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Defect Recognition of Low-temperature Deposition Food 3D Printing Based on Deep Learning
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Qiang TONG1, 2, Huangping MA1, Junhao YANG1, Ye BI1, Shuailei XIAO1, Xiuping DONG2, 3, Qingguo MENG4, *
Science and Technology of Food Industry | 2026, 47(5) : 339 - 347
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Science and Technology of Food Industry | 2026, 47(5): 339-347
• Analysis and Determination •
Defect Recognition of Low-temperature Deposition Food 3D Printing Based on Deep Learning
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Qiang TONG1, 2, Huangping MA1, Junhao YANG1, Ye BI1, Shuailei XIAO1, Xiuping DONG2, 3, Qingguo MENG4, *
Affiliations
  • 1College of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
  • 2National Key Laboratory of Marine Food Processing and Safety Control, National Marine Food Engineering Technology Research Center, Liaoning Province Seafood Deep Processing Industry Common Technology Innovation Platform, Dalian City Chinese-Style Premade Food Technology Innovation Center, Dalian 116034, China
  • 3Institute of Food Cross-Science, Dalian Polytechnic University, Dalian 116034, China
  • 4Dalian Changfeng Industrial Corporation, Dalian 116092, China
Published: 2026-03-01 doi: 10.13386/j.issn1002-0306.2025040229
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The low-temperature deposition (LDM) food 3D printing process is easily affected by a combination of multiple factors, such as extrusion temperature, speed, and material rheological properties, leading to various issues such as blockage, excessive extrusion, and interference, affecting the printing quality. To achieve efficient identification of defects, this study developed a visual monitoring platform based on industrial cameras by constructing a multi-category nozzle extrusion defect image dataset, addressing the problems of small defect sizes and insignificant inter-class differences. A dual-channel multi-scale feature extraction network that integrated ResNet and Swin Transformer was proposed, and a multi-scale attention fusion module was introduced to improve recognition accuracy. Experimental results showed that the proposed model could effectively identify various types of extrusion defects, with prediction accuracies of 98.20%, 97.89%, 94.90%, and 97.86% for blockage, excess, granularity, and interference, respectively, and 97.63% for standard extrusion. The average detection time for a single image was 0.2 s. The overall accuracy was 97.53%, and the recall rate was 97.62%, which were 4.11 and 4.00 percentage points higher than those of ResNet34, respectively. This demonstrates excellent recognition performance and real-time capabilities, providing reliable data support for food 3D printing quality control.

food 3D printing  /  deep learning  /  process detection  /  low-temperature deposition  /  defect identification
童强, 马黄平, 杨俊豪, 毕野, 肖帅磊, 董秀萍, 孟庆国. 基于深度学习的低温沉积食品3D打印缺陷识别. 食品工业科技, 2026 , 47 (5) : 339 -347 . DOI: 10.13386/j.issn1002-0306.2025040229
Qiang TONG, Huangping MA, Junhao YANG, Ye BI, Shuailei XIAO, Xiuping DONG, Qingguo MENG. Defect Recognition of Low-temperature Deposition Food 3D Printing Based on Deep Learning[J]. Science and Technology of Food Industry, 2026 , 47 (5) : 339 -347 . DOI: 10.13386/j.issn1002-0306.2025040229
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Year 2026 volume 47 Issue 5
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Article Info
doi: 10.13386/j.issn1002-0306.2025040229
  • Receive Date：2025-04-18
  • Online Date：2026-10-24
  • Published：2026-03-01
Article Data
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History
  • Received：2025-04-18
Affiliations
    1College of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
    2National Key Laboratory of Marine Food Processing and Safety Control, National Marine Food Engineering Technology Research Center, Liaoning Province Seafood Deep Processing Industry Common Technology Innovation Platform, Dalian City Chinese-Style Premade Food Technology Innovation Center, Dalian 116034, China
    3Institute of Food Cross-Science, Dalian Polytechnic University, Dalian 116034, China
    4Dalian Changfeng Industrial Corporation, Dalian 116092, China
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