Precision Aluminum Heat Sink Success in AI Computing Modules

1. Application Pain Points

AI computing modules (GPU accelerator cards, AI inference units) continue to increase power density, with single-chip heat dissipation power exceeding 300W. Traditional heat sinks have three core pain points: first, ordinary die-cast aluminum thermal conductivity is only about 96W/(m·K), with excessive thermal resistance causing chip temperature rise beyond limits, frequently triggering computing downclocking and affecting AI computing efficiency; second, AI equipment needs to adapt to -40°C~+120°C wide-temperature operating conditions, traditional heat sink structural stiffness is insufficient, prone to deformation and poor contact under vibration and thermal cycling environments, with thermal resistance rising sharply; third, traditional processing technology heat sinks have large flatness deviations, with chip contact interface thermal resistance exceeding standards, and batch production dimensional consistency is poor, unable to meet the large-scale delivery requirements of AI equipment.

2. Customized Solution

Combining AI equipment heat dissipation characteristics with national standard GB/T 3880.2 processing specifications, Yukun Electronics developed a full-process customized solution:

1. Material Selection and Performance Optimization: Selected 6063 aluminum alloy T5/T6 temper base material with thermal conductivity reaching 200W/(m·K), balancing thermal performance and structural strength; strictly controlled material mechanical properties for AI chip installation requirements, ensuring structural stability of heat sinks under wide-temperature operating conditions.

2. Structural and Process Design: Optimized fin structure parameters, with natural convection scenario fin spacing of 2~5mm, forced air cooling scenario spacing of 1~3mm, thickness of 1.0~1.5mm, maximizing heat dissipation area; adopted precision stretch forming process, ensuring heat sink base plate flatness ≤0.05mm, reducing contact thermal resistance with chips; surface anodizing treatment, radiation heat dissipation coefficient increased to 0.8~0.9, enhancing natural heat dissipation capability.

3. Automated Batch Production: Built precision CNC machining + automated inspection production line, achieving full-process automation of heat sink fin forming, surface grinding, and dimensional inspection, ensuring batch product dimensional deviation ≤±0.02mm, adapting to AI equipment large-scale assembly requirements.

3. Implementation Results

This customized heat sink has been mass-produced for two mainstream AI computing modules, with core results as follows:

1. Significantly Improved Heat Dissipation Performance: AI computing modules equipped with this heat sink showed chip core temperature rise 12~18°C lower than traditional heat dissipation solutions under full load operation, completely solving computing downclocking issues, with AI inference efficiency improved by 22%.

2. Operating Condition Adaptability Achieved: Passed -40°C~+120°C thermal cycling and continuous vibration testing, heat sink structure showed no deformation and no significant increase in contact thermal resistance, adapting to the demanding operating environments of automotive and industrial edge AI equipment.

3. Outstanding Mass Production Benefits: Automated production line achieved heat sink batch production yield rate of 99.2%, production efficiency improved by 45%, single AI equipment heat dissipation cost reduced by 18%; products successfully passed downstream AI equipment manufacturer incoming material audits, achieving stable batch supply, establishing a core foundation for the company to expand AI electronic component supply chain business.