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

Laser beam machining (LBM) is one of the most widely used thermal energy based non-contact type advance machining process which can be applied for almost whole range of materials. Laser beam is focussed for melting and vaporizing the unwanted material from the parent material. It is suitable for geometrically complex profile cutting and making miniature holes in sheetmetal. Among various type of lasers used for machining in industries, CO2 and Nd:YAG lasers are most established. In recent years, researchers have explored a number of ways to improve the LBM process performance by analysing the different factors that affect the quality characteristics. The experimental and theoretical studies show that process performance can be improved considerably by proper selection of laser parameters, material parameters and operating parameters. This paper reviews the research work carried out so far in the area of LBM of different materials and shapes. It reports about the experimental and theoretical studies of LBM to improve the process performance. Several modelling and optimization techniques for the determination of optimum laser beam cutting condition have been critically examined. The last part of this paper discusses the LBM developments and outlines the trend for future research.

/pdf/laser-beam-machining-a-review-kavveu0ges.pdf

Laser beam machining—A review

Application of Taguchi method in the optimization of end milling parameters

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.

An artificial neural network (p,d,q) model for timeseries forecasting

In this paper, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling The data used for the training and checking of the networks’ performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force Using feedforward artificial neural networks (ANNs) trained with the Levenberg–Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5×3×1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 186% and to be consistent throughout the entire range of values

Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

Optimizing feedforward artificial neural network architecture

This paper presents a highly interactive and immersive Virtual Reality Training System (VRTS) (“beWare of the Robot”) in terms of a serious game that simulates in real-time the cooperation between industrial robotic manipulators and humans, executing simple manufacturing tasks. The scenario presented refers to collaborative handling in tape-laying for building aerospace composite parts. The tools, models and techniques developed and used to build the “beWare of the Robot” application are described. System setup and configuration are presented in detail, as well as user tracking and navigation issues. Special emphasis is given to the interaction techniques used to facilitate implementation of virtual human–robot (HR) collaboration. Safety issues, such as contacts and collisions are mainly tackled through “emergencies”, i.e. warning signals in terms of visual stimuli and sound alarms. Mental safety is of utmost priority and the user is provided augmented situational awareness and enhanced perception of the robot’s motion due to immersion and real-time interaction offered by the VRTS as well as by special warning stimuli. The short-term goal of the research was to investigate users’ enhanced experience and behaviour inside the virtual world while cooperating with the robot and positive pertinent preliminary findings are presented and briefly discussed. In the longer term, the system can be used to investigate acceptability of H–R collaboration and, ultimately, serve as a platform for programming collaborative H–R manufacturing cells.

Design of a virtual reality training system for human–robot collaboration in manufacturing tasks

Milling performed with robots is quite demanding, even for low-strength materials, due to the high accuracy requirements, the generally high and periodically varying milling forces and the low stiffness of robots compared to CNC machine tools. In view of the generally improved recently robot stiffness, it is desirable to perform the milling operation in regions of the robot's workspace where manipulability, both kinematic and dynamic, is highest, thereby exhausting the robot's potential to cope with the process. In addition, by selecting the most suitable initial pose of the robot with respect to the workpiece, a reduction in the range of necessary joint torques may be reached, to the extent of alleviating the heavy requirements on the robot. Two genetic algorithms (GAs) are employed to tackle these problems. The values of several robot variables, such as joint positions and torques, which are needed by the genetic algorithms, are calculated using inverse kinematics and inverse dynamics models. In addition, initial positions and poses leading to singularities along the milling path are penalized and, thus, avoided. The first GA deals solely with robot kinematics to maximize manipulability. The second GA takes into account milling forces, which are computed numerically according to the particular milling parameters, to minimise joint torque loads.

George-Christopher Vosniakos

Papers

Predicting surface roughness in machining: a review

Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

Optimizing feedforward artificial neural network architecture

Design of a virtual reality training system for human–robot collaboration in manufacturing tasks

Improving feasibility of robotic milling through robot placement optimisation