Rezumat

Procesele de prelucrare trebuie să se ocupe de deformarea sculei de tăiere, a mașinii-unelte și a piesei de prelucrat și a anomaliilor sculei, cum ar fi ruperea sculei și uzura sculei, care este cauzată de forța de așchiere, efectul termic și vibrația chatter. Detectarea și monitorizarea în timpul procesului îmbunătățesc calitatea și eficiența prelucrării în deplasarea către prelucrarea automată și inteligentă. Multe sisteme de monitorizare a procesului de prelucrare au fost dezvoltate în ultimele două decenii. Îmbunătățirea viitoare a proceselor de prelucrare necesită ca sistemele de monitorizare să fie robuste, reconfigurabile, fiabile, inteligente și ieftine. Acest capitol a prezentat tehnicile fundamentale și dezvoltarea recentă a sistemelor de monitorizare a proceselor de prelucrare. Acesta acoperă o mare varietate de măsuranzi și senzori, cum ar fi senzorul de curent/putere al motorului, senzorul de forță/cuplu, senzor de emisie acustică, senzor de vibrație/accelerare, senzor de temperatură și senzor optic/de viziune care au fost utilizați pentru a monitoriza procesul de prelucrare. Sunt explicate tehnici de achiziție de date și concepte importante precum ADC, cuantizare, rata de eșantionare, teorema de eșantionare Nyquist și aliasing. Sunt introduse diferite tehnici de procesare a semnalului, inclusiv analiza domeniului timp, analiza domeniului frecvență, analiza domeniului timp-frecvență și inteligența artificială, împreună cu strategii, abordări și exemple detaliate de monitorizare a procesului de prelucrare. O schemă de monitorizare eficientă se bazează pe interpretarea exactă a ieșirilor senzorului și maparea caracteristicilor semnalului extras cu caracteristicile procesului corespunzător. Datorită complexității procesului de prelucrare, este necesară dezvoltarea în continuare a algoritmilor robuști de procesare a semnalului și a algoritmilor sofisticați de extracție a caracteristicilor pentru a recunoaște condițiile de proces care să poată fi aplicate industriilor de producție cu costuri acceptabile.

Referințe

Abburi NR, Dixit US (2006) A knowledge-based system for the prediction of surface roughness in turning process. Robot Comp Integr Manuf 22(4):363–372

Abellan-Nebot J, Romero Subiro´n F (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257

Achiche S, Balazinski M, Baron L, Jemielniak K (2002) Tool wear monitoring using geneticallygenerated fuzzy knowledge bases. Eng Appl Artif Intel 15(3–4):303–314

Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press, Cambridge/New York

Andreasen JL, De Chiffre L (1998) An automatic system for elaboration of chip breaking diagrams. CIRP Ann Manuf Technol 47(1):35–40

Arrazola PJ, Arriola I, Davies MA, Cooke AL, Dutterer BS (2008) The effect of machinability on thermal fields in orthogonal cutting of AISI 4140 steel. CIRP Ann Manuf Technol 57(1):65–68

Axinte DA, Gindy N (2003) Tool condition monitoring in broaching. Wear 254(3–4):370–382 Axinte DA, Gindy N, Fox K, Unanue I (2004) Process monitoring to assist the workpiece surface quality in machining. Int J Mach Tool Manuf 44(10):1091–1108

Byrne G, Dornfeld D, Inasaki I, Konig W, Teti R (1995) Tool condition monitoring (TCM) – the status of research and industrial application. CIRP Ann Manuf Technol 44(2):541–567

Chen JC, Huang B (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21(5):339–347

Chen XQ, Zeng H, Li HZ (2008) In-process sensing and monitoring for intelligent machining: overview and implementation. Int J Process Syst Eng (IJPSE) 1:1–12

Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection using support vector machine learning in a milling process. Int J Mach Tool Manuf 45(3):241–249

Chungchoo C, Saini D (2002) On-line tool wear estimation in CNC turning operations using fuzzy neural network model. Int J Mach Tool Manuf 42(1):29–40

C¸ olak O, Kurbanog˘lu C, Kayacan MC (2007) Milling surface roughness prediction using evolutionary programming methods. Mater Design 28(2):657–666

Davies MA, Ueda T, M’Saoubi R, Mullany B, Cooke AL (2007) On the measurement of temperature in material removal processes. CIRP Ann Manuf Technol 56(2):581–604

