Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Composite Materials

Background:

• Conventional machining of intricate composite laminate profiles is challenging and time-consuming.
• Novel fiber intermetallic laminates (FILs) offer advanced properties but require precise manufacturing techniques.
• Abrasive waterjet cutting (AWJC) presents a viable alternative for machining these complex materials.

Purpose of the Study:

• To develop a hybrid intelligent model for optimizing the abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL).
• To investigate the influence of reduced graphene oxide (r-GO) content and AWJC parameters on kerf taper (Kt) and surface roughness (Ra).
• To determine the optimal AWJC parameters for achieving superior surface quality and dimensional accuracy in FILs.

Main Methods:

• Hybrid modeling combining adaptive neuro-fuzzy inference system (ANFIS) optimized by metaheuristic algorithms (particle swarm, moth flame, dragonfly optimization).
• Experimental design varying reduced graphene oxide (r-GO) wt%, traverse speed, waterjet pressure, and stand-off distance.
• Multi-response optimization using the salp swarm optimization (SSO) algorithm to identify optimal cutting parameters.

Main Results:

• Moth flame optimization significantly improved ANFIS prediction accuracy for kerf taper and surface roughness.
• The salp swarm optimization (SSO) algorithm identified optimal parameters: 1.004 wt% r-GO, 600 mm/min traverse speed, 214 MPa waterjet pressure, and 4 mm stand-off distance.
• Confirmation experiments validated the model, showing low average prediction errors of 3.38% for Kt and 3.77% for Ra.

Conclusions:

• The hybrid intelligent modeling approach effectively optimizes AWJC parameters for FILs.
• The developed model demonstrates high prediction capability and can guide manufacturing processes for improved part quality.
• This research contributes to efficient and precise machining of advanced composite materials.