ArticleMaterials (Basel, Switzerland)2021
Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
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Who cites it
12 citing papers in PubMed.
- AWJ Cutting Process Quality Modeling and Optimization Based on Footprint Angle.Materials (Basel, Switzerland) · 2025Article
- Adaptive Control of Pressure Difference in Abrasive Flow Machining for Inner Hole with High Depth/Diameter Ratio.Materials (Basel, Switzerland) · 2024Article
- Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting.Materials (Basel, Switzerland) · 2023Article
- Modelling the Kerf Angle, Roughness and Waviness of the Surface of Inconel 718 in an Abrasive Water Jet Cutting Process.Materials (Basel, Switzerland) · 2023Article
- The Displacements Study of Birch Veneer Layers from Composition of Plywood during Water Jet Cutting Using the Finite Element Method (FEA).Materials (Basel, Switzerland) · 2023Article
- Prediction of Tool Eccentricity Effects on the Mechanical Properties of Friction Stir Welded AA5754-H24 Aluminum Alloy Using ANN Model.Materials (Basel, Switzerland) · 2023Article
- Analysis of Vibration, Deflection Angle and Surface Roughness in Water-Jet Cutting of AZ91D Magnesium Alloy and Simulation of Selected Surface Roughness Parameters Using ANN.Materials (Basel, Switzerland) · 2023Article
- Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm.Materials (Basel, Switzerland) · 2022Article
- Prediction of Surface Roughness in Gas-Solid Two-Phase Abrasive Flow Machining Based on Multivariate Linear Equation.Micromachines · 2022Article
- Machine Learning-Based Prediction of Specific Energy Consumption for Cut-Off Grinding.Sensors (Basel, Switzerland) · 2022Article
- Prediction of Abrasive Waterjet Machining Parameters of Military-Grade Armor Steel by Semi-Empirical and Regression Models.Materials (Basel, Switzerland) · 2022Article
- Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The study's primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.