A review of optimization techniques in metal cutting processes

Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.

Review: A review of data mining applications for quality improvement in manufacturing industry

Research highlights? The surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut by full factorial experimental design. ? Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. ? The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model.

Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method

Transitioning to sustainable production – part II: evaluation of sustainable machining technologies

This paper presents the ANN model for predicting the surface roughness performance measure in the machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring surface roughness. A revision of several previous studies associated with the modelling issue is carried out to assess how capable ANN is as a technique to model the problem. Based on the studies conducted by previous researchers, the abilities and limitations of the ANN technique for predicting surface roughness are highlighted. Utilization of ANN-based modelling is also discussed to show the required basic elements for predicting surface roughness in the milling process. In order to investigate how capable the ANN technique is at estimating the prediction value for surface roughness, a real machining experiment is referred to in this study. In the experiment, 24 samples of data concerned with the milling operation are collected based on eight samples of data of a two-level DOE 2^k full factorial analysis, four samples of centre data, and 12 samples of axial data. All data samples are tested in real machining by using uncoated, TiAIN coated and SN"T"R coated cutting tools of titanium alloy (Ti-6A1-4V). The Matlab ANN toolbox is used for the modelling purpose with some justifications. Feedforward backpropagation is selected as the algorithm with traingdx, learngdx, MSE, logsig as the training, learning, performance and transfer functions, respectively. With three nodes in the input layer and one node in the output layer, eight networks are developed by using different numbers of nodes in the hidden layer which are 3-1-1, 3-3-1, 3-6-1, 3-7-1, 3-1-1-1, 3-3-3-1, 3-6-6-1 and 3-7-7-1 structures. It was found that the 3-1-1 network structure of the SN"T"R coated cutting tool gave the best ANN model in predicting the surface roughness value. This study concludes that the model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.

Prediction of surface roughness in the end milling machining using Artificial Neural Network

The paper proposes a new optimization technique based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA. It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.

Optimization of cutting process by GA approach

Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.

Optimization of cutting conditions during cutting by using neural networks

Approach to optimization of cutting conditions by using artificial neural networks

Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.

/pdf/real-time-cutting-tool-condition-monitoring-in-milling-2zxwophzll.pdf

Real-Time Cutting Tool Condition Monitoring in Milling

The process of surface roughness formation is complex and dependent on numerous factors. The analysis of the latest reports on the subject shows that mathematical relationships used for determining surface irregularities after turning and milling are not complete or accurate enough and, therefore, need to be corrected. A new generalized mathematical model of roughness formation was developed for surfaces shaped with round-nose tools. The model provides us with a quantitative analysis of the effects of the tool representation, undeformed chip thickness, tool vibrations in relation to the workpiece, tool runout (for multicutter tools) and, indirectly, also tool wear. This model can be used to prepare separate models for most of the typical machining operations. Surface roughness is represented here by two parameters Ra and Rt. Simulations carried out for this model helped to develop nomograms which can be used for predicting and controlling the roughness Ra of surfaces sculptured by face milling.

Franci Cus

Papers

Optimization of cutting process by GA approach

Optimization of cutting conditions during cutting by using neural networks

Approach to optimization of cutting conditions by using artificial neural networks

Real-Time Cutting Tool Condition Monitoring in Milling

A Model of Surface Roughness Constitution in the Metal Cutting Process Applying Tools with Defined Stereometry