Artificial Neural Network System for Predicting Cutting Forces in Helical-End Milling of Laser-Deposited Metal Materials

When machining difficult-to-cut metal materials often used to make sheet metal forming tools, excessive cutting force jumps often break the cutting edge. Therefore, this research developed a system of three neural network models to accurately predict the maximal cutting forces on the cutting edge in helical end milling of layered metal material. The model considers the different machinability of individual layers of a multilayer metal material. Comparing the neural force system with a linear regression model and experimental data shows that the system accurately predicts the cutting force when milling layered metal materials for a combination of specific cutting parameters. The predicted values of the cutting forces agree well with the measured values. The maximum error of the predicted cutting forces is 5.85% for all performed comparative tests. The obtained model accuracy is 98.65%

CONTROL STRATEGY FOR ASSURING CONSTANT SURFACE FINISH BY CONTROLLING CUTTING FORCES

The objective of this paper is to present surface roughness control strategy aimed at controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. The idea of this control structure is to merge the off-line cutting condition optimization and genetic programming (GP) model based surface roughness control. The off-line optimization integrates the neural network (NN) modelling of the objective function and particle swarm optimization (PSO) of cutting parameters. The GP method is conducted to find the correlation between surface roughness and the cutting force and to provide a functional relationship with controllable factors. Simulation setup and simulation results are presented to confirm the efficiency of the control model and its relevance to industry

Sustav predviđanja i odlučivanja u procesu nadzora alata primjenom ANFIS-a i neuronske mreže

The aim of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time using a combination of a neural decision system, an ANFIS tool wear estimator and a machining error compensation module. The principal presumption was that the force signals contain the most useful information for determining tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals. The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. The fundamental challenge to research was to develop a single-sensor monitoring system, reliable as a commercially available system, but much cheaper than the multi-sensor approach.Cilj ovog rada je prikazati sustav nadzora alata (TCM) koji može detektirati lom alata u stvarnom vremenu primjenjujući kombinaciju sustava za odlučivanje pomoću neuronske mreže, ANFIS procjena trošenje alata i modula za kompenzaciju pogreške u obradi. Glavna pretpostavka je da signali sila sadrže najkorisnije informacije za utvrđivanje stanja alata. Stoga se ANFIS model koristi za izdvajanje značajki o stanju alata kroz signale sila rezanja. Nakon faze učenja ANFIS model trošenja alata je integriran s neuronskom mrežom za utvrđivanje stanja istrošenosti alata (novi, istrošen). Neuronska mreža je korištena u TCM kao podloga za donošenja odluka, pri tomu izbjegavajući stanja prouzročena nepravilnostima u izmjerenim signalima. Predviđanje ukupne pogreške obrade s vrlo visokom točnošću pomoću modula za ugib alata i visokog postotka njegovog eliminiranja kroz predloženi proces kompenzacije pogreške

Predicting of Roll Surface Re-Machining Using Artificial Neural Network

The paper presents a model for predicting the roll wear in the hot rolling process. It includes all indicators from the entire continuous rolling line that best predict the roll wear in the hot rolling process. Data for model development were obtained from annual production on the first rolling stand of the continuous roll mill. The main goal of the research was to determine significant parameters that affect the wear of the roll in the process of hot rolling. It has been found that the amount of rolled material before the re-machining of the roll surface has the greatest impact on the life of the roll contour. Therefore, the amount of material rolled before re-machining of the roll was used to estimate the wear of the roll. An artificial neural network was used to predict this amount of rolled material and was validated using data from one-year production

Comparison of different optimization and process control procedures

This paper includes a comparison of different optimization methods, used for optimizing the cutting conditions during milling. It includes also a part of using soft computer techniques in process control procedures. Milling is a cutting procedure dependent of a number of variables. These variables are dependent from each other in consequence, if we change one variable, the others change too. PSO and GA algorithm are applied to the CNC milling program to improve cutting conditions, improve end finishing, reduce tool wear and reduce the stress on the tool, the machine and the machined part. At the end a summary will be given of pasted and future researches

Development and control of virtual industrial process using Factory IO and MATLAB

In today\u27s rapidly evolving business landscape, the strategic adoption of virtual manufacturing methods has emerged as a key driver for companies seeking to streamline operations and expedite product launches in a cost-effective manner. This progressive approach involves the creation of a synthetic and interconnected environment, empowered by advanced software tools and systems, including Virtual Reality and Simulation technologies, tailored to optimize industrial processes. Our methodology employs a unique combination of two simulation software tools: Factory I/O for process development and MATLAB for control program implementation. Furthermore, we explore the use of the Modbus TCP/IP communication protocol as the framework for seamless interaction between these software tools during simulation. This research presents practical insights into the transformative potential of virtual manufacturing, showcasing its real-world application in enhancing operational efficiency and agility within industrial settings

Tool cutting force modeling in ball-end milling using multilevel perceptron

This paper uses the artificial neural networks (ANNs) approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. A neural network algorithms are developed for use as a direct modeling method, to predict forces for ball-end milling operation. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling process. The training of the networks is preformed with experimental machining data. The predictive capability of using analytical and neural network approaches are compared using statistics, which showed that neural network predictions for three cutting force components were for 4% closer to the experimental measurements, compared to 11% using analytical method. Exhaustive experimentation is conduced to develop the model and to validate it. The milling experiments prove that this model can predict accurately the cutting forces in three Cartesian directions.The force model can be used for simulation purposes and for defining threshold values in cutting tool condition monitoring system

Real-time cutting tool condition monitoring in milling

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 cuttingforces 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\u27s 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 single sensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach

Adaptive self-learning controller design for feedrate maximization of machining process

An adaptive control system is built which controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. The paper discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy (Particle Swarm Optimization) in modeling and adaptively controlling the process of end milling. An overall approach of hybrid modeling of cutting process (ANfis-system), used for working out the CNC milling simulator has been prepared. The basic control design is based on the control scheme (UNKS) consisting of two neural identificators of the process dynamics and primary regulator. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear