Machining Data Handbook Research Articles

Machining Data Selection by Using a Fuzzy Logic Model

The fuzzy model has been developed for machining parameter selection. The correlation between the hardness of the work-piece and cutting velocity is not exact, but a proper relationship can be established by using a developed model. The fuzzy model was applied to machining data for carbon steel (wrought) that are collected from the ASM handbook, and a proper relationship between handbook data and predicted data by the fuzzy application was obtained. The model was developed by considering input hardness and output speed. A linear fuzzy equation has been proposed to represent the machining data with good correlation. The aim of the present work is to develop a fuzzy logic model to enable the computerization process of machining data that is available in the machining data handbook. As a result, a fuzzy logic-based expert system for machining data selection can be created.

A METHODOLOGY FOR ECONOMIC OPTIMIZATION OF PROCESS PARAMETERS IN CENTERLESS GRINDING

Centerless grinding is an abrasive machining process commonly used in the aerospace and automotive industries for shaping axisymmetric components that require a high dimensional accuracy and a smooth surface finish. For this high volume manufacturing operation, process parameters have historically been selected conservatively using machining data handbooks or on a trial-and-error basis according to operator experience without explicit consideration of how the selected settings affect the productivity or cost of the operation. To improve operation efficiency, a science-based approach for selecting the optimum process parameters must be developed to replace traditional industrial practices. In this article, a methodology combining several analytical and experimental techniques is presented for optimizing the key process parameters in plunge centerless grinding of Inconel 718 and Ti-6Al-4V superalloy fasteners in two phases: (i) modeling of process constraints using experimental design and statistical regression, and (ii) determination of optimum grinding conditions in the feasible operating region using a simulation algorithm based on classical machining economics theory. The results show that the implementation of optimum material removal rates can lead to an appreciable reduction in the cost of the operation while satisfying the constraints imposed on the machine tool and the workpiece quality.

Fuzzy Logic Modeling For Peripheral End Milling Process

Fuzzy logic has been deployed in this study to predict cutting speed and feed rate of peripheral end milling process at given hardness of material, radial depth of cut and cutter diameter. There were two types of fuzzy models had been designed and developed throughout in this study. The first developed fuzzy model (Model A) was two inputs with two outputs while the second developed model (Model B) was three inputs with two outputs. Hardness of material and radial depth of cut had been chosen as the inputs for the Model A. Cutter diameter then had been introduced as the third input besides material hardness and radial depth of cut for Model B. Two types of fuzzy model were designed to evaluate the effectiveness and efficiency of introducing cutter diameter as another input into the system. Both types of fuzzy model had been tested and validated with the recommended data obtained from Machining Data Handbook (MDH). The results showed a very good correlation between predicted data and the data from MDH. Model B had been chosen as the best fuzzy model to represent peripheral end milling process although Model A had performed better; 3.78% and 2.06% (Model A) compared to 3.81% and 2.27% (Model B) for cutting speed and feed percentage errors respectively. This is due to less development time and the ability to predict cutting speed and feed at any given cutting tool diameter.

Cutting Parameter Optimization for Multi-Pass Milling Operations by Genetic Algorithms

Parameter optimization in multi-pass cutting operations involves optimal selection of cutting speed, feed rate, depth of cut, and the number of passes, duo to significant influence of these parameters on the quality of machined parts and machining economics. In this paper, a non-linear mathematical model based on minimum production cost for multi-pass milling operations is presented. The unwanted material is removed by one finishing pass and one or multiple roughing passes depending on the total depth of cut. Various realistic constraints are considered when developing the model. Optimal values of machining parameters are found by Genetic Algorithms. An example is presented to illustrate the optimization model and solution approach. The method yields lower unit production costs compared with the results from the literature and machining data handbook.

Optimal Selection of Machining Parameters for Multi-Pass Turning Operations

In this paper, the parameter optimization problem for multi-pass turning operations is studied. A mathematical model based on the minimum production cost criterion is developed. The unwanted material is removed by one finishing pass and at least one roughing passes depending on the total depth of cut. Maximum and minimum allowable cutting speeds, feed rates and depths of cut, as well as tool life, surface roughness, cutting force and cutting power consumption are constraints of the model. Optimal values of machining parameters are found by three methods: integer programming, nonlinear programming, and genetic algorithms. An example is presented to illustrate the effectiveness of the optimization model and solution methods. The model generates lower unit production costs compared with the results from the literature and machining data handbook.

