Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

International Journal of Advanced Manufacturing Technology

Volumn 52, Issue 1-4, 2011, Pages 101-114

  Gaitonde, Vinayak Neelakanth ^a   Karnik, S R ^a   Figueira, Luis ^b   Davim, J Paulo ^b  

^a KLE TECHNOLOGICAL UNIVERSITY   (India)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

Artificial neural network; Conventional and wiper ceramic inserts; Hard turning; High chromium AISI D2 cold work tool steel; Machinability

Indexed keywords

AISI D2; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELING; CERAMIC INSERTS; COLD WORK TOOL STEELS; CONVENTIONAL AND WIPER CERAMIC INSERTS; CUTTING CONDITIONS; CUTTING FORCES; CUTTING SPEED; ERROR BACK-PROPAGATION; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; FEED-RATES; FULL FACTORIAL DESIGN; GRINDING OPERATIONS; HARD TURNING; HIGH CHROMIUM AISI D2 COLD WORK TOOL STEEL; HIGH PRECISION; INPUT-OUTPUT PATTERN; MACHINING TIME; PERFORMANCE COMPARISON; PROCESS PARAMETERS; SIMULATION RESULT; TOOL MATERIALS; TOOL WEAR; TRAINING ALGORITHMS; TRAINING AND TESTING; TURNING EXPERIMENTS; WORK PIECES;

BACKPROPAGATION ALGORITHMS; CERAMIC MATERIALS; CERAMIC TOOLS; CHROMIUM; COMPUTER SIMULATION; EQUIPMENT; GRINDING (MACHINING); SURFACE PROPERTIES; SURFACE ROUGHNESS; TOOL STEEL; WEAR OF MATERIALS;

NEURAL NETWORKS;

EID: 79751524432     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-010-2714-3     Document Type: Article

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