THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

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

Application of response surface methodology in the optimization of cutting conditions for surface roughness

The geometric error in the surface grinding process is mainly affected by the thermal effect and the stiffness of the grinding system. For minimizing the geometric error, the selection of grinding parameters is very important. This paper presented an application of Taguchi and response surface methodologies for the geometric error. The effect of grinding parameters on the geometric error was evaluated and optimum grinding conditions for minimizing the geometric error were determined. A second-order response model for the geometric error was developed and the utilization of the response surface model was evaluated with constraints of the surface roughness and the material removal rate. Confirmation experiments were conducted at an optimal condition and selected two conditions for observing accuracy of the developed response surface model.

Application of Taguchi and response surface methodologies for geometric error in surface grinding process

Many machining monitoring systems based on artificial intelligence (AI) process models have been successfully developed in the past for optimising, predicting or controlling machining processes. In general, these monitoring systems present important differences among them, and there are no clear guidelines for their implementation. In order to present a generic view of machining monitoring systems and facilitate their implementation, this paper reviews six key issues involved in the development of intelligent machining systems: (1) the different sensor systems applied to monitor machining processes, (2) the most effective signal processing techniques, (3) the most frequent sensory features applied in modelling machining processes, (4) the sensory feature selection and extraction methods for using relevant sensory information, (5) the design of experiments required to model a machining operation with the minimum amount of experimental data and (6) the main characteristics of several artificial intelligence techniques to facilitate their application/selection.

A review of machining monitoring systems based on artificial intelligence process models

Due to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining mild steel, using Response Surface Methodology (RSM). The experimentation was carried out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. A second order mathematical model, in terms of machining parameters, was developed for surface roughness prediction using RSM. This model gives the factor effects of the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA) to optimize the objective function. The GA program gives minimum and maximum values of surface roughness and their respective optimal machining conditions.

P.V.S. Suresh

Papers

A genetic algorithmic approach for optimization of surface roughness prediction model