A Nonlinear Mathematical Model for Turning Parameter Optimization Based on Minimum Production Cost

Cutting parameters including cutting speed, feed rate, depth of cut and the number of passes have significant influence on both machining quality and machining economics. Cutting parameter optimization involves optimal selection of a combination of these parameters. It is an essential part of a computer-aided manufacturing system. In this paper, an optimization model based on minimum production cost for multi-pass turning operations is developed. Various realistic machining conditions and machining requirements are incorporated in the model as constraints. Optimal solutions are found by a nonlinear programming solution approach. A turning example is presented to test the model. Compared with the literature and the cutting regimes recommended in machining data handbook, our model and solution method are simple and generate much lower unit production costs.

A composite fitting model of discrete handbook data for peripheral end milling

Machining data handbooks are important reference books in the machining industry, as they provide recommended process parameter values for common machining operations. The machining data, although covering a wide range of relevant cutting conditions, are only listed under discrete cutting conditions. Rough interpolation-based calculations are often needed in order to estimate the process parameter values at the desired cutting condition. In this work, a composite fitting model is presented to fit a composite functional curve through the discrete handbook data of recommended cutting speeds and feeds with respect to the cutting condition of radial depth of cut for peripheral end milling. The objective is to establish a functional relationship from the handbook data such that recommended cutting speed and feed can be obtained for any given radial depth of cut. According to the tabulated layout of the machining data, the entire range of the radial depth of cut is divided into three segments having distinctive formulations and trends. Constraints are then imposed to preserve the trends and smoothly connect the adjacent segments. As a possible application of the presented model, a case study of machining a rectangular pocket is provided. Machining time of a potential process plan is readily evaluated based on the cutting speeds and feeds obtained from the composite model.

Networked Fuzzy Control System for a High-Performance Drilling Process

The high-performance drilling (HPD) process has a significant impact on production in many industries, such as the automotive, die/mold and aerospace industries. However, cutting conditions for drilling are generally chosen from a machining-data handbook, requiring operator experience and skill. In order to improve drilling efficiency while preserving tool life, the current study focuses on the design and implementation of a simple, optimal fuzzy-control system for drilling force. The main topic of this study is the design and implementation of a networked fuzzy controller. The control system consists of a two-input (force error and change of error), single-output (feed-rate increment) fuzzy controller with nine control rules, the sup-product compositional operator for the compositional rule of inference, and the center of area as the defuzzification method. The control algorithm is connected to the process through a multipoint interface (MPI) bus, a proprietary programming, and communication interface for peer-to-peer networking that resembles the PROFIBUS protocol. The output (i.e., feed-rate) signal is transmitted through the MPI; therefore, network-induced delay is unavoidable. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) criterion. The goal is to obtain the optimal tuning parameters for the input scaling factors while minimizing the ITAE performance index. In this study, a step in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. The optimization is performed using the Nelder–Mead simplex (direct search) method. The main advantage of the approach presented herein is the design of a simple fuzzy controller using a known maximum allowable delay to deal with uncertainties and nonlinearities in the drilling process and delays in the network-based application. The results demonstrate that the proposed control strategy provides an excellent transient response without overshoot and a slightly higher drilling time than the CNC working alone (uncontrolled). A major issue in high performance drilling is the increase in cutting force and torque that occurs as the drill depth increases. Therefore, the fuzzy-control system reduces the influence of these factors, thus eliminating the risk of rapid drill wear and catastrophic drill breakage.

Investigating Machining Impact on Fatigue Variance: A Machining Quality Perspective

Fatigue life of nominally identical structures under nominally identical loading conditions can scatter widely. This study has investigated the impact of machining processes on such scatters. After Ti 6Al-4V samples were surface ground and face turned, they were subject to constant amplitude four-point bending fatigue tests under room temperature. The best-case scenario of process capability ratios of fatigue for these samples were evaluated with assumed tolerances of fatigue life. Based on these ratios, the numbers of nonconforming parts were estimated. Under the machining conditions from the Machining Data Handbook (Machining Data Center, Cincinnati, 1980), up to 39% of samples due to one process are expected to be nonconforming, whereas only up to 0.6% of samples due to the other process are expected to be nonconforming. The ramifications in terms of cost for machining quality control due to the different capability ratios have been discussed. The current findings indicate an urgent need to further the study of this issue in a scientific manner.

