V. Rasooli Sharabiani; O. Farhangi; E. Taghinezhad
Abstract
Introduction Nowadays the packaging of a product is considered as a symbol of its quality and has a direct effect on its consumer-satisfaction and sales. The visual inspection method is much slower and more error-prone than that of automated method which is used for mass production. Also, this method ...
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Introduction Nowadays the packaging of a product is considered as a symbol of its quality and has a direct effect on its consumer-satisfaction and sales. The visual inspection method is much slower and more error-prone than that of automated method which is used for mass production. Also, this method has other problems such as high labor cost, fatigue, low accuracy, and inconsistency due to various environmental conditions such as lighting, or lack of concentration as well as lack of standards and skilled worker. Machine vision has different applications in the industry. In the packaging of liquid such as cooking oils and different beverages (mineral water, soft drinks, fruit juices ...) that liquid can leak out. So, inspecting cap defects, seal ring defects and liquid level are urgent. Also, label placement plays an important role in customer satisfaction. Machine vision can be able to detect these defects; therefor its application will be effective and useful. So, the advantages of machine vision are non-destructive, accurate, and consistent. Researchers have been used the machine vision system for different area including inspection of surface and structural flaw inspection; steel strips and pharmaceutical tablets. Also, machine vision was used for online control of grading and separation of different agricultural products, such as kiwi, pomegranate, dates, cucumber, almonds, potatoes, tomato and peach. The aim of this research was to manufacture and application a system based on machine vision for inspection and classification of defects in bottles on production lines (case study: soft drink). Sample quality was included of three defects: cap defaults, liquid level and label placement. Materials and Methods 300cc Coca Cola bottles were used as samples for this research. The research was performed to inspect the common defects, including of the cap defaults, liquid level and label placement. In this research, a bottle classification system was designed and developed which it consists of hardware and a software unit. The hardware includes of a conveyor belt, a power system and a power transmission unit, light source, a digital camera, a mechanical ejector and a computer. In this project Lab view 2011 software was used. In this online system, decision was done based on Boolean logic and the defected bottles were separated from the normal ones. For image acquisition and algorithm design the different steps were followed: Vision acquisition, image processing and programming. Clamp (Rake) function was used for inspection of liquid level. It calculated the maximum distance between the cap and liquid level and its result was compared to the edge strength and threshold level. Inspection of cap defaults and label placement was performed using pattern matching and edge detection algorithm, respectively. The appropriate time of ejector must be calculated to take defective samples out of the production line. Results and Discussion Research results were reported at four parts including of cap defects, liquid level and label placement inspection furthermore the combination of all three groups. To find of inspection accuracy, it was repeated 100 times for each default. Accuracy of inspection of the cap, label placement and liquid level were earned as 95, 90 and 100%, respectively. The average accuracy of system was 95.6%. With regard to the conveyor belt’s speed (20cm s-1) and the distance (10cm) between the bottles, the required time to inspect each bottle was 500ms. So, program’s performance was acceptable according to process time of 150-250ms. Finally, the operational capacity of the system was 7200 bottles per hour. These findings were similar to results reported by other researchers. They reported the accuracy of 97 and 90.61% for beer bottles inspection and tomatoes separating, respectively. Conclusion Average of total accuracy for this system was obtained as 95.6%. It separately was 100, 95 and 92% for inspection of liquid level, cap, and label placement, respectively. The highest and lowest accuracy were for inspection of liquid level and label placement. So, performance of the algorithm was suitable for use on production lines. Also, it will be applicable to liquid packaging in the food industry, chemical industry and so on.
