Evaluating the Effect of Machine Runtime on Energy Consumption

Rebekah DrakeMark 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

Research ObjectiveIdentify 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

Inputs End of LifeManufacturing

ProcessProduct Use

Reuse of Manufacturing By-Products

Environmentally Benign Materials

Component Recovery

Materials Recovery

Remanufacture

EnergyWastePollution WaterHazardous materials

Sub-cellular Machine Level Decisions

ProductionOperational Decisions

Supply Chain DecisionsEnergyWastePollution WaterHazardous 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

ThesisThe 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.

Preliminary Study

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.

Machined Part

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

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

Simulation Energy Comparison

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En

erg

y (

Bil

lio

n J

ou

les)

Best Case Worst Case Idle

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

Thank you.