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In this paper a Virtual Reality (VR) based interactive system for specifying robotic tasks using virtual tools is described. This environment allows an operator to reach into a live video scene and direct robots to use corresponding real tools in complex scenarios that involve integrating a variety of otherwise autonomous technologies. The attribute‐rich virtual tool concept provides a human‐machine interface that is robust to unanticipated developments and tunable to the specific requirements of a particular task. This interactive specification concept is applied to intermediate manufacturing processes such as robotic based grinding and polishing. Further, in this research, when the operator selects a virtual tool by “clicking” on an icon of the desired tool in a virtual toolbox, a representation of the real‐world tool, laden with associated attributes is displayed. A new flavor of tool is created from the parent class when desired. According to operating constraints, new subclasses, which are offspring of the parent tool class, are derived. A specific instance of a tool can be evoked from any of the derived subclasses. Such attribute laden virtual tools enable easy control of otherwise complicated manufacturing task planning. This paper also explores the use of JAVA applet based interface for using these tools over the Internet. Successful implementation of such a Web‐based system will open the door to the use of robots in many other human intensive manufacturing processes.

This paper aims to develop an assembly behaviour dynamic model of reheat stop valve assembly under run‐time situations and combined (assembly error, friction, fluid dynamics and thermal load behaviour) and to carry out assembly process evaluation and optimisation.

The fluid dynamic behaviour analysis is carried out for the dynamic torque characteristics of reheat stop valve and for the thermal load distribution of the valve shaft‐bush subassembly, which is used for evaluating the thermal deformation of valve shaft by using of finite elements method. The assembly behaviour dynamic model is developed by multibody dynamics theory, which is as the basis of developing virtual prototyping platform for analysing and evaluating the current assembly process.

It is revealed that the deformation (ε) of valve shaft due to the thermal load, and the assembly coaxial error (e) can change the motion clearance remarkably, which lead the dynamic properties and performance of reheat stop valve changed greatly. The current assembly behaviour variable are not optimum and the initial design clearance between valve shaft and bush 4# can be optimised by the developed virtual prototyping platform on the basis of ADAMS® API. The results of evaluation for the assembly behaviour reveal the well dynamic characteristics of reheat stop valve with the optimum assembly behaviour variable. This will be useful for improving the current assembly process of reheat stop valve.

The present assembly behaviour dynamic model based on virtual prototyping for optimum assembly process design uses only single objective optimisation (the most important clearance between valve shaft and bush 4#). For a complete optimum assembly process design has to be carried out with other three clearance variables (the clearance between valve shaft and bush 1#, bush 2# and bush 3#) together.

The present analysis provides some benchmarks for improving the current assembly process. In practice, the assembly coaxial tolerance of valve shaft‐bush subassembly and the initial design clearances must be limited strictly.

This paper provides a methodology for analysis and evaluation of reheat stop valve assembly behaviour with the consideration of combined environmental behaviours. Based on this methodology, it is possible to develop an assembly behaviour dynamic model, and further, to develop a virtual prototyping platform for analysing and evaluating the assembly process which will offer help to designers for improving the reheat stop valve assembly process.

Hot‐cells are shielded structures protecting individuals from radioactive materials. The purpose of this paper is to propose a design approach for a hot‐cell simulator using digital mock‐up (DMU) technology and combining Haptic guided complex robotic manipulation for assembly tasks in a virtual environment.

The principal reason for developing a simulator was to explore the feasibility of hot‐cell structure design and collision‐free assembly process. For this, a simulation design philosophy has been proposed that includes DMU facility offering the ability of analyzing the operations and performing complex robotic manipulations in the virtual hot‐cell environment. Furthermore, enhanced Haptic mapping for tele‐manipulation is proposed for training and guidance purposes.

From the analysis and task scenarios performed in virtual simulator, the optimal positions of the manipulators and need of (bridge transport dual arm servo‐manipulators) type were identified. Operation tasks were performed remotely using virtual hot‐cell technology by simulating the scenarios in the DMU reducing the overall operation cost and user training. The graphic simulator substantially reduced the cost of the process and maintenance procedure as well as the process equipment by providing a pre‐analysis of whole scenario for real manipulation.

This research tries to contribute to the virtual hot‐cell design philosophy. Tele‐operated complex robotic operations in DMU technology are performed in virtual hot‐cell. The simulator provides improved Haptic guidance with force and torque feedback enhancing the realism of virtual environment.

This study aims to identify 3D-printed medical model (3DPMM) supply chain barriers that affect the supply chain of 3DPMM in the Indian context and investigate the interdependencies between the barriers to establish hierarchical relations between them to improve the supply chain.

The methodology used interpretive structural modeling (ISM) and a decision-making trial and evaluation laboratory (DEMATEL) to identify the hierarchical and contextual relations among the barriers to the 3DPMM supply chain.

A total of 15 3DPMM supply chain barriers were identified in this study. The analysis identified limited materials options, slow production speed, manual post-processing, high-skilled data analyst, design and customization expert and simulation accuracy as the significant driving barriers for the medical models supply chain for hospitals. In addition, the authors identified linkage and dependent barriers. The present study findings would help to improve the 3DPMM supply chain.

There were no experts from other nations, so this study might have missed a few 3DPMM supply chain barriers that would have been significant from another nation’s perspective.

ISM would help practitioners minimize 3DPMM supply chain barriers, while DEMATEL allows practitioners to emphasize the causal effects of 3DPMM supply chain barriers.

This study minimizes the 3DPMM supply chain barriers for medical applications through a hybrid ISM and DEMATEL methodology that has not been investigated in the literature.

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.