[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kumar et al., 2023 - Google Patents

Developing digital twin design for enhanced productivity of an automated anodizing industry and process prediction using hybrid deep neural network

Kumar et al., 2023

Document ID
654525500998749198
Author
Kumar V
Manikandan V
Manavaalan G
Elango S
Publication year
Publication venue
Engineering Applications of Artificial Intelligence

External Links

Snippet

Automation is beneficial when implemented in challenging environments requiring less human effort or reducing human effort. Employee safety is a concern to increase productivity, delivery and achieve the required quality. The present research investigates the possibility …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0631—Resource planning, allocation or scheduling for a business operation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning

Similar Documents

Publication Publication Date Title
Kumar et al. Developing digital twin design for enhanced productivity of an automated anodizing industry and process prediction using hybrid deep neural network
JP6438450B2 (en) Machine learning apparatus, robot system, and machine learning method for learning processing sequence of laser processing robot
Kim et al. An investigation into an intelligent system for predicting bead geometry in GMA welding process
JP2017146879A (en) Machine learning device, industrial machinery cell, manufacturing system, and machine learning method for learning work share of multiple industrial machines
Sachan et al. Optimizing basic COCOMO model using simplified genetic algorithm
Lasheras et al. The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process
JP2017199074A (en) Production system for setting determination value of variable related to abnormality of product
Zhao et al. Optimization of geometry quality model for wire and arc additive manufacture based on adaptive multi-objective grey wolf algorithm
Katırcı et al. Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods
Cheng et al. Artificial neural network technology for the data processing of on-line corrosion fatigue crack growth monitoring
Wu et al. Multi-objective parameter optimization of fiber laser welding considering energy consumption and bead geometry
Zare Chavoshi Analysis and predictive modeling of performance parameters in electrochemical drilling process
Wang et al. An intelligent process parameters determination method based on multi-algorithm fusion: a case study in five-axis milling
Zou et al. Prediction and optimization of phosphorus content in electroless plating of Cr12MoV die steel based on PSO-BP model
Singh et al. Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses
Kassaymeh et al. A hybrid salp swarm algorithm with artificial neural network model for predicting the team size required for software testing phase
Cao et al. An adaptive multi-strategy artificial bee colony algorithm for integrated process planning and scheduling
Zhang et al. Intelligent design of robotic welding process parameters using learning-based methods
Han et al. A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV
Abhishek et al. Taguchi approach followed by fuzzy linguistic reasoning for quality‐productivity optimization in machining operation: A case study
Zhang et al. Tool life prediction model based on GA-BP neural network
Malik et al. Smart manufacturing with artificial intelligence and digital twin: A brief review
Liu et al. Digital twin model-driven capacity evaluation and scheduling optimization for ship welding production line
Seid Ahmed et al. Offline digital twin for simulation and assessment of product surface quality
Tuntas et al. An ANFIS model to prediction of corrosion resistance of coated implant materials