Energy optimisation for the combination of turning and drilling features using differential evolution

Area of Science:

• Manufacturing Engineering
• Sustainable Manufacturing
• Operations Research

Background:

• Previous research optimized individual machining features for energy savings.
• Separate optimization of machining features may increase overall energy consumption due to interdependencies.
• A holistic approach is needed to balance energy reduction across multiple machining operations.

Purpose of the Study:

• To propose an integrated optimization method for minimizing machine tool energy consumption (EMT-TD) for combined turning and drilling features.
• To investigate the trade-offs in energy consumption reduction when optimizing multiple machining features simultaneously.
• To develop a novel optimization problem formulation for integrated dimensional and cutting parameters.

Main Methods:

• Formulation of the integrated dimensional and cutting parameter optimization problem for minimizing energy consumption in turning and drilling (EMT-TD).
• Application of the differential evolution (DE) algorithm to solve the EMT-TD minimization problem.
• Validation through case studies on typical turning and drilling operations.

Main Results:

• The differential evolution algorithm efficiently found optimal solutions within 1 second of computation time.
• Achieved significant savings in total energy consumption for machine tools (EMT-TD): 5.41%, 10.85%, and 7.19% across three cases.
• Resulted in substantial reductions in machining time: 2.23%, 5.90%, and 2.73% for the respective cases.

Conclusions:

• Integrated optimization of dimensional and cutting parameters is crucial for maximizing energy efficiency in multi-feature machining.
• The proposed EMT-TD optimization framework effectively reduces both energy consumption and machining time.
• Differential evolution is a viable and efficient algorithm for solving complex integrated machining optimization problems.