• 산업식품공학
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• 제21권1호
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• pp.12-21
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• 2017

Foods are becoming more customized and consumers demand food that provides great taste and appearance and that improves health. Food three-dimensional (3D)-printing technology has a great potential to manufacture food products with customized shape, texture, color, flavor, and even nutrition. Food materials for 3D-printing do not rely on the concentration of the manufacturing processes of a product in a single step, but it is associated with the design of food with textures and potentially enhanced nutritional value. The potential uses of food 3D-printing can be forecasted through the three following levels of industry: consumer-produced foods, small-scale food production, and industrial scale food production. Consumer-produced foods would be made in the kitchen, a traditional setting using a nontraditional tool. Small-scale food production would include shops, restaurants, bakeries, and other institutions which produce food for tens to thousands of individuals. Industrial scale production would be for the mass consumer market of hundreds of thousands of consumers. For this reason, food 3D-printing could make an impact on food for personalized nutrition, on-demand food fabrication, food processing technologies, and process design in food industry in the future. This article review on food materials for 3D-printing, rheology control of food, 3D-printing system for food fabrication, 3D-printing based on molecular cuisine, 3D-printing mobile platform for customized food, and future trends in the food market.

• 디지털산업정보학회논문지
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• 제11권3호
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• pp.1-9
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• 2015

In this paper, we implemented the computer vision platform design with MEAN Stack through Raspberry PI 2 model which is an open source platform. we experimented the face recognition, temperature and humidity sensor data logging with WiFi communication under Raspberry Pi 2 model. Especially we directly made the shape of platform with 3D printing design. In this paper, we used the face recognition algorithm with OpenCV software through haarcascade feature extraction machine learning algorithm, and extended the functionality of wireless communication function ability with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the vision platform for identifying the face recognition characteristics of scanning with PI camera with gathering the temperature and humidity sensor data under IoT environment. and made the vision platform with 3D printing technology. Especially we used MongoDB for developing the performance of vision platform because the MongoDB is more akin to working with objects in a programming language than what we know of as a database. Afterwards, we would enhance the performance of vision platform for clouding functionalities.