consideration of cutting parameters, such asspindle speed, feed rate, and depth of cut as fuzzy-nets input variables, this paper also describes the use of vibration in the FN-ISRP system. This cutting vibration was measured using an accelerometer and a proximity sensor. Five steps of the fuzzy-nets training scheme were implemented throughout the experiments, followed by the fuzzy rule bank, which was created based on physical experimentation. After the fuzzy rule bank was established, tests were conducted in a real-time fashion to evaluate the performance. In the fuzzynets model, Ra was predicted with a 96% accuracyrate, and the system could respond to the prediction value within 0.5 seconds during the end-milling process.