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The recent introduction of low‐cost vision sensors has greatly increased the range of applications for vision. Within the arena of automated assembly there are a number of tasks that vision is suited to and these are outlined. Also the idea of distributing vision throughout the assembly process together with networking via Ethernet is examined.

The purpose of this paper is to review the progress of machine vision as it applies to automated assembly applications.

A series of technological developments is described: 3D vision, smart cameras, near infrared (NIR) imaging and LED illumination. Associated with each are relevant assembly applications.

Advances in multi‐core processors are facilitating the development of 3D image processing algorithms for robot guidance and product inspection, which in turn enable the automation of skilful and labour‐intensive tasks. Machine vision products are becoming more capable, yet simpler to use. NIR imaging is useful for inspecting semiconductors and bottle filling. Advances in LED lighting address difficult inspection tasks at the macro and microscopic levels.

The paper recognises the emergence of 3D machine vision as a new tool in assembly automation. Updates engineers on other relevant machine vision advances.

Marking and inspecting are key steps in hull structure construction. However, currently most marking and inspecting operations are still carried out manually, which leads to low assembly efficiency and quality. This paper aims to solve these problems through the application of digital technology: the optical projection and machine vision.

First, the assembly process model of hull construction is established in 3D design environment. Second, the process information is presented to workers in a virtual form through optical projector, which provides accurate guidance for the manual operation. On this basis, the workers can complete welding and assembly operations readily. Finally, the machine vision method is used to check the assembly results, which can decrease the subjective errors.

A rapid and accurate assembly positioning for hull structure construction is realized based on optical projection, which can avoid the pollution caused by the marking machine and the error caused by human.

This paper combines the advantages of optical projection and machine vision to the field of shipbuilding. The shortcomings of the traditional marking and inspection methods is effectively solved, which may provide a new way for enhancing the assembly efficiency and quality.

The study aims to evaluate the capability of a machine vision camera and software to recognize fasteners for the purpose of assembly verification. This will enable the current assembly verification system to associate torque verfication with a specific fastener.

A small camera is installed at the head of a tool near the socket. The camera is used to capture images surrounding the fastener, and feeding them into machine vision recognition software. By recognizing unique features around the fastener, the fastener can be uniquely identified and therefore verified to be assembled. Additional filtering and multiple frame recognition will improve the reliability of the recognition.

The machine vision technology is found to be adequately reliable in identifying fasteners after tuning key threshold parameters and requiring multiple positively recognized frames. The time to verify can be kept around a fraction of a second to prevent impacting assembly speed.

This experiment was run under simulated assembly line lighting conditions. It also does not explore industrial remote head industrial camera hardware.

By using a remote-mounted camera in combination with electric tools, a reliable assembly verification system can be used to eliminate torque check processes of critical fasteners, thereby reducing the cost of assembly.

Currently, assembly verification is done only using the torque values. In automated assembly line, each process might involve fastening multiple fasteners. Using this system, a new level of assembly verification is achieved by recording the assembled fastener and its associated torque.

The initial excitement over increased solder joint densities, higher manufacturing throughput, and superior electrical performance brought forth by surface mount technology (SMT) has been replaced by frustrations over lower yields and the inherent difficulties of inspecting hidden solder joints. In the plated through hole (PTH) process, rework and inspection tasks were not only relatively easier tasks, but also less costly. The high cost of inspecting and reworking SMT assemblies dictates a rethinking of the assembly process. Increasing first time yields becomes the key to reducing SMT inspection and rework costs. In a high volume facility, a 100% visual inspection process is not feasible because of the high cost of inspection and rework. However, if a company intends to remain competitive, inspection and rework must be reduced without a sacrifice to final product quality. Realising that it is not possible to ‘inspect’ quality into a product, improved yield must result from a controlled process environment. By maintaining a controlled environment, one will be provided with lower inspection costs, lower rework costs, lower scrap and, in the final analysis, improved product quality. At the heart of any process control environment should be a real‐time process control system designed specifically to accommodate SMT process defects. Process monitoring is accomplished by locating and identifying SMT process flaws. These flaws will then be reported to a host system for statistical analysis. These are statistical data used to make timely adjustments to the various stages of the assembly process in a real‐time manner. Being able to monitor the production process objectively in real time, and detect hidden flaws accurately, are the keys to having a successful process inspection system. Automated X‐ray Inspection is gaining acceptance as a viable process monitoring tool, capable of detecting and reporting SMT process flaws, including those hidden flaws not reported with typical visual inspection systems. The purpose of this paper is to show how an Automated X‐ray Inspection system can be integrated into the SMT production process as a cost‐effective method for improving SMT yield.

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.

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.

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.

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.

Reports on the exhibits and exhibitors at Manufacturing Week, which took place at the NEC, 10‐12 November 1998. It reveals that machine vision is the major attraction at the show with over a quarter of the exhibitors exhibiting activity in this area. A number of the companies who offer turnkey systems are highlighted; some include vision as an element in their systems while others are totally dedicated to application of the technology. The emphasis in the article is on applications with the predominance being for inspection. Brief case studies are given of inspecting automotive switches, a contact lens line, robot grinding of propellers and a seat belt retractor assembly system. Some new products are also described including a “control free” junction for Bosch twin belt pallet conveyors and compact fast camera with integral image acquisition and processing electronics.

This paper aims to provide a background to the use of robots by the automotive industry and describe a number of applications which illustrate the capabilities and importance of robotic machine vision technology.

Following an historical background to the use of robots by the automotive industry, this paper discusses a selection of applications which involve the use of robotic machine vision. Brief conclusions are drawn.

This shows that robotic vision technology is playing an important and growing role within the automotive industry and can yield improved product quality and greater productivity.

This paper illustrates how robots equipped with machine vision are contributing to the automotive industry's needs for greater productivity and improved quality.

The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques.

This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined.

This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable.

Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.