Number Of Machine Parameters Research Articles

Prediction and controlling of roundness during the BTA deep hole drilling process: Experimental investigations and fuzzy modeling

Abstract In view of the large number of machining parameters that influence the system dynamics, experimental study is performed using the Boring Trepanning Association's (BTA) deep hole drilling process by varying the cutting parameters (workpiece rotational frequency, drilling feed rate, and tool usage), which are considered as input of the process. The specimens were then tested for roundness, which is considered as an output of the process. In this study, first order Sugeno-fuzzy models are designed by using the cutting parameters as input data and the roundness as output data. The relation between the input and the output is created to find the influence of the input parameters on the output surface quality in terms of roundness error. Hence, the best cutting condition in deep hole drilling is designated to improve the output. A scheme is recommended to precisely create the relationship between the different cutting parameters using subtractive clustering procedure based on the first order Sugeno fuzzy model. Minimum error model with lesser numbers of rules for roundness error is established by enumerative exploration of the clustering parameters. The resulted model with best clustering factors is then attuned by using adaptive neuro-fuzzy inference system (ANFIS).

Tool wear rate optimization in PMEDM using titanium powder by Taguchi method for die steels

Powder mixed electrical discharge maching (PMEDM) is a complex machining process which is controlled by a number of machining parameters. Each machining parameter has its own influence on performance of the process. For achieving the best performance of the electrical discharge machining (EDM) process, it is crucial to carry out parametric design responses such as Metal Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness(SR). The objective of this paper is to optimization of input parameters for the TWR in PMEDM using powder titanium are presented. The Taguchi method was applied to the processing parameters to investigate the following: workpiece material, tool material, polarity, pulse-on time, current, pulse-off time, and powder concentration. The analysis used the Taguchi method and given the optimal value for TWR with respective parameters. Electrode material affected the strongest factor, the Taguchi coefficient, S/N of TWR. And the optimal value of TWR was 3.092 mm3/min. Results from optimization calculations and experimentation have demonstrated high accuracy and efficiency.

Evaluation of 3- and 5-axis sculptured surface machining in CAM environment through design of experiments

Sculptured surface machining (SSM) is an operation widely applied to several industrial fields such as aerospace, automotive and mould/die. The number of the parameters and strategies involved to program such machining operations can be enormously large owing to surface complexity and advanced design features. This study focuses on the examination of machining strategies and related parameters for the assessment of roughing and finishing stages. A fractional factorial design implementing an L27 Taguchi orthogonal array (OA) was established to conduct machining experiments with the use of a computer-aided manufacturing (CAM) software. Fractional factorial design specifics involve the statistical elimination of unimportant parameters, thus reducing experimental runs without the loss of useful information. Two scenarios were considered to machine a sculptured part; one involving 3-axis roughing/3-axis finish machining experiments and the other one involving 3-axis roughing/5-axis finish machining experiments. Roughing operation was common for both scenarios. The problem was subjected to discrete technological constraints to reflect the actual industrial status. For each machining phase, two quality objectives reflecting productivity and part quality were determined. Roughing experiments were tested to minimise machining time and remaining volume, whilst finishing experiments were subjected to minimise machining time and surface deviation between the designed and the machined 3D model. Quality characteristics were properly weighted to formulate a single objective criterion for both machining phases. Results indicated that DOE applied to CAM software, enables NC programmers to have a clear understanding about the influence of process parameters for SSM operations, thus generating efficient toolpaths to improve productivity, part quality and process efficiency. Practically the work contributes to machining improvement by through the proposition of machining experimentation methods using safe and useful platforms such as CAM systems; the investigation of approaches to avoid problem oversimplification mainly when large number of machining parameters should be exploited and the evaluation of quality criteria which allow their assessment directly form CAM software.