Advancements in Cold Spray Additive Manufacturing: Process, Materials, Optimization, Applications, and Challenges.

Area of Science:

• Materials Science
• Manufacturing Engineering

Background:

• Cold spray additive manufacturing (CSAM) is an advanced, high-speed process for producing high-strength components without high temperatures.
• CSAM yields oxide-free deposits, preserving feedstock properties and substrate integrity, crucial for demanding industries.

Purpose of the Study:

• To explore strategies for enhancing material quality in CSAM.
• To investigate the role of nozzle design, particle size, and process parameters in deposition efficiency and bonding.
• To present machine learning (ML) as a solution for optimizing CSAM processes and overcoming challenges like porosity.

Main Methods:

• Analysis of nozzle design parameters.
• Investigation of particle size distribution effects.
• Fine-tuning of process variables (gas pressure, temperature, spray distance).
• Application of machine learning algorithms for parameter prediction and process control.

Main Results:

• Optimized parameters lead to efficient deposition and superior bonding, enhancing mechanical properties.
• ML enables precise control, reduces trial-and-error, and improves material utilization.
• CSAM demonstrates significant advances in material strength, including tensile strength and corrosion resistance.

Conclusions:

• CSAM is a promising additive manufacturing technology for high-performance, durable components.
• Optimizing key factors and leveraging ML are crucial for advancing CSAM efficiency and application scope.
• The study provides insights for future research and industrial implementation of CSAM in aerospace, defense, and automotive sectors.