Gandarias E, Dimov S, PhamDT, IvanovA, Popov K, Lizarralde R,Arrazola PJ (2006)Newmethods for tool failure detection in micromilling. Proc IMechE Part B: J Eng Manuf 220(B2):137–144

Griffin J, Chen X (2009) Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming. Int J Adv Manuf Technol 45(11–12):1152–1168

Ibrahim Nur T, Mekdeci C, McLaughlin C (1995) Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN). Int JMach ToolManuf 35(8):1137–1147

Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15 (10):711–721

Jemielniaka K, Tetib R, Kossakowskaa J, Segretob T (2006) Innovative signal processing for cutting force based chip form prediction. In: 2nd Virtual Integration Conference on IPROMS, Ischia, pp 7–12

Kim H-Y, Ahn J-H (2002) Chip disposal state monitoring in drilling using neural network based spindle motor power sensing. Int J Mach Tools Manuf 42(10):1113–1119

Kim J-D, Kim D-S (1997) Development of a combined-type tool dynamometer with a piezo-film accelerometer for an ultra-precision lathe. J Mater Process Technol 71(3):360–366

Kim HY, Ahn JH, Kim SH, Takata S (2002) Real-time drill wear estimation based on spindle motor power. J Mater Process Technol 124(3):267–273

Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4–5):672–693

Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mech Syst Signal Process 23(5):1704–1718

Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Indus 34(1):55–72

Lanzetta M (2001) A new flexible high-resolution vision sensor for tool condition monitoring. J Mater Process Technol 119(1–3):73–82

Latha B, Senthilkumar VS (2010) Modeling and analysis of surface roughness parameters in drilling GFRP composites using fuzzy logic. Mater Manuf Process 25(8):817–827

Le Coz G, Marinescu M, Devillez A, Dudzinski D, Velnom L (2012) Measuring temperature of rotating cutting tools: application to MQL drilling and dry milling of aerospace alloys. Appl Thermal Eng 36:434–441

Li S, Elbestawi MA (1996) Fuzzy clustering for automated tool condition monitoring in machining. Mech Syst Signal Process 10(5):533–550

Li HZ, Zeng H, Chen XQ (2006) An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts. J Mater Process Technol 180(1–3):296–304

Li HZ, Chen XQ, Zeng H, Li XP (2007) Embedded tool condition monitoring for intelligent machining. Int J Comp Appl Technol 28(1):74–81

Li HZ, Albrecht A, Chen XQ (2009) A tool wear observer model for flank wear monitoring in the milling of nickel-based alloys. Int J Mech Manuf Syst 2(5/6):620–637

Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state-ofthe-Art. J Manuf Sci Eng 126(2):297–310

Marinescu I, Axinte D (2009) A time-frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously. Int J Mach Tool Manuf 49(1):53–65

Nowicki B, Jarkiewicz A (1998) In-process surface roughness measurement using fringe field capacitive (FFC) method. Int J Mach Tool Manuf 38(5–6):725–732

Ozel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tool Manuf 45(4–5):467–479

Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tool Manuf 47(14):2140–2152

Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43(1–2):40–51

Susanto V, Chen JC (2003) Fuzzy logic based in-process tool-wear monitoring system in face milling operations. Int J Adv Manuf Technol 21(3):186–192

Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739

Tsai Y-H, Chen JC, Lou S-J (1999) An in-process surface recognition system based on neural networks in end milling cutting operations. Int J Mach Tool Manuf 39(4):583–605

Ueda T, Hosokawa A, Oda K, Yamada K (2001) Temperature on flank face of cutting tool in high speed milling. CIRP Ann Manuf Technol 50(1):37–40

Wang L, Mehrabi MG, Kannatey-Asibu E Jr (2002) Hidden Markov model-based tool wear monitoring in turning. Trans ASME J Manuf Sci Eng 124(3):651–658

Wang X, Wang W, Huang Y, Nguyen N, Krishnakumar K (2008) Design of neural network-based estimator for tool wear modeling in hard turning. J Intell Manuf 19(4):383–396

Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210(5):713–719

Zhang J, Zhang C, Guo S, Zhou L (2012) Research on tool wear detection based on machine vision in end milling process. Product Eng 6(4–5):431–437