Presentation on theme: "Evaluating the Effect of Machine Runtime on Energy Consumption Rebekah Drake Mark Hansen Prashant Lodhia Department of Industrial and Manufacturing Engineering."— Presentation transcript:
Evaluating the Effect of Machine Runtime on Energy Consumption Rebekah Drake Mark Hansen Prashant Lodhia Department of Industrial and Manufacturing Engineering Green Manufacturing Faculty Advisors: Dr. Janet Twomey, Dr. Bayram Yildirim, Dr. Lawrence Whitman, Dr. Jamal Sheikh-Ahmad Supported by NSF CAREER: DMI
Research Objective Identify environmental impacts of the manufacturing system so that we can: Conserve natural resources Offset adverse effects of rising fuel costs Prevent negative impacts of advances in technology Extend useful product life
Product Life Cycle InputsEnd of Life Manufacturing Process Product Use Reuse of Manufacturing By-Products Environmentally Benign Materials Component Recovery Materials Recovery Remanufacture
Energy Waste Pollution Water Hazardous materials Sub-cellular Machine Level Decisions Production Operational Decisions Supply Chain Decisions Energy Waste Pollution Water Hazardous materials Process Diagram
Background Manufacturers objective is to decrease production costs Current agenda focus includes: Optimization of batch size Minimizing cycle time Optimizing production sequence Quality control Status quo models do not consider the environment, specifically energy consumption
Thesis The purpose of this research is to determine the energy consumption of a machine during startup, idle, runtime operations, and cutting in order to minimize the energy use of a production sequence through the development of a scheduling model.
Method Empirical study Production run of a single machined part Track power over time using National Instruments Load Control Evaluate energy consumption of each operation Startup Coolant Feed Movement Cutting Movement Etc.
Simulation Scenario Two 8-hour shifts, producing 250 parts/shift Two 15-minute breaks, one 30-minute lunch Non-bottleneck machine running at approximately 50% capacity Best Case Scenario All parts arrive at the beginning of the shift Parts are machined continuously without idle time Machine is shut off when all parts are complete Worst Case Scenario Parts arrive with a random inter-arrival time Machine runs idle for any time not machining
Best Case Scenario Machining energy/part = 65,590 J/part Machining energy for 250 parts (one shift) = 65,590 J/part * 250 parts/shift = 16,397,566 J/shift Machining energy for 1 day = 16,397,566 J * 2 = 32,795,132 J/day Total energy/year = 32,795,132 J/day * 250 days/year = 8,198,782,876 J/year
Simulation Energy Comparison 26% of Total Energy
Future Work Other factors to consider Consider cycle time, batch size, production sequence, etc. More machines Different parts Different materials Monitors, lighting, air conditioning, etc. Real-world scheduling algorithms Expand study to entire product life cycle