Measurement method of three-dimensional profiles of small lens with gratings projection and a flexible compensation system

2013

Assembly Automation

Purpose -Product quality inspection is of importance in manufacturing industries to ensure that low quality or unqualified products are not delivered to the consumer. Human inspection has many limitations such as low accuracy or speed due to factors such as tiredness and boredom. Traditional 2D vision inspection also has limitations of product shape complexity or flexibility. Thus, automated 3D vision inspection is anticipated to meet the requirements of higher applicability. This paper seeks to address these issues. Design/methodology/approach -In many product quality inspection problems, geometrical parameters of the industrial parts are commonly used as the basis of quality inspection. Machine vision is widely applied to acquire such kind of parameters. Comparing to traditional 2D vision, 3D vision can acquire 3D coordinates of the object directly, so that the inspection can be accomplished which is difficult to do with 2D vision. As an active vision technique, structure light system (SLS) is applied to acquire the 3D coordinate information of inspected object in this paper. On the basis of point cloud and regression analysis, features relative to quality are defined and extracted as the attributes for the product classification. Three data mining techniques are applied to accomplish the classification in this paper, which include decision trees, artificial neural networks and support vector machine. Findings -A new intelligent automated 3D vision quality inspection for assembly lines has been developed, which comprises structure light system (SLS) and data mining approaches such as decision tree, artificial neutral networks and support vector machine. Originality/value -The combination of structure light system (SLS) and data mining approaches makes the automated quality inspection available. The proposed system is easy to be implemented and flexible for different types of products.

Section: Introductionmentioning

confidence: 99%

3D vision based quality inspection with computational intelligence

2013

Assembly Automation

“…The 3D measurement information of the product is retrieved in the form of the point cloud on the basis of the images. With the generated 3D point cloud, automated quality inspection can be performed with less human interference, where data mining approach is commonly used [5,6]. However, although the quality information is able to be decided on the basis of the SLS and data mining approaches, it will not become more valuable until it is used to improve the production process and also achieve the real-time data access and the quality traceability.…”

Section: Introductionmentioning

confidence: 99%

Product Quality Inspection Combining with Structure Light System, Data Mining and RFID Technology

IFIP Advances in Information and Communication Technology

2013

3D vision inspection has been rapidly developed and increasingly applied in product quality inspection. Structure Light System (SLS) is one of these technologies with good performance and lower cost. To achieve the automated quality inspection, a decision support system is required to provide with the SLS together to automatically perform the quality check. However, for extracting quality information, the quality inspection system has to be combined together with a data logging system, so that the quality information of the product is available for production query and quality checking. In this paper, Radio Frequency Identification (RFID) technique is used to accomplish the production tracing and tracking together with the quality inspection system. By assigning a RFID tag to each inspected part, it is possible to identify the type of the product and write the quality inspection result decided by the data mining classifier for the real-time quality query. The proposed approach will be an alternative for SMEs considering the fast product type update with respect to the fast changing market.

“…Traditional 2D vision is not applicable to inspect the object with free surface such as the turbine blade. 3D vision provides an alternative for these problems with more flexibility while little cost added, including various technologies [42], which is a powerful tool in industrial quality inspection [43], welding [44], lens quality inspection [45], thin-walled components inspection [46] and suitable for sorts of working conditions [47][48][49].…”

Section: Pilot Study: 3d Iqismentioning

confidence: 99%

Towards zero-defect manufacturing (ZDM)—a data mining approach

2013

Adv. Manuf.

119

The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero-defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.