Purpose A useful potential application of additive manufacturing (AM) techniques is in automated assembly of existing discrete parts via printing of new material onto two or more parts simultaneously to form joints between them. The purpose of this paper is to explore the concept of extrusion-based AM for automated assembly, examine potential concerns and perform validation to test the feasibility and value of such an assembly method. Design/methodology/approach To validate the theory and address potential concerns, six factorial-designed sets of joined ABS, PETG and PLA samples were manufactured and tensile tested. Each set contained two replications of four samples and was a unique part-joint material combination. To better interpret the results, a new static material characterization was completed on the materials used, as well as joint tests using four mechanical and chemical methods for each material. In total, 69 test articles were examined. Findings The tests showed that the joints were effective and strong, even under the inherently eccentric geometry. While there was some variance between replications, in almost every case, the AM joints were found to be equal or superior to those made by traditional methods. ANOVA showed variance in which factors were significant between sets, but all cases were shown to satisfy the Fisher Assumptions at a significance of a = 0.10. Originality/value This paper develops and validates a new application of extrusion-based AM. When developed further, this application is expected to increase the commercial application range and industrial efficiency of fused deposition modeling and AM in general. The results of this study should provide a link between traditional automated assembly methods and AM. This paper also provides some original AM material characterization data and observations on material behavior under eccentric loading.

In the last few years the exponential rise in the demands or robust surveillance systems have revitalized academia-industries to achieve more efficient vision based computing systems. Vision based computing methods have been found potential for the different surveillance purposes such as Intelligent Transport System (ITS), civil surveillance, defense and other public-private establishment security. However, computational complexities turn-out to be more complicate for ITS under occlusion where multiple cameras could be synchronized together to track certain target vehicle. Classical texture, color based approaches are confined and often leads false positive outcome thus impacting decision making efficiency. Considering this as motivation, in this paper a highly robust and novel Distinctly Trained Multi-Source Convolutional Neural Network (DCNN) has been developed that exhibits pre-training of the real-time traffic videos from multiple cameras to track certain targeted vehicle. Our proposed DCNNvehicle tracking model encompasses multiple shared layers with multiple branchesof the source-specific layers. In other words, DCNN is implemented on each camera or source where it performs feature learning and enables a set of features shared by each camera, which is then learnt to identify Region of Interest (ROI) signifying the “targeted vehicle”. Our proposed DCNNmodel trains each source input iteratively to achieve ROI representations in the shared layers. To perform tracking in a new sequence, DCNNforms a new network by combining the shared layers in the pre-trained DCNN with a new binary classification layer, which is updated online. This process enables online tracking by retrieving the ROI windows arbitrarily sampled near the previous ROI state. It helps achieving real-time vehicle tracking even under occlusion and dynamic background conditions.

Ownership structure or the stakeholder structure of an organization often play significant role in operations decision, monitoring and control. This as a result possesses influences over process and hence performance. On the other hand, the role of stakeholders and respective conflict of interests can also be not ruled out. Under such circumstances, assessing the impact of organizational structure or stakeholder pattern and firm performance becomes inevitable to assess. In addition, the relationship between the investment pattern and respective conflicts of interests is inevitable to be examined. To ensure investment security corporate governance has played vital role that suggests assessing the inter-relationship between the stakeholder pattern and firm performance. With this motivation, in this paper an empirical study has been done to examine the impact of internal shareholding patterns on the associated firm’s performance. In this paper we have performed an empirical study where the aforementioned relationship has been examined for Indian listed NIFTY 50 companies for the duration of the financial year 2011 to 2016. Our empirical results provide evidence that insider shareholding is positively and significantly related to the firm performance as measured by market capitalization; market value by book value and Tobin’s Q.

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