Data-driven optimization of frozen sand mold cryogenic cutting process parameters for cutting energy and tool wear reduction

Abstract

Green manufacturing prioritizes quality, efficiency, low energy consumption, and cleanliness in milling technology. Consequently, a data-driven optimization method was proposed for configuring multiple technological parameters in frozen sand molds, to reduce energy consumption and tool wear during processing. Initially, a power composition model for frozen sand mold processing was developed, based on an analysis of the relationship between the energy consumption and various actions during different processing states. Subsequently, using Archard wear theory, a discrete element model for cutting frozen sand molds was established to investigate the wear characteristics of flat-end milling tools influenced by multiple factors. The cutting wear of the frozen sand mold presents as a continuous abrasive wear form at polycrystalline diamond (PCD) cutting edge and the side end of the tool shank. Comprehensive experiments were conducted to develop a Kriging energy consumption model and radial basis function model for tool wear based on varying cutting technological parameters. The accuracy of the developed surrogate model was confirmed using an optimal Latin hypercube experimental design and leave-p-out cross-validation (LPOCV). Analysis of the technological parameters revealed that the milling speed and feed rate per tooth significantly affected both milling power and tool wear. Finally, the surrogate model was integrated with particle swarm optimization and a genetic algorithm to solve for the Pareto frontier and identify the optimal combination of cutting parameters. The optimized parameters of the multi-objective model reduced the milling power by 29.88% and tool wear by 18.18% during the processing of frozen sand molds. The models proposed for the milling power and tool wear in this study are accurate and reliable. By revealing the mapping relationship among the cutting power, tool wear and various cutting parameters, the proposed model can serve as an excellent platform for the energy-saving manufacturing of frozen sand molds.

Keywords

Frozen sand mold / Data-driven / Cutting energy consumption / Tool wear / Multi-objective optimization

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Funding

National Key R&D Program of China(2021YFB3401200)

Jiangsu Provincial Basic Research Program (Natural Science Foundation) Youth Fund(BK20230885)

High-quality Development Project of Ministry of Industry and Information Technology

RIGHTS & PERMISSIONS

Shanghai University and Periodicals Agency of Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature