MRG Labs recently introduced a new sampling kit that can be used to analyze the condition of grease-lubricated robots. Serving essentially as a "blood test" for robots, the kits utilize the Grease Thief sampling tools outlined in ASTM D7718 and analysis methods listed in the new ASTM D7918 standard for in-service grease testing.
Each kit includes all the tools needed to obtain representative samples from grease-lubricated robots commonly employed in the automotive and manufacturing industries. The methods outlined in the kit enable sampling without robot disassembly or grease purging, thus allowing for periodic grease sampling while the robot is in service. The in-service samples can help operators determine the optimal grease relubrication frequency, identify contaminants or degraded greases, and discover latent wear issues that can lead to failure.
A streamlined version of the grease analysis process is included in the cost of the prepaid sampling kit, which provides two key analysis parameters to screen large numbers of samples. The approximate 1-gram sample size taken in the Grease Thief can be evaluated for wear content (ferrous debris in parts per million) and optical profile (colorimetric spectral response).
Wear content can be trended and evaluated to determine action levels for maintenance and intervention. The colorimetric spectrum can be compared to new, fresh grease to identify potential grease mixing, aging and other contaminants. Together, the two screening tests can monitor a robot fleet to improve maintenance planning and extend component life.
The prepaid screening kit consists of six protected sampling sleeves, which can be used to sample each of the joints in a six-axis robot. The protective shipping tubes come with a unique barcode to be scanned at the time of sampling. With a free smartphone app, the barcode is scanned, and the operator is asked to input the robot's original equipment manufacturer, sample location, operating environment, type of grease in use, and age of the grease. The resulting data is compared to similar components in a cloud-based compilation of generic data for analysis.
For more information, visit mrgcorp.com.
