Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces.

Hydrogen fuel cells have been used worldwide due to their high energy density and zero emissions. The metallic bipolar plate is the crucial component and has a significant effect on a cell’s efficiency. However, the springback behavior of the metallic bipolar plate will greatly influence its forming accuracy in the micro-scale sheet metal forming process. Therefore, accurate calculation of the springback angle of the micro-scale metallic bipolar plate is urgent but difficult given the state of existing elastoplastic theory. In this paper, a constitutive model that simultaneously considers grain size effect and strain gradient is proposed to analyze micro-scale bending behavior and calculate springback angles. The specialized micro-scale four-point bending tool was designed to better calculate the springback angle and simplify the calculation step. A pure micro-bending experiment on a 316LN stainless steel sheet with a thickness of 0.1 mm was conducted to verify the constitutive model’s accuracy.

Micro-cutting is different from conventional cutting in its mechanics. The workpiece material is not considered to be homogeneous in the micro-cutting process. As a result, it is critical to comprehend how microstructure affects surface integrity, cutting forces, and chip formation. In this paper, we experimented with micro-turning on oxygen-free high-conductivity (OFHC) copper with different microstructures after annealing. Feed rate parameters were smaller than, larger than, and equal to the grain size, respectively. Experimental results show that when the feed rates are equivalent to the grain size, the surface roughness of the machined surface is low and the width of the flake structure on the free surface of chips is minimal, and the explanations for these occurrences are connected to dislocation slip.