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As most existing computer‐aided design systems do not provide part feature information which is essential for process planning, automatic part feature recognition systems serve as an important link between Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). Attempts to provide a structural framework for understanding various issues related to part feature recognition. Reviews previous work in the field of part feature recognition and classifies known feature recognition systems for the sake of updating information and future research. Briefly introduces about 12 systems. Studies 31 systems and lists them in the Appendix based on 60 references. Comments on future research directions.

This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone.

This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis.

The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online.

This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.

The capability of an artificial neural network to determine part pose by processing image data from the silhouette of a back‐lit part has been established in recently reported work. The chief benefit of this new approach is simplicity of training, which is important for flexible automated parts feeders. The objective of the work presented herein is to develop an effective and efficient method for determining the position and orientation of the parts to be used in training the neural network. Candidate methods were used to create sets of training data containing different numbers of images taken of each part in different patterns of position and orientation. For each set of training data, the neural network was trained and its pose recognition performance was empirically evaluated. Based on these empirical results, a method for generating training data is reported that ensures accurate performance of the trained neural network while requiring only a minimum amount of training data.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

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.

The purpose of this paper is to provide a method for the generation of information machines for part mating process diagnosis. Recognition of contact states between parts during robotized part mating represents a significant element of the system for active compliant robot motion. All proposed information machines for contact states recognition will recognize one of the possible contact states even when irregular events in the process occur, and the active motion planner will continue to send commands to robot controller according to the planned trajectory.

The presented framework is based on the general theory of automata and formal languages. Starting from possible regular contact states transitions in part mating, the authors create an automaton for diagnostics, which, besides regular, accepts all irregular (observable and unobservable) process sequences.

Contact states do not appear arbitrarily during regular processes, but in certain context. Theory of automata represents a solid basis for contextual recognition and diagnosis of irregularities in part mating.

The proposed methodology is elaborated and experimentally verified using an example of cylindrical part mating, and stick-slip effect as an observable irregularity. The future work will address the generation of diagnosers for other types of part mating tasks and extension of the set of observable irregularities.

The process diagnosis increases the robustness of active compliant motion system.

Although very important feedback information provider for active motion planner, part mating process monitoring was not frequently addressed in the past. In this paper, the authors propose a methodology for generation of part mating process diagnoser that is based on general automata theory.

Recognition of a tactile image independent of position, size and orientation has been a goal of much recent research. Many tasks (e.g. parts identification) often give rise to situations which demand a more generalized methodology than the derivation of a single forward measurement, such as the computation of part area and perimeter from its run‐length‐coding representation. In this situation, an interpretation procedure generally adopts the techniques and methodology of a pattern recognition approach. To achieve maximum utility and flexibility, the methods used should be sensitive to any image change in size, translation and rotation, and should provide good repeatability. The algorithm used in this article generally meets these conditions. The results show that recognition schemes based on these invariants are position, size and orientation independent, and also flexible enough to learn most sets of parts. Assuming that parts can vary only in location, orientation and size, then certain moments are very convenient for normalization. For instance, the first moments of area give the centroid of a part, which is a natural origin of co‐ordinates for translation invariant measurements. Similarly, the eigenvectors of the matrix of second central moments define the directions of principal axes, which leads to rotation moment invariant measurements.

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.

After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications in design and manufacturing. Genetic input selection approach is introduced to obtain optimal NN topology. Experimental results are given to evaluate the performance of the proposed system.

The purpose of this paper is to report an evaluation of a leading-with-compassion recognition scheme and to present a new framework for compassion derived from the data.

Qualitative semi-structured interviews, a focus group and thematic data analysis. Content analysis of 1,500 nominations of compassionate acts.

The scheme highlighted that compassion towards staff and patients was important. Links to the wider well-being strategies of some of the ten organisations involved were unclear. Awareness of the scheme varied and it was introduced in different ways. Tensions included the extent to which compassion should be expected as part of normal practice and whether recognition was required, association of the scheme with the term leadership, and the risk of portraying compassion as something separate, rather than an integral part of the culture. A novel model of compassion was developed from the analysis of 1,500 nominations.

The number of respondents in the evaluation phase was relatively low. The model of compassion contributes to the developing knowledge base in this area.

The model of compassion can be used to demonstrate what compassion “looks like”, and what is expected of staff to work compassionately.

A unique model of compassion derived directly from descriptions of compassionate acts which identifies the impact of compassion on staff.