Regression Analysis for Predicting Surface Finish and Its Application in the Determination of Optimum Machining Conditions
01 Aug 1970-Journal of Engineering for Industry (American Society of Mechanical Engineers)-Vol. 92, Iss: 3, pp 711-714
About: This article is published in Journal of Engineering for Industry.The article was published on 1970-08-01. It has received 105 citations till now. The article focuses on the topics: Machining & Surface finish.
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TL;DR: In this article, the use of grey relational analysis for optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced, where various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered.
Abstract: The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of grey relational analysis for optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced. Various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered. An orthogonal array was used for the experimental design. Optimal machining parameters were determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness and the burr height). Experimental results have shown that the surface roughness and the burr height in the drilling process can be improved effectively through the new approach .
394 citations
Cites methods from "Regression Analysis for Predicting ..."
...Bhattacharyaa [2] used the Lagrangian function method in searching for optimum cutting parameters....
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TL;DR: Wang et al. as discussed by the authors developed an empirical model for the prediction of surface roughness in finish turning, which considers the following working parameters: workpiece hardness (material); feed; cutting tool point angle; depth of cut; spindle speed; and cutting time.
Abstract: 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.
319 citations
TL;DR: In this paper, a new approach for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis was presented.
Abstract: This paper presents a new approach for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis. Experiments are conducted on LM25-based aluminium alloy reinforced with green bonded silicon carbide of size 25 μm (10% volume fraction). Drilling tests are carried out using TiN coated HSS twist drills of 10 mm diameter under dry condition. In this study, drilling parameters namely cutting speed, feed and point angle are optimized with the considerations of multi responses such as surface roughness, cutting force and torque. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in drilling process can be improved effectively through the new approach.
295 citations
Cites methods from "Regression Analysis for Predicting ..."
...Bhattacharyya [8] used the Lagrangian function method in searching for optimum cutting parameters....
[...]
TL;DR: In this paper, an in-process surface recognition system was developed to predict the surface roughness of machined parts in the end milling process to assure product quality and increase production rate by predicting the surface finish parameters in real time.
Abstract: 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 Ra 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 Ra values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.
195 citations
TL;DR: In this paper, an abductive network is adopted to construct a prediction model for surface roughness and cutting force, which is composed of a number of functional nodes, which are self-configured to form an optimal network hierarchy by using a predicted square error (PSE) criterion.
188 citations