Highlighted Projects

Onsite Structural Restoration Methods for Aircraft Components

This project automates part restoration, leading to robust and quality repairs for restoration process qualification. The proposed metallic melt deposition (MMD) technology will enable reliable, cost-effective, on-demand repairs of high value metal components that can be incorporated onto an existing machine tool to create a hybrid additive/subtractive system. This will result in an advanced manufacturing technology that replaces conventional processes to allow for rapid on-site part repair. Techniques to characterize the mechanical and microstructural properties achieved through the restoration process have been developed.

Precision Machining of Composite Structures

PINE is currently advancing a laser cutting system for Fiber Reinforced Polymer (FRP) materials that will reduce tool replacement costs and simplify the hole drilling process improving manufacturing efficiency. PINE's proprietary dual laser cutting technology prevents edge melting and delamination by rough cutting composite materials with a high-power laser and finish cutting with a pulsed laser. This laser machining technology is being used on carbon fiber, glass fiber, and composite sandwich structure systems utilized in the CH-53 Heavy Lift Helicopter. We are looking to partner with prime manufacturers.

Innovative Processing Techniques for Additive Manufacture of 7000 Series Aluminum Alloy Components

A novel Multi-Beam Energy (MBE) Directed Energy Deposition (DED) system is used to process aluminum alloys with properties equivalent to the 7XXX series. 7XXX aluminum is widely used throughout the military, and timely, quality repair is needed in many programs. Along with 7XXX, this system can be used to make parts and repairs in other aluminum alloys, copper alloys, and titanium alloys all of which are widely used in structural and other applications. The system utilizes multiple laser energy sources to create a hybrid system that can deposit and remove material as needed.

Additive Manufacturing of Inorganic Transparent Materials for Advanced Optics

The objective of this project is to develop the AM process investigated which is capable of fabricating optical components. The resulting optical components will be evaluated in terms of geometrical, mechanical, microstructural and optical properties to validate the effectiveness of the developed process.

Defect Detection in Metal Additive Manufacturing

In-process inspection offers a path forward to insuring components produced via metal additive manufacturing (AM) such as directed energy deposition (DED). However, a simple anomaly detection ML technique driven from observational data may be insufficient for tracking the performance of a system intended to produce arbitrary geometry. The methodology chosen for a defect monitoring system must be given information about the intent of the machine's current activity (e.g. by being fed data from the CAM system in addition to process monitoring data) in order to sufficiently judge the state of the system. The key innovation proposed for this project is the ability to cleanly automate the entire specimen preparation and analysis process. For the success of machine learning tasks, much attention must be paid to training a joint embedding of the different data types in latent space that adequately represent the combined feature space. As preliminary work, the team developed an AM state prediction deep learning (DL) model that was applied to AM anomaly detection. With PINE's Hybrid Additive Manufacturing Repair (HAMR) we can unify the specimen creation, data collection, and model training in one unified system. By using ML techniques, we can develop a real-time defect detection method that could inform AM engineers to intervene and prevent further defects from happening, reducing waste of time, energy and resources.

Modeling and Process Planning Tool for Hybrid Metal Additive/Subtractive Manufacturing to Control Residual Stress and Reduce Distortion

Hybrid Metal additive manufacturing (AM) is difficult. Residual stresses from directed energy deposition (DED) and laser powder bed fusion (LPBF) processes can be very information-dense. Infill patterns can greatly affect the residual stress distribution, and thus deformation, in the finished component. Subtractive manufacturing (SM) processes, such as milling, upset the balance of long-range residual stresses, as well as introduce new stresses at the cut surface. The majority of the academic work studying residual stresses from metal AM and machining processes is expository. They explain what the residual stresses are but lack an actionable plan to fix the issue. The key innovation proposed here is a framework by which the complex stress states produced by hybrid AM/SM processes can be addressed by two-stage optimization created with a unified set of simulation and computer-aided manufacturing (CAM) tools.