Strategy for generalizing the development of alloy steel fuzzy model for machinability data selection

The paper proposes several fuzzy models for representing the turning process of alloy steel. These fuzzy models serve the purpose of predicting the optimum machinability data (cutting speed and feed rate) for a given work-piece material and cutting tools. The fuzzy models were developed based on the relationship of two-input-two-output, where the inputs are material hardness and depth of cut, and the outputs are cutting speed and feed rate. A new strategy was suggested for generalization of fuzzy model development. Initially, a reference fuzzy model was developed, and then other fuzzy models were developed based on the reference fuzzy model by using the suggested strategy. This strategy is useful and required less effort in developing any related new fuzzy models. Besides that, linear interpolation method has also been incorporated for eliminating the effort in designing the fuzzy rules. The predicted cutting speed and feed rate are compared with the data obtained from “Machining Data Handbook” [Metcut Research Associates Inc., Machining Data Handbook, second ed., vol. 1–2, Cincinnati, 1980], and a good correlation has been shown.

Adjustment approach for fuzzy logic model based selection of non-overlapping machining data in the turning operation

In the Machining Data Handbook [Machining Data Handbook, vol. 2, 3rd ed., Metcut Research Associates Inc., Cincinnati, 1980] work-piece materials are classified in various groups covering a wide range of materials according to their material hardness (BHN) and cutting speed according to their depths of cut. The handbook provides machining parameters for different tool work-piece combinations. For each work-piece material and hardness, one may scan through different possible tool materials. This paper describes an adjustment approach applied to a fuzzy logic based model for selecting cutting speed where the machining data for speed ranges between the different depths cut are not overlapped. For this study data for two different materials and four different types of cutting tools are used to illustrate the adjustment approach. In material type one the cutting speed ranges for different depths of cut are overlapped and when a fuzzy logic based model was applied, it produced the cutting speed data providing good correlation with handbook data. In material type two the cutting speed ranges for different depths of cut are not overlapped. In this situation the cutting speed data are superimposed and results were adjusted according to their own ranges.

Data selection for turning carbon steel using a fuzzy logic approach

This paper describes the development of a model based on fuzzy logic for selecting cutting speed in single-point turning operations. The model is based on the assumption that the relationship between the hardness and depth of cut and the recommended cutting speed is an imprecise one and can be described and evaluated by the fuzzy set theory. The model has been applied to data extracted from the Machining Data Handbook [1]. For the turning process software has been developed based on the fuzzy model for selecting turning parameters in the machining process. The system was developed on a PC using Visual basic programming language. In this paper, the model has been applied to three types of steel materials and four different types of cutting tool materials. Recently the fuzzy logic principles have been applied for selecting cutting conditions in the turning process in machining operations [2], where the rules were based on a one to one relationship. The model developed and described in this paper is based on a matrix system where the output depends on the combination of two inputs. It is shown that the software is capable of specifying data providing very good correlation between the Handbook Data [3] and that predicted using fuzzy logic model.

Off-line feed rate scheduling using virtual CNC based on an evaluation of cutting performance

This paper presents an advanced technology to automatically determine optimum feed rates for 2 1 2 axis CNC machining, without requiring the expertise of a machinist or the information contained in a machining data handbook. Present CAM technology does not consider important physical properties such as cutting forces and machined surface errors. However, the virtual machining system developed in this study can simulate real machining for a given set of NC codes. An analytical model for off-line feed rate scheduling is formulated to improve productivity and machining accuracy. Using this model, it is possible to regulate the cutting force, which drastically improves the overall form accuracy of the machined surface.

Generation and Evolutionary Learning of Cutting Conditions for Milling Operations

1. The modification of recommended cutting conditions obtained from a machining data handbook. 2. The incremental learning of obtained cutting conditions using fuzzy ARTMAP neural networks. 3. The substitution of better cutting conditions for those learned previously by a proposed replacement algorithm. Various simulations illustrate the performance of GELCC, and then the simulation results for a given part are provided and discussed.

Fuzzy logic based data selection for the drilling process

Recently, fuzzy logic principles have been applied for selecting cutting conditions in the turning process in machining operations [Hashmi, EL Baradie, Ryan, Fuzzy logic based intelligent selection of machining parameters, in: Proceedings of 22nd Conference on Computers and Industrial Engineering (ICC&IE), The American University in Cairo, Egypt, December 1997, pp 309–312]. Theoretical calculations were performed on data extracted from the Machining Data Handbook [Machining Data Handbook, third ed., vol. 2, Metcut Research Associates, Inc., Cincinnati, OH, 1980]. This handbook provides a vast information source that includes selection of appropriate cutting tools and machining parameters, e.g. cutting speed, feed rate, and depth of cut, etc. for different tool work combinations. For the turning process, the results showed a very good correlation between the Machining Data Handbook’s recommended cutting speed values and that predicted using a fuzzy logic model. The strategy and action of the skilled machine tool operator for selecting cutting speed and depth of cut can be described by the fuzzy set theory. In this paper, the fuzzy logic model has been used in the drilling operation to select drilling speeds for three different materials. The relationship between a given material hardness and drilling speed can be described and evaluated by the fuzzy relation for different cutting tool materials and different hole diameters and feed rates.

Generalized fuzzy model for metal cutting data selection

Metal cutting data selection is complex and cannot be easily formulated to meet design specification by any mathematical model. Optimized Machinability data is obtained from a skilled machine tool operator’s experience and intuition. A rule-based expert system and materials database have been incorporated into many CAD/CAM systems in order to obtain optimal machining parameters. Fuzzy logic is a better tool to describe the strategy and action of the skilled operator when selecting the metal cutting data. A first prototype of such a system was developed by the present authors. This paper further describes development of fuzzy models and their feasibility. Development of several models for different cutting tools is presented and discussed. The models are validated with the Machining Data Handbook. The feasibility of a generalized fuzzy model for all the cutting tools is also presented.

On-Line Optimization of the Turning Process Using an Inverse Process Neurocontroller

This paper presents a feedback neurocontrol scheme that uses an inverse turning process model to synthesize optimal process inputs. The inverse process neurocontroller is implemented in a multilayer feedforward neural network. On-line adjustments of feed rate and cutting speed parameters are carried out based on a cost/quality performance index, estimated from force and vibration sensor measurements. Both non-adaptive and adaptive neurocontrol schemes are considered. The simulations and experimental investigations presented herein demonstrated the effectiveness of neural networks for controlling and optimizing turning operations. Applied to single point turning of a typical finishing cut, the final dimensions and surface finishes were found to be better by 40 and 80 percent respectively, while productivity was increased by 40 percent over the conditions proposed in machining data handbooks. This approach is also applicable to several other manufacturing processes.

A fuzzy logic model for machining data selection

This paper describes the development stages of a fuzzy logic model for metal cutting. The model is based on the assumption that the relationship between the hardness of a given material and the recommended cutting speed is an imprecise relationship, and can be described and evaluated by the theory of fuzzy sets. The model has been applied to data extracted from the Machining Data Handbook [Metcut Research Associates, 3rd edn, Vols 1 and 2 (1980)], and a very good correlation was obtained between the handbook data and that predicted using the fuzzy logic model. The objective of the model is to facilitate the computerization process of the vast machining information contained in machining data handbooks. Also, the proposed model suggests the possibility of developing an expert system for machining data selection based on fuzzy logic.

A neural-network system for predicting machining behaviour

This paper describes a case study the aim of which was to apply neural-network technology to the selection of machining parameters and to the prediction of machining performance in metal cutting. The project involved the development of a back-propagation neural network using Neuralwork Professional II, a multi-paradigm, prototyping and development system. The network was trained using data from the Machining Data Handbook, after training the network being able to select appropriate machining conditions and to predict cutting forces and surface finish for a given work material. The results so obtained are analysed and discussed. A novel feature of this work is the development of an on-line implementation of the trained network using the C programming language. Current limitations of neural-network technology with respect to engineering application are discussed also.

Determining the cutting conditions for sculptured surface machining

This paper deals with a new method for determining cutting conditions for the machining of 3D sculptured surface shapes with a ball endmill. Since the interaction area between workpiece and cutting tool changes during ball endmill machining, the cutting conditions have to be changed accordingly. To determine the spindle speed, the tool-workpiece contact area is obtained from the depth of cut, path interval, and cutter contact (CC) data. The spindle speed is determined so that the tangential cutting speed of the centroid of the tool-workpiece contact area becomes the value suggested in the machining data handbook. The feedrate is determined so that the material removal rate is maximised within the range of the cutting-force constraint. The practical feasibility of the proposed scheme has been verified through experiments performed on a workpiece with sculptured surfaces. The test result shows that the new method can be used to increase the tool life, and improve the production rate.