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).

Predicting surface roughness in machining: a review

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. q 2004 Elsevier Ltd. All rights reserved.

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

A review of optimization techniques in metal cutting processes

Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel

Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.

Review: A review of data mining applications for quality improvement in manufacturing industry

Surface roughness plays an important role in product quality. This paper focuses on developing an empirical model for the prediction of surface roughness in finish turning. The model considers the following working parameters: workpiece hardness (material); feed; cutting tool point angle; depth of cut; spindle speed; and cutting time. One of the most important data mining techniques, nonlinear regression analysis with logarithmic data transformation, is applied in developing the empirical model. The values of surface roughness predicted by this model are then verified with extra experiments and compared with those from some of the representative models in the literature. Metal cutting experiments and statistical tests demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in both model construction and verification. Finally, further research directions are presented.

Development of Empirical Models for Surface Roughness Prediction in Finish Turning

Roll-forging process, an innovative local plastic deformation process, is very suitable for forming the automotive front axle beam Due to the complicated geometry of automotive front axle beam and the complicated roll-forging process, it is difficult to develop an accurate FE model of roll-forging of automotive front axle beam In this paper, an experiment of roll-forging of automotive front axle beam is carried out By comparing the simulation results with the experimental ones, the reasonable friction factors between the dies and roll-forged pieces are determined, so that an accurate 3D FE model of roll-forging of automotive front axle beam is developed under the Deform-3D platform Based on this accurate 3D FE model, the forming laws of roll-forging of automotive front axle beam are illustrated, which includes the geometry development, effective strain distribution and evolution, temperature distribution and evolution, roll-forging load evolution, and metal flowing velocity distribution and evolution The research results have important significance for comprehensively revealing the macro-deformation laws of roll-forging of automotive front axle beam

Numerical and experimental investigation of roll-forging of automotive front axle beam

The invention discloses a front axle correcting device and a front axle correcting technology. The front axle correcting device comprises an upper pressing plate, a lower pressing plate and a plurality of extruding mechanisms arranged between the upper pressing plate and the lower pressing plate in sequence, wherein each extruding mechanism comprises a pressing component fixed on the lower surface of the upper pressing plate and a pressed component fixed on the upper surface of the lower pressing plate, and a clamping groove used for clamping a front axle to be corrected is formed in each pressed component. According to the front axle correcting device and the front axle correcting technology, the front axle is placed horizontally to be corrected, and therefore the front axle is prevented from being damaged and discarded; the front axle is corrected in the X direction, in the Y direction and in the Z direction respectively, and therefore the correcting effect is greatly improved.

Front axle correcting device and front axle correcting technology

Roll-forging process is widely used to fabricate the automobile front axle beams. The reasonable selection of the process parameters is significant to ensure the quality and improve the property of the automobile front axle beam owing to the complexity and importance of the preliminary roll-forging process. This paper developed a 3D Finite element (FE) model of the preliminary roll-forging process of the AISI-1045 automobile front axle beam under the Deform-3D platform. A corresponding experiment is subsequently conducted. Good agreement between the experimental and simulative results validates the employed model. Based on this dependable FE model, the influences of initial temperature of billet T0, rotational speed of rolls n and friction factor m on the geometric dimension, temperature inhomogeneity, deformation inhomogeneity, and roll-forging load are investigated comprehensively. The results can provide reliable guidelines for the selection of process parameters.

Wang Xiaowen

Papers

Development of Empirical Models for Surface Roughness Prediction in Finish Turning

Numerical and experimental investigation of roll-forging of automotive front axle beam

Front axle correcting device and front axle correcting technology

The influences of process parameters on the preliminary roll-forging process of the AISI-1045 automobile front axle beam

Technological method and device for ultrasound-assisted quenching