Jun Laboratory Laboratory for Advanced Multiscale Manufacturing (LAMM)

Smart Manufacturing

Owing to innovations in digital technologies such as IoT, big data, artificial intelligence, and so on, many machines and devices have become automated, smaller, and smarter. These digital technologies have significant impact on manufacturing technologies and are at the core of 4th industrial revolution. Using these digital technologies can enable full autonomous operation of manufacturing machines, leading to survival and prosperity of small and medium sized manufacturing enterprises. In order to realize this, the first step is to establish the technology for smart monitoring and control of the manufacturing processes and equipment. Smart monitoring and control will enable adaptive automation, where unforeseeable events such as component failure or tool breakage can be predicted and the machine is accordingly serviced autonomously. This requires development of sensors to monitor the machine condition and status, sensor data analytics and machine learning algorithms to predict and determine the machine condition, and middleware technology to enable effective communication between machine and main controller or human. Therefore, the objectives of this project are to use IoT sensors to monitor machines at local manufacturing companies, develop and use data analytics and machine learning algorithms to predict the machine condition, and develop middleware technology to enable standard communication with various machines.

Fiber optic sensor fabrication

There are many challenges inherent to this sensing application, most notably: long transmission lengths; extreme physical and chemical conditions; and the need for many widely-distributed measurements. To address these challenges and constraints, our approach is to develop all-optical sensing arrays that leverage the particular characteristics of the long period grating (LPG). The use of optical fiber leads to reliable sensing because optical fibers are not affected by electrical interference or harsh environment. Demonstration of reliable long-term monitoring systems for CO2 storage sites will be essential to achieving public and regulatory acceptance. Although there are established technologies that are currently used to monitor these sites, gaps in the capabilities of these technologies limit their applicability to carbon management. In this project, we are working on optical sensor approach will directly address these gaps and, thereby, enable to long-term large-scale monitoring of subsurface CO2. Fiber optic sensors as well as fabrication methods for innovative fiber optic sensor designs are currently being researched and developed.

Spray nanoparticle coating using decoupled dual velocity nozzle

There is a lot of potential for nanoparticle-based coatings; however, the challenge still lies in the large-scale deployment of these coatings with high uniformity. These coatings are typically applied using either spin-coating or dip-coating, and although potentially scalable, these methods prove to be a problematic at larger scales. Thus, a cost-effective large area deposition technology with high uniform deposition thickness is currently needed in the industry sectors of glass coating, solar cell, flexible electronics, transparent and conductive films, etc, and the need continues to grow. The spray deposition system developed has the potential to fill the emerging need in these markets, because it allows rapid deposition of multiple nanoparticles over a large area using a continuous stream of droplets at the nanometric thickness. The spray coating system could be potentially used for large area coating, such as windows and solar panels, as well as micro printing of electronic circuits and numerous other applications.

Carbon fiber reinforced plastics (CFRP) composite machining

In CFRP composites machining, delamination is one of several damages modes, which occurs on the top layers of machined edges. The prediction and detection of CFRP delamination during trimming and milling process are important to select optimal fiber cutting direction for better finish surface quality. In this project, simulations of fibre cutting length, fibre cutting angle, and cutting forces in different composites layers are studied to understand and identify potential areas of increased delamination. A flat end milling simulation software is developed in C# to simulate and display the CFRP milling process. Fiber lengths, fiber cutting angle and engaged cutting angle can be displayed in real-time during simulation. Cutting force modeling for CFRP is also investigated to study the effects of fiber cutting length and angle on delamination.

Jun Laboratory

Laboratory for Advanced Multiscale Manufacturing

School of Mechanical Engineering , Room 3061D

Purdue University

585 Purdue Mall, West Lafayette, IN, 47907-2088, USA