Robotic Arm Sensor for Validations in Manufacturing


Available for Licensing
TRL: 6

IP Status

​Steve Simske
Katrina Weinmann

Reference No: 2021-058
Licensing Manager

Aly Hoeher

At a Glance

Researchers at Colorado State University have developed a method to provide a four-fold hybrid validation system for custom manufacturing processes, from inspection to authentication and forensic validation – all using the same robotic-arm supported sensor.


Using a nano-indenter method, the system provides a novel means of simultaneously validating a material used in manufacturing; encoding and decoding intentional identifying information at the surface of the material, which might be the interface between two layers in a joining process during multi-step manufacturing; using the identifying information for an advantageous purpose, like promoting adhesion, binding, or joining during manufacturing; and providing loci for forensic authentication of the specific item.


Robotic arms are increasingly incorporated into manufacturing. In traditional mass production environments, they are used to provide assistance to human workers and for automation. In the increasingly prevalent custom manufacturing and additive manufacturing arenas, they are used to help perform a multitude of tasks required for assembly and finishing.

There are several challenges in additive manufacturing. Currently, material must be validated post-print, there are variations in properties throughout the part, and there can be different surface morphologies. In post-processing there can be a variety of part sizes and shapes as well as the need to join different additively manufactured parts. This all contributes to validation taking up valuable time as it must be done differently for different parts. 

Using robotic arms for a wide range of materials and process validation, authentication, and forensic validation tasks is, therefore, a reasonable means of preventing fraud in an increasingly complex and distributed manufacturing environment, while also saving time doing post-manufacturing validation.


This implementation includes nano-indenter tips that indent the material of the part in a specific pattern under a certain set of conditions based on the material. This allows the apparatus to perform a hardness test while simultaneously marking the part with a specific barcode, and/or increasing the surface area for better adhesion of joined parts.

Implementation of the proposed robotic arm

The robotic arm implementation, as described above, is ideal for custom additive manufacturing parts. There is no need to change the setup for different parts, which saves time. Greater location and orientation flexibility is obtained for complex additive manufactured part geometries. It is also easy to change the process parameters (e.g. surface location and orientation, grid angle and size, and barcode configuration). It would also be possible to mount multiple sensors to the end-effector to perform imaging in the same manufacturing step, which can contribute to the forensic potential of this system, achieved through analysis of high-resolution images of the indents.

  • One implementation can be used for 4 different validations: mass sterilization, hardness testing, material validation, and enhanced joining
    • Increased cyber-physical security by creating 2D barcodes for part identification and copy prevention
    • Less destructive hardness testing than tensile testing and includes surface characterization
    • Can validate 3D print setting by verifying material properties and ensuring sufficient density
    • Ensures proper joining of parts by increasing surface area where identifying indents are made
  • Capable of inspection, authentication, and forensic validation
  • All validation methods are supported using the same robotic-arm supported sensor
  • Use in 3D printing and other manufacturing applications
  • Custom manufacturing validation (forensics of failure-unreadable indents may indicated stress concentrations)
  • Manufacturing quality control (can help identify vulnerabilities in the manufacturing process)
  • Asserting copyright (similar to a watermark that is expensive to remove for fraudulent agents)
  • Binding of dissimilar parts