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This review aims to analyze the effects of various parameters on abrasive water jet machining of titanium alloys and explore AI applications for optimization.

February 2, 2026Open Access

Abrasive Water Jet Machining (AWJM) of Titanium Alloy—A Review

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This review aims to analyze the effects of various parameters on abrasive water jet machining of titanium alloys and explore AI applications for optimization.
Review of existing literature on AWJM processes.
Analysis of parameters like jet pressure, abrasive flow rate, and nozzle design.
Discussion on the role of artificial neural networks in predicting machining outcomes.
Higher jet pressure and abrasive flow rate increase material removal rate.
Nozzle speed and stand-off distance negatively affect material removal rate.
Artificial neural networks show potential for process modeling with high accuracy.

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Implication

This review uncovers factors affecting machining performance in titanium alloys, suggesting AI can enhance process efficiency.

Cite This Study

Arumugam et al. (2026) studied this question.

synapsesocial.com/papers/6981020cc1c9540dea81336f https://doi.org/10.3390/designs10010013

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Abrasive Water Jet Machining (AWJM) of Titanium Alloy—A Review

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Discussion

Implication

Cite This Study

Abrasive Water Jet Machining (AWJM) of Titanium Alloy—A Review

Key Points

Discussion

Implication

Cite This Study

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