Towards the development of a virtual environment-based training system for mechanical assembly operations

In this paper, we discuss the development of Virtual Training Studio (VTS), a virtual environment-based training system that allows training supervisors to create training instructions and allows trainees to learn assembly operations in a virtual environment. Our system is mainly focused on the cognitive side of training so that trainees can learn to recognize parts, remember assembly sequences, and correctly orient the parts during assembly operations. Our system enables users to train using the following three training modes: (1) Interactive Simulation, (2) 3D Animation, and (3) Video. Implementing these training modes required us to develop several new system features. This paper presents an overview of the VTS system and describes a few main features of the system. We also report user test results that show how people train using our system. The user test results indicate that the system is able to support a wide variety of training preferences and works well to support training for assembly operations.

---

Study of variational inequality and equality formulations for elastostatic frictional contact problems

Summary An overview ofvariational inequality andvariational equality formulations for frictionless contact and frictional contact problems is provided. The aim is to discuss the state-of-the-art in these two formulations and clearly point out their advantages and disadvantages in terms of mathematical completeness and practicality. Various terms required to describe the contact configuration are defined.Unilateral contact law and classical Coulomb’s friction law are given.Elastostatic frictional contact boundary value problem is defined. General two-dimensional frictionless and frictional contact formulations for elastostatic problems are investigated. An example problem of a two bar truss-rigid wall frictionless contact system is formulated as an optimization problem based on the variational inequality approach. The problem is solved in a closed form using the Karush-Kuhn-Tucker (KKT) optimality conditions. The example problem is also formulated as a frictional contact system. It is solved in the closed form using a new two-phase analytical procedure. The procedure avoids use of the incremental/iterative techniques and user defined parameters required in a typical implementation based on the variational equality formulation. Numerical solutions for the frictionless and frictional contact problems are compared with the results obtained by using a general-purpose finite element program ANSYS (that uses variational equality formulation). ANSYS results match reasonably well with the solutions of KKT optimality conditions for the frictionless contact problem and the two-phase procedure for the frictional contact problem. The validity of the analytical formulation for frictional contact problems (with one contacting node) is verified. Thevariational equality formulation for frictionless and frictional, contact problems is also studied in detail. The incremental/iterative Newton-Raphson scheme incorporating the penalty approach is utilized. Studies are conducted to provide insights for the numerical solution techniques. Based on the present study it is concluded that alternate formulations and computational procedures need to be developed for analysis of frictional contact problems.     

AH-64D main transmission accessory drive spur gear installation fault detections

The US Army has launched an aggressive program to implement condition based maintenance on its rotary wing assets. Condition Based Maintenance takes advantage of technology developments in the areas of machinery monitoring, signal processing and fault modeling to reduce the cost of ownership through improved maintenance procedures. Documenting the reduction in maintenance burden, cost savings and increased safety through early detection of helicopter faults is an important step in justifying the program. This paper describes a novel technique used to detect a serious fault in the accessory section