SICO TESTING FOR ADVANCED MATERIALS
 

What Is SICO Testing?
Strain Induced Crack Opening (SICO) testing is a Gleeble-exclusive thermo-mechanical test method designed to evaluate a material's susceptibility to hot cracking under realistic processing conditions. A specimen is heated to target temperatures within the material's solidification range or hot working regime, then subjected to controlled compressive deformation across varying strain rates — while the test system monitors continuously for crack initiation. The result is a map of the critical temperature, strain, and strain rate combinations at which cracking begins.

Unlike conventional approaches that isolate individual variables, SICO replicates the complex thermo-mechanical stress state a material experiences during industrial forging, forming, and welding — where temperature, strain, and strain rate interact simultaneously to determine whether cracking occurs. This interaction cannot be captured by thermal analysis or static mechanical testing alone. The SICO test was developed specifically for the Gleeble platform because only a system with simultaneous high-rate thermal control and servo-hydraulic mechanical control can reproduce these coupled conditions with the precision required for meaningful crack initiation data.

Because SICO testing is performed on small specimens under fully controlled laboratory conditions, engineers can screen multiple alloy compositions, process parameters, or heat treatment conditions in days — replacing weeks or months of full-scale production trials with quantitative, reproducible laboratory data.

Pain Points We Solve
 

• Hot cracking during forging, forming, or welding with no quantitative understanding of the conditions that trigger it — SICO testing maps the precise temperature-strain-strain rate combinations where crack initiation occurs
• Process development relying on full-scale production trials to define safe operating windows — the Gleeble generates this data from small specimens in a fraction of the time and cost
• Alloy selection based on composition alone without processability data — SICO testing quantifies hot cracking resistance across alloy variations so composition decisions reflect both performance and manufacturability
• Weld procedure qualification without insight into base metal cracking response — SICO characterizes how materials respond to the thermal cycles and restraint conditions of specific joint configurations before procedure trials begin
• Field failures with suspected hot cracking root causes that cannot be confirmed — the Gleeble reproduces the exact thermo-mechanical history of a failure event to validate root cause hypotheses and corrective actions

Comprehensive SICO Property Characterization

This dataset captures material hot cracking behavior across the full range of process-relevant temperature, strain, and strain rate conditions. Hot cracking susceptibility windows define the precise thermo-mechanical combinations that trigger crack initiation, enabling process parameters to be designed around critical zones rather than discovered through production failures. Safe operating windows for forging temperatures, strain rates, and deformation sequences are established directly from SICO data, replacing trial-and-error process development with quantitative boundary conditions.

Alloy composition effects on hot cracking resistance — including the influence of residual elements, microalloying additions, and solidification range on crack susceptibility — are quantified to guide alloy selection and development toward compositions that balance mechanical performance with processability. Prior heat treatment effects on cracking behavior characterize how thermal processing history influences susceptibility, enabling optimization of pre-process treatment sequences. Weld procedure development data, derived from SICO testing under welding-representative thermal cycles and restraint conditions, supports procedure qualification and filler metal selection across joint configurations. Failure analysis correlation datasets, generated by reproducing field failure conditions in the laboratory, confirm root cause hypotheses and validate corrective actions before production implementation.