CARBURIZING NEWS

Practical Approach to Determining Effective Case Depth of Gas Carburizing

BOTW-50w  Source:  Gear Technology

“Effective case depth is an important factor and goal in gas carburizing, involving complicated procedures in the furnace and requiring precise control of many thermal parameters. Based upon diffusion theory and years of carburizing experience, this paper calculates the effective case depth governed by carburizing temperature, time, carbon content of steel, and carbon potential of atmosphere. In light of this analysis, carburizing factors at various temperatures and carbon potentials for steels with different carbon content were calculated to determine the necessary carburizing cycle time. This methodology provides simple (without computer simulation) and practical guidance of optimized gas carburizing and has been applied to plant production. It shows that measured, effective case depth of gear parts covering most of the industrial application range (0.020 inch to over 0.250 inch) was in good agreement with the calculation.”

Read More:  Practical Approach to Determining Effective Case Depth of Gas Carburizing by March Li

March Li Metallurgist, Automotive Heat Treating, Practical Approach to Determining Effective Case Depth of Gas CarburizingAuthor March Li -Metallurgist

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Analysis of Heat Treat on Carburized Ring Gear and Multivariate Regression Model Development

BOTW-50w  Source:  Thermal Processing for Gear Solutions

Like most other heat treatments, the carburizing and hardening process is known to introduce dimensional changes and gear distortion [4]. If these size and shape changes can be anticipated and controlled, it is possible to eliminate post–heat treatment machining by designing ring gears that would allow for the heat treat change. Such gear manufacturing would significantly reduce cost and machining-to-assembly time, both of which are critical in a commercial environment and large-scale production.

Read More:  Analysis of Heat Treat Growth on Carburized Ring Gear and Multivariate Regression Model Development

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