2 research outputs found
Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application
The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm.
 
Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application
The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm.