Energy analysis of manufacturing equipment in a production setting

• Main Author: Corcoran, Samantha L.
• Other Authors: Twomey, Janet M.
• Format: Others
• Language: en_US
• Published: Wichita State University 2011
• Subjects: Electronic dissertations
• Online Access: http://hdl.handle.net/10057/3475

Manufacturers are increasingly looking at ways to reduce operating costs through energy savings. While research has been done to identify energy usage throughout a facility--such as lighting, computers, heating and cooling--very little research has been done on reducing the energy consumption of manufacturing equipment. Sample literature review shows the bulk of research on equipment is for tip energy, the energy when a tool makes contact with a work piece. This excludes the energy of all the machine’s background processes: motors, pumps, fans, etc. Several models have been created to predict the energy usage of a machine including both the tip energy and the energy of the background processes; however, these models are experimental laboratory studies. The purpose of this thesis is to collect and analyze real-time data of manufacturing equipment in a production setting. Real-time data is important to understand energy consumption at the machine level and the product level. This thesis reports on a method to collect and analyze real-time manufacturing equipment energy data for a simple part. It also reports on the use of that data to validate the uplci method to estimate the energy consumed for a part using three uplci’s: turret punch uplci, brake forming uplci and drilling uplci. A data logger recorder was connected to each machine to record the energy usage as it produced parts. This data was then broken down into energy modes and analyzed. The results for each machine showed how the machine utilized energy as it produced parts. The real-time data did not validate the uplci calculations; however, the method to collect the real-time data was proven and shown to be easily repeatable by manufacturers. This research provides a solid method for manufacturers to use to identify areas to reduce their energy costs through improved production scheduling and CNC programming. === Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering.