INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING

In this article, an adaptive neural controller for the ball end-milling process is described. Architecture with two different kinds of neural networks is proposed, and is used for the on-line optimal control of the milling process. A BP neural network is used to identify the milling state and to determine the optimal cutting inputs. The feedrate is selected as the optimised variable, and the milling state is estimated by the measured cutting force. The adaptive controller is operated by a PC and the adjusted feedrates are sent to the CNC. The purpose of this article is to present a reliable, robust neural controller aimed at adaptively adjusting feed-rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. The goal is also to obtain an improvement of the milling process productivity by the use of an automatic regulation of the cutting force. Numerous simulations are conducted to confirm the efficiency of this architecture. The proposed architecture for on-line determining of optimal cutting conditions is applied to ball end-milling in this paper, but it is obvious that the system can be extended to other machines to improve cutting efficiency

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

Planiranje in optimizacija proizvodnje z informacijskim sistemom APO (Advanced Planning and Optimisation)

As the scope of logistics operations in the company increases from day to day it is necessary to provide quality and reliable IT support to operational work. Selection, implementation and the usage of the support tools is one of the critical tasks. The main tpoic of the article is upgrading the ERP system SAP R3 with the new system of advanced planning and optimization (Advanced Planning and Optimization - APO). In the company Krka, d.d. the business information system SAP R3 is used for to support most processes. For the management and control of production, the system Werum PAS-X is used. In the Supply Chain the APO, which is an upgrade of SAP R3 and is also a leading system for planning in the company has been implemented. All the three systems which are also integrated with each other constitute the basis of a new modern, efficient and transparent Supply Chain system.Podjetje za svoje uspešno delovanje potrebuje dobro informacijsko podporo, ki strateškemu in taktičnemu managementu prinaša lažjo in učinkovito sprejemanje odločitev, saj bodo le-te zasnovane na točnih, preglednih in pravočasnih podatkih. Izhodišče prispevka je nadgradnja informacijskega sistema SAP R3 z novim sistemom naprednega planiranja in optimizacije (Advanced Planning and Optimisation, v nadaljevanju APO). Ker se obseg logističnih operacij iz dneva v dan v podjetju povečuje, je nujno zagotoviti kakovostno in zanesljivo informacijsko podporo tudi operativnemu delu. Odločitve o izbiri in načinu uporabe posameznih orodij so ena izmed kritičnih nalog. V podjetju Krka d.d. se uporablja poslovni informacijski sistem SAP R3 za večino procesov, za vodenje in nadzor proizvodnje se uporablja proizvodni informacijski sistem Werum PAS-X, v logističnem centru se uporablja poleg SAP R3, tudi APO, ki je nadgradnja SAP R3 in je tudi vodilni sistem za planiranje v podjetju. Vsi trije sistemi, ki so tudi med sabo integrirani, so postavili temelje novemu sodobnemu, učinkovitemu in transparentnemu sistemu izvajanja logističnih in drugih operacij

Teorijski i numerički pristup određivanju termičkih i mehaničkih naprezanja disk kočnica na vlakovima

This paper shows a thermal and tension analysis of a brake disc for railway vehicles. The FEM (Finite Element Method) was used to carry out the analysis. The analysis deals with one cycle of braking – braking from maximum velocity to a standstill, cooling off faze and then accelerating to maximum velocity and again braking to a standstill. This case of braking represents a part of a railway working conditions. The main boundary condition in this case was the entered heat flux on the braking surface, the centrifugal load and the force of the brake clamps. One type of disc was used - with permitted wearing.U radu je prikazana analiza toplinskih i mehaničkih naprezanja na disk kočnicama tračničkih vozila, a provedena je primjenom FEM (Finite Element Method) metode. Analizom se tretira jedan ciklus kočenja – kočenje od najveće brzine do zaustavljanja, faza hlađenja, a zatim ubrzanje do najveće brzine te ponovno kočenje do zaustavljanja. Takvim ciklusom kočenja predstavljen je jedan od radnih uvjeta. Pri takvom pristupu glavni granični uvjeti su toplinski tok na površinu kočnice, centrifugalno opterećenje i sila stezanja. Analiziran je jedan tip diskova - s dopuštenim trošenjem

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