Improving Data Collection for Increased Manufacturing Efficiency

Murad Hamirani* and Thorsten Wuest, Smart Manufacturing Lab at Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV 26506

Field (Broad Category): Engineering (Physical Sciences & Engineering) 

Student’s Major: Computer Science 

Data collection is an important step in creating machine learning models. Machine learning is rooted in statistics and mathematics, so the need for data is paramount. Just how machine learning can improve efficiency in any manufacturing process, having a quick method of data collection can also lead to higher efficiency. My research involves the use of a Bluetooth sensor to transfer the data gathered from a manufacturing process machine to a computer. I have been working with a Dialog semiconductor sensor that has embedded Bluetooth technology. The steps involved include determining the feasibility of using the Dialog sensor and writing a program that connects to an Android tablet and relays the data. We expect our results to confirm that the data from the manufacturing process is successfully transmitted. An efficient transmission time would be around 3 to 5 seconds between attaining the data and transferring it to the tablet. Future work includes relaying the data to another program that can process and format it for easier use. 

Funding: J. Wayne and Kathy Richards Faculty Fellows 

Program/mechanism supporting research/creative efforts: WVU's Research Apprenticeship Program (RAP) & accompanying HONR 297-level course