The role of Cyber-Physical systems (CPS) is well recognized in the context of Industry 4.0, which consists of human operators working with machines/robots. The interactions among them can be quite demanding in terms of cognitive resources. Existing systems do not yet consider the psychological aspects of safety in the domain. This lack can lead to hazardous situations, thus compromising the performance of the working system. This work proposes a connective decision-making framework for a flexible CPS, which can quickly respond to dynamic changes and be resilient to emergent hazards. First, Anxiety is defined and categorized for expected/unforeseen situations that a CPS could encounter through historical data using the Ishikawa method. Second, visual cues are used to gather the CPS's current state (such as human pose and object identification). Third, a mathematical model is developed using Mixed-integer programming (MIP) to allocate optimal resources, to tackle high-impact situations generating Anxiety. Finally, the logic is designed for an effective counter-mechanism to mitigate Anxiety. The proposed method was tested on a realistic industrial scenario incorporating a collaborative CPS. The results demonstrated that the proposed method improves the decision-making of a CPS facing a complex scenario, ensures physical safety, and effectively enhances the human-machine team's productivity.

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing. Workspaces are transformed into fully shared spaces for performing tasks during human–robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The interactions are quite demanding in terms of safety, employing both cognitive and physical resources. In this work, we have proposed a four-layered connective framework that can quickly respond to changing physical and psychological safety situations. The first layer performs the desired operation of the cyber-physical production system (CPPS) and gets itself aware of anomalies. The second layer assesses, categorizes and quantifies the developed situations as anxiety factor. The third layer mitigates the arising anxiety through the optimal allocation of resources. The fourth layer makes decisions based on the historical knowledge, the current state of anxiety, and the suggested optimization using logic. The results demonstrated that the proposed method improves decision-making of a CPPS, eventually increasing the productivity. The case study shows that the method is less time-intensive as the maximum time to decide on a situation was found 0.03 s. The survey highlighted the technique leads to enhanced fluency, collaboration, comfort, safety and legibility during collaboration. The proposed system was found 16.85% more accurate than the standard system. The significance test of the results highlighted the p-value < 0.01. The proposed framework can be applied to any industrial scenario where HRC is involved like manufacturing, assembling, packaging, etc.

Social intelligence in robotics appeared quite recently in the field of artificial intelligence (AI) and robotics. It is becoming increasingly evident that social and interaction skills are essentially required in any application where robots need to interact with humans. While the workspaces have transformed into fully shared spaces for performing collaborative tasks, human–robot collaboration (HRC) poses many challenges to the nature of interactions and social behavior among the collaborators. The complex dynamic environment coupled with uncertainty, anomaly, and threats raises questions about the safety and security of the cyber-physical production system (CPPS) in which HRC is involved. Interactions in the social sphere include both physical and psychological safety issues. In this work, we proposed a connective framework that can quickly respond to changing physical and psychological safety state of a CPPS. The first layer executes the production plan and monitors the changes through sensors. The second layer evaluates the situations in terms of their severity as anxiety by applying a quantification method that obtains support from a knowledge base. The third layer responds to the situations through the optimal allocation of resources. The fourth layer decides on the actions to mitigate the anxiety through the allocated resources suggested by the optimization layer. Experimental validation of the proposed method was performed on industrial case studies involving HRC. The results demonstrated that the proposed method improves the decision-making of a CPPS experiencing complex situations, ensures physical safety, and effectively enhances the productivity of the human–robot team by leveraging psychological comfort.

Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions.

Smart factory research is paced up in the current decade due to the development of many enabling technologies and tools available to the developers. This has led to the progress of cyber physical systems in manufacturing, now coined as cyber physical production systems. The ultimate goal of this domain is to integrate underlying technologies and connect physical plants with the virtual factory in real time for improvement in product quality, process improvements, predictive maintenance, mass customization as well as mass production. The involved technology modules include sensor network, machine learning and AI, Internet of things, human machine interface, augmented reality and collaborative robotics. For the physical element in this research, a micro factory scenario is envisaged that consists of a high precision micro/nano positioning stage installed on a tabletop sized conventional machine tool, a collaborative robot for handling of micro parts and running of machine operations, other factory devices and a human worker for supervision tasks. Due to the multi-faceted technologies involved in both the virtual and physical systems, a simultaneous design strategy is followed in both domains. First, a flexure based micro positioning, 3-axis stage device is designed that can be installed on a conventional 3-axis desktop size milling machine. Secondly, a work zone is considered for effective human robot collaboration in the production area. The work zone considered as a social space is designed in a safe and secure way with the help of integrated devices, IoT and AI.

Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). ‘Retinal Bright Lesions’ such as ‘Hard Exudates’ (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection & extraction followed by classification. ‘Speed Up Robust Features’ (SURF) algorithm has been used for features detection & extraction while ‘Feed-Forward Back-propagation’ (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% ‘Sensitivity’ (SE), 97.5% ‘Specificity’ (SP) and 97.7% ‘Accuracy’ (AC) on ‘DIARETDB1’ fundus images.

The worldwide loss in human vision is primarily associated with Diabetic Retinopathy (DR). It occurs due to accelerated levels of blood sugar thereby causing perforation, bulging and leakage of retinal blood vessels (BVs). DR commences with the emergence of small blood spots on the retinal surface known as Microaneurysms (MAs) that are subsequently transformed into heavy blood deposits called Hemorrhages (HGs). This paper proposes an optimized and computationally inexpensive digital image processing (DIP) technique for detection and classification of 'Retinal Red Lesions' (RRLs) i.e. MAs and HGs using green channel of the digital fundus images. The basic essence of the proposed technique revolves around regional spatial transformations detection performed through region based spatial filtering, matching features and neural networks classification. The proposed technique comprises of five main stages i.e. Pre-processing, Regional Spatial Transformations, Optimization, Features extraction and Classification. Speed Up Robust Features (SURF) algorithm has been used for features selection & extraction while Feed-forward Back-propagation Artificial Neural Network (FFBP ANN) has been used for classification. The proposed technique has been successfully applied on commercially available digital fundus image data-set and has yielded 98.4% 'Sensitivity' (SE), 94% 'Specificity' (SP) and 98% 'Accuracy' (AC). The SE, SP and AC have also been compared with other RRLs detection methods and has shown highly promising and encouraging results.

A very important aspect to fabricate MEMS devices, is understanding of the mechanical properties at micro-scale level. The mechanical properties of components not only depend upon the material but also on the micro-structure. Unlike other devices, MEMS components are developed from specialized micro fabrication processes in which they are subjected to cyclic loads thus producing intrinsic stresses. Loading is also produced during actuation of these devices that leads to the development of fatigue. Fatigue phenomenon is of immense importance while deliberating upon reliability of MEMS devices. In this paper, a test specimen has been tested for fatigue testing for assessment of the yield strength. Design rules of MetalMUMPs process have been followed while designing the test device. The tests were performed in FEM analysis software and the results were compared with the previously designed gold structure. The paper is aimed at incisive review of fatigue behavior in the specimen tested with designed MEMS device at specified voltage limit with a view to present analytical results and conclusions.