Cutting is an imperative operation in the food‐manufacturing factory, separating food into a predefined geometry. A broad range of solid foods, with various components, textures, and structures, pose enormous challenges to conventional cutting strategies. Additionally, the cutting performance is significantly impacted by the processing parameters, wherein trial‐and‐error or empirical methods are often used to select the parameters in source‐wasting and time‐consuming ways. Hence, there is a need to accelerate the development of advanced cutting techniques and novel modeling approaches in the food‐manufacturing industry. Recently, advanced cutting techniques (ultrasonic vibration‐assisted [UVA], laser, and waterjet cutting) are seen to be superior in processing foods of various textures, with the advantages of high cutting quality, low contamination, and easy operation. Compared with conventional cutting, advanced cutting techniques can dramatically reduce cutting force and energy consumption, resulting in high efficiency, energy‐and‐source saving, and low carbon footprint. Additionally, the finite element (FE) model does simulate the cutting process well, and artificial intelligence (AI) technology is competent to optimize the cutting parameters. This review is perhaps the first one focusing on the advanced cutting techniques applied in the food industry, serving as a summary of the cutting mechanisms, critical influence factors, and applications of conventional and advanced cutting techniques including UVA, laser, and waterjet cutting. In addition, the modeling approaches with respect to FE and AI models are emphasized. Finally, the challenges and future perspectives of advanced cutting techniques combined with modeling approaches are highlighted, and those approaches are promising in the future intelligent food‐manufacturing industry.
The review clearly demonstrates that advanced cutting techniques as having advantages such as high efficiency, energy‐and‐source saving, and low damages, thus exhibiting great potential in processing food of various textures with high cutting quality, low contamination, and easy operation. Additionally, the FE model does simulate the cutting process well and AI is competent in optimizing the cutting parameters, which possesses great potential in providing comprehensive cutting information and selecting the optimal combination of cutting parameters.