Controlling system state is a purpose in system automation. Monitoring of tool wear is the same in machining process. Dimensional accuracy, surface quality of work, machining economy, machine tools life and machining forces were influenced by tool wear. So estimating the tool wear without stopping the operation can be a monitoring method. In this paper an on-line multisensoring approach for tool wear estimation in face milling proposed. Measuring of forces and motor current simultaneously is used. Many experimental tests have been done in different machining conditions and a back propagation neural network (bp) has been designed. This neural network can estimate the tool wear amount with a high accuracy by measuring of machining forces and motor current simultaneously. This method can be used in controlling and monitoring of machining process.

Tool wear is a purpose in machining process. Dimensional accuracy, surface quality of work, machining forces, vibration, tool life, and machine tools life are influenced by tool wear. In other hand estimating the tool wear without stopping the operation is an essential problem in system automation. In this paper an intelligent approach for tool wear estimation from machining forces is proposed. Many experimental tests have been done in different machining conditions and tool wear amount have been measured directly. With this data, a back propagation (bp) neural network have been designed and trained. This neural network can estimate the tool wear amount with a high accuracy receiving data of depth of cut, feed rate, revolution and machining forces in X, Y and Z coordinates.

The monitoring of tool wear is one of the important methods to improve the dimensional accuracy and economic aspects of machining. To achieve this, instead of measuring the tool wear, other parameters, which are related to tool wear, are used. In this paper, an intelligent system, based on neural networks, is presented by this method; the tool wear is estimated online with measuring spindle motor parameters, such as current and the speed of motor. Thus, the current and the speed of motor in different condition of machining (feed, depth of cut and rpm of tool) and wear were measured with practical experiments and the effects of tool wear on current and speed of motor is analyzed. Based on the results, a back propagation (BP) neural network is developed and trained. Using this network, the tool wear could be estimated while machining in different conditions, measuring current and speed of motor. This system could be used to control and monitor the machining process.

There is a requirement to find accurate parameters to accomplish precise dimensional accuracy, excellent surface integrity and maximum MRR. This work studies the influence of various cutting parameters on output parameters like Cutting force, Surface roughness, Flatness, and Material removal rate while face milling. A detailed finite element model was developed to simulate the face milling process. The material constitutive behavior is described by Johnson-Cook material model and the damage criteria is established by Johnson-Cook damage model. The result indicate significant effects of all three cutting parameters on MRR and both feed rate and depth of cut have significant effect on cutting force. Also, feed rate has significant effect on PEEQ and none of the parameters have effect on flatness.

An approach for modeling of cutting force in ball-end milling process is presented in this paper. This model has the capability of estimating the cutting forces for various cutting conditions. A commercially available geometric engine (ACIS) is used to represent the cutting edge, cutter and updated part. (To determine cutting edge engagement for each tool rotational step, the intersections between the cutting edge and boundary of the contact face, and between tool and updated part, are determined). The engaged portion of the cutting edge is divided into small differential oblique cutting edge segments and the cutting force components are calculated by summing up the differential cutting forces, A series of experiments were performed to verify the proposed approach.

An approach for modeling of cutting force in ball-end milling process is presented in this paper. This model has the capability of estimating the cutting forces for various cutting conditions. A commercially available geometric engine (ACIS) is used to represent the cutting edge, cutter and updated part. To determine cutting edge engagement for each tool rotational step, the intersections between the cutting edge with boundary of the contact face between tool and updated part are determined. The engaged portion of the cutting edge is divided into small differential oblique cutting edge segments and the cutting force components are calculated by summing up the differential cutting forces. A series of experiments were performed to verify the proposed approach.

During the milling process, one of the most important factors in reducing tool life expectancy and quality of workpiece is the chattering phenomenon due to self-excitation. The milling process is considered as a MIMO strongly coupled nonlinear plant with time delay terms in cutting forces. We stabilize the plant using two independent Emotional Learning-based Intelligent Controller (ELIC) in parallel. Control inputs are considered as forces Ux and Uy in two directions x and y, which are applied by the piezoelectrics. The ELIC consists of three elements; Critic, TSK controller and the learning element. The results of the ELIC have been compared with a Sliding Mode Controller (SMC). The simulation for the nominal plant shows better performance of the ELIC in IAE and ITSE values at least 86% in the x-direction and 79% in the y-direction. Similar simulation for an uncertain plant also shows an improvement of at least 89% in the x-direction and 97% in the y-direction.

This paper presents an approach to analytically determine the most energy efficient toolpath strategy in mechanical machining. This was achieved by evaluating the electrical energy requirement of the NC codes generated for the zag, zigzag, and rectangular contour toolpath strategies. The analytical method was validated by performing pocket milling on AISI 1018 steel with the considered toolpaths using a 3-axis Takisawa Mac-V3 milling machine. The rectangular contour toolpath was the most efficient in terms of the electrical energy demand of the feed axes and cycle time. Pocket milling with the zigzag toolpath strategy resulted in higher electrical energy demand of the feed axes and cycle time by 2% due to acceleration and deceleration characteristics of the machine tool feed axes execution at corners of the toolpath strategy adopted. Also, the electrical energy demand of the feed axes and cycle time for the zag toolpath were higher by 14% and 8%, respectively, due to the number of tool retracts as a result of the executed toolpath strategy. The experimental validation results showed good agreement with the analytical approach presented in this study. It can be deduced that for sustainable machining, the rectangular contour toolpath should be adopted since it has less tool retractions irrespective of the toolpath strategy adopted for machining. This could further enhance the selection of optimum green parameters by shop floor process engineers for sustainable manufacture of products.

The analysis and simulation of the manufacturing process require extensive and complicated computations. Nowadays, computer resources and computational algorithms have reached the stage where they can model and simulate the problem efficiently. One of the important processes in manufacturing is machining. In this research, the end-milling process, which is one of the most complex and widespread processes in machining, is chosen. The most important parameters in end-milling are surface roughness and surface location errors. Comprehensive simulation software is developed to model the end-milling process, in order to anticipate the finishing parameters, such as surface roughness and errors. The proposed algorithm takes into account cutting conditions, such as feed, doc, woc and tool run out etc. In addition, the dynamic simulation module of the software can accurately model the flexible end-mill tool, the milling cutting forces and regeneration of the waviness effects, in order to construct a realistic surface texture model. The software can accurately determine the most commonly used index of surface roughness parameters, such as Ra, P.T.V. and surface errors.

Endmilling is a type of machining tool for chipping the surfaces of parts, which has received attention due to its wide application in industries such as molding. Therefore, today, the need of the industry to find the optimal parameters of the process is felt so that the quality of the desired surface can be achieved. In general, the selection of effective parameters in any milling process significantly affects the surface quality of a finished part. In this research, using E-fast statistical sensitivity analysis method, the simultaneous influence of input parameters including spindle speed, depth of cut, and feed rate on the output parameter of surface roughness for the samples has been investigated quantitatively. Machining experiments have been carried out under different cutting parameters as defined in steady state conditions for the milling tool. surface roughness and vibration rate of machining with non-linear quadratic forms,It has been modeled based on the cutoff parameters and its interactions through several regression analysis methods. The results of this research showed that the spindle speed time parameter is known as the most influential parameter on the surface roughness with 67% influence. It was also observed that the feed rate parameter with 30% effect of cutting depth with 3% are known as the second and third influencing parameters on surface roughness.