From: Federal Government(Federal)

Opportunity Type

Bid Notification

Opportunity Identifier


Customer / Agency

Department of Energy


Industrial Partnerships & Commercialization
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Opportunity: Lawrence Livermore National Laboratory (LLNL), operated by the Lawrence Livermore National Security (LLNS), LLC under contract no. DE-AC52-07NA27344 (Contract 44) with the U.S. Department of Energy (DOE), is offering the opportunity to enter into a collaboration to further develop and commercialize its novel Machine Learning Informed Control Systems for Extrusion Printing Processes technology to provide optimum control for additive manufacturing.Background: Extrusion-based printing processes can be subdivided into direct-ink writing (DIW) and fused-deposition modeling (FDM). Both extrusion processes are additive-manufacturing methods that allow for customizable materials to be 3D printed into arbitrarily shaped parts. DIW printing exploits the rheological properties of shearing-thinning fluids that exhibit a yield stress by extruding a material as a liquid, which subsequently solidifies once shear forces are no longer applied. The state-of-the-art embodiment of a DIW system uses a computer-controlled translation stage that moves relative to a nozzle allowing for predetermined paths of the ink to be deposited onto the build platform. FDM printing is a similar method that exploits the melting point of thermoplastics to produce a shear-thinning fluid that can be extruded from a nozzle and subsequently solidified once cooled to the ambient temperature. Although many materials can be developed into inks and filaments that fit the specifications required for extrusion-based printing, the variation in ink preparation and the resulting complex rheological properties make precision fluid control difficult to manage. Frequent starts and stops in fluid flow are often required for optimal tool paths but are difficult to achieve due to varying capacitance or response times for different materials. Over or under extrusion relative to the speed of the translational stage will cause irregular filament sizes and produce varying defects throughout a given print.. Moreover, disturbances such air bubbles and clogs are catastrophic to the printing process as they introduce voids to the part or completely halt the printing process all together. Potenial for these defects can be addressed through in-situ monitoring and feedback/feedforward control. By manipulating the extrusion rate, stage speed and z-height (distance between the nozzle and substrate), the quality of the filament dispensed during the build can be controlled.Previous methods employed to combat these issues involve a conventional proportional-integral-derivative (PID) servo loop for extrusion control. Extrusion actuation is generated by a ball screw servo motor and the feedback loop takes voltage or current as the control input and linear displacement of the ink within the reservoir as the control output. More sophisticated research has been performed by implementing an on-off (or "bang-bang") controller(s) or modeling DIW system dynamics and implementing predictive algorithms (e.g., model predictive control) to control extrusion rate or to abort printing altogether. Although these methods significantly improve the extrusion process, the user is still required to manually determine printing parameters (i.e., stage speed, extrusion rate, and z-height) and monitor for air bubbles or clogs. This requirement for human involvement severely limits the scalability of extrusion-based printing processes. Additionally, the controller is decoupled from the quality of the extruded filaments and is not part of the control loop.Description:  Livermore researchers have developed a method for implementing closed-loop control in extrusion printing processes by means of novel sensing, machine learning, and optimal control algorithms for the optimization of printing parameters and controllability. The system includes a suite of sensors, including cameras, voltage and current meters, scales, etc., that provide in-situ process monitoring and an online optimizer for determining ideal process parameters throughout a given print. Computer vision and machine learning are used in the process to monitor and derive quantitative values describing filament quality, e.g. eccentricity, continuity of a filament, color as it relates to mixing quality, among other possible metrics. These process data are passed through a dynamic model of the system to obtain estimates of essential state variables. An optimal controller uses this feedback to obtain the relationship between control inputs and control outputs to calculate future regulatory moves for overall precision printing.Advantages:  This technology includes optimal printing parameters and filament quality in the control loop to produce higher quality prints. This system can enable real-time monitoring and control that is necessary for long production runs without human intervention. Also, the system will allow rapid development of new printing materials by expediting the process of determining optimal print conditions (i.e., stage speed, extrusion rate, and z-height).Potential Applications:  Uses for the technology include:(1) Automated monitoring and control to inform proper functioning of DIW printing.(2) Simultaneous control of multiple systems for sophisticated methods of DIW printing, such as:a. UV-curingb. Non-continuous toolpathsc. Simultaneous printing of multiple inksd. Optimal extrusion for varying filament qualitye. Advanced manufacturing techniques that implement printing outer molds and infilling for full dense objects. (3) Sensor development for 3D printingb. Interferometry measurements of ink displacementc. Filament composition monitoringd. Filament quality control Once MPC is designed and developed for extrusion-based printing, the same strategy can be applied to other methods: • Dynamic mixing in DIW printing• FDM• Powder bed• Digital light projection• Stereolithography• Ink-jet 3D printing Development Status:  LLNL has filed for patent protection on this technology.LLNL is seeking industry partners with a demonstrated ability to bring such inventions to the market. Moving critical technology beyond the Laboratory to the commercial world helps our licensees gain a competitive edge in the marketplace. All licensing activities are conducted under policies relating to the strict nondisclosure of company proprietary information.  Please visit the IPO website at for more information on working with LLNL and the industrial partnering and technology transfer process.Note:  THIS IS NOT A PROCUREMENT.  Companies interested in commercializing LLNL's MACHINE LEARNING INFORMED CONTROL SYSTEMS for EXTRUSION PRINTING PROCESSES technology to provide optimal control for additive manufacturing should provide a written statement of interest, which includes the following:1.   Company Name and address.2.   The name, address, and telephone number of a point of contact.3.     A description of corporate expertise and facilities relevant to commercializing this technology.Written responses should be directed to:Lawrence Livermore National LaboratoryInnovation and Partnerships OfficeP.O. Box 808, L-795Livermore, CA  94551-0808Attention:  FBO 445-19 Please provide your written statement within thirty (30) days from the date this announcement is published to ensure consideration of your interest in LLNL's MACHINE LEARNING INFORMED CONTROL SYSTEMS for EXTRUSION PRINTING PROCESSES technology to provide optimal control for additive manufacturing.    Contact Information: Connie L Pitcock, Administration, Phone 925-422-1072, Fax 925-423-8988, Email Office Address :7000 East AvenueL-795 Livermore CA 94550 Location: Industrial Partnerships & Commercialization Set Aside: N/A


Start Date

01 Aug, 2019 (12 months ago)

Due Date

03 Sep, 2019 (11 months ago)


Country : United States