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 present work concerns an experimental study of hard turning with CBN tool of AISI 52100 bearing steel, hardened at 64 HRC. The main objectives are firstly focused on delimiting the hard turning domain and investigating tool wear and forces behaviour evolution versus variations of workpiece hardness and cutting speed. Secondly, the relationship between cutting parameters (cutting speed, feed rate and depth of cut) and machining output variables (surface roughness, cutting forces) through the response surface methodology (RSM) are analysed and modeled. The combined effects of the cutting parameters on machining output variables are investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of machining parameters with respect to objectives (surface roughness and cutting force values). Results show how much surface roughness is mainly influenced by feed rate and cutting speed. Also, it is underlined that the thrust force is the highest of cutting force components, and it is highly sensitive to workpiece hardness, negative rake angle and tool wear evolution. Finally, the depth of cut exhibits maximum influence on cutting forces as compared to the feed rate and cutting speed.

Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool

Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Application of soft computing techniques in machining performance prediction and optimization: a literature review

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this paper , T aguchi method is applied to find optimum process parameters for end milling while hard machining of hardened steel. A L 18 array , signal-to-noise ratio and analysis of variance (ANOV A) are applied to study performance characteristics of machining parameters (cutting speed, feed, depth of cut and width of cut) with consideration of surface finish and tool life. Chipping and adhesion are observed to be main causes of wear . Results obtained by T aguchi method match closely with ANOV A and cutting speed is most influencing parameter . Multiple regression equations are formulated for estimating predicted values of surface roughness and tool wear

http://nopr.niscair.res.in/bitstream/123456789/5301/1/JSIR%2068%288%29%20686-695.pdf

Taguchi method and anova: an approach for process parameters optimization of hard machining while machining hardened steel

Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts

Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques

Hard turning with ceramic cutting tool has several benefits over grinding process such as elimination of coolant, reduced processing costs, improved material properties, reduced power consumption and increased productivity. Despite its significant advantages, hard turning can not replace all grinding due to lack of data concerning surface quality and tool wear and hence there is a need to study the machinability characteristics in high precision and high-hardened components. An attempt has been made in this paper to analyze the effects of depth of cut and machining time on machinability aspects such as machining force, power, specific cutting force, surface roughness and tool wear using second order mathematical models during turning of high chromium AISI D2 cold work tool steel with CC650, CC650WG and GC6050WH ceramic inserts. The experiments were planned as per full factorial design (FFD). From the parametric analysis, it is revealed that, the CC650WG wiper insert performs better with reference to surface roughness and tool wear, while the CC650 conventional insert is useful in reducing the machining force, power and specific cutting force.

Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts

The current article presents an investigation into predicting tool wear in hard machining D2 AISI steel using neural networks. An experimental investigation was carried out using ceramic cutting tools, composed approximately of Al2O3 (70%) and TiC (30%), on cold work tool steel D2 (AISI) heat treated to a hardness of 60 HRC. Two models were adjusted to predict tool wear for different values of cutting speed, feed and time, one of them based on statistical regression, and the other based on a multilayer perceptron neural network. Parameters of the design and the training process, for the neural network, have been optimised using the Taguchi method. Outcomes from the two models were analysed and compared. The neural network model has shown better capability to make accurate predictions of tool wear under the conditions studied.

Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel

Hard turning is an attractive replacement for grinding operations due to numerous advantages such as low capital investment, shorter setup time, higher material removal rate, better surface integrity, and elimination of cutting fluids. As a potential alternative process, there is a need to assess the machinability in high-precision and high-hardened components. The current study establishes the relationships between the cutting conditions (cutting speed, feed rate, and machining time) on machinability aspects (machining force, power, specific cutting force, surface roughness, and tool wear). The response surface methodology-based mathematical models are proposed for modeling and analyzing the effects of process parameters on machinability during turning of high chromium AISI D2 cold work tool steel using CC650WG wiper ceramic inserts. The experiments have been planned as per full factorial design. From the parametric analysis, it is revealed that the power increases with increase in feed rate, while the s...

Luis Figueira

Papers

Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts

Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques

Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts

Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel

Analysis of Machinability During Hard Turning of Cold Work Tool Steel (Type: AISI D2)