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Evaluating the Effect of Machine Runtime on Energy Consumption

This research investigates the environmental impacts of machine runtime on energy consumption in manufacturing systems. By examining energy use during startup, idle, runtime operations, and cutting, the study aims to develop a scheduling model that minimizes overall energy consumption. Through empirical data from production runs, we assess various scenarios and their energy implications, striving to optimize resource use and mitigate the adverse effects of rising fuel costs. This research is essential for informing sustainability practices in manufacturing and improving operational efficiencies.

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Evaluating the Effect of Machine Runtime on Energy Consumption

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  1. 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-973347

  2. 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

  3. Product Life Cycle Reuse of Manufacturing By-Products Inputs Manufacturing Process Product Use End of Life Environmentally Benign Materials Remanufacture Component Recovery Materials Recovery

  4. Process Diagram Supply Chain Decisions • Energy • Waste • Pollution • Water • Hazardous materials Production Operational Decisions • Energy • Waste • Pollution • Water • Hazardous materials Sub-cellular Machine Level Decisions

  5. 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

  6. 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.

  7. Preliminary Study

  8. 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.

  9. Machined Part

  10. 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

  11. 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

  12. Worst Case Scenario • Machining energy/year = 8,198,782,876 J/year • Idle energy/hour = 1,472,988 J/hour • Idle energy/shift = 1,472,988 J/hour * 4 hours/shift = 5,891,951 J/shift • Idle energy/day = 5,891,951 J/shift * 2 shifts/day =11,783,901 J/day • Idle energy/year = 11,783,901 J/day * 250 days/year = 2,945,975,351 J/year • Total energy/year = 8,198,782,876 J/year + 2,945,975,351 J/year =11,144,758,227 J/year

  13. Simulation Energy Comparison 26% of Total Energy

  14. 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

  15. Thank you.

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