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Energy efficiency has become crucial in the metal cutting industry. Machining power has therefore become an important metric because it directly affects the energy consumed during the operation of a machine tool. Attempts to predict machining power using machine learning have relied on the training datasets processed from actual machining data to derive the numerical relationship between process parameters and machining power. However, real fields hardly provide training datasets because of the difficulties in data collection; consequently, traditional learning approaches are ineffective in such data-scarce or -absent environment. This paper proposes a transfer learning approach for the predictive modeling of machining power. The proposed approach creates machining power prediction models by transferring the knowledge acquired from prior machining to the target machining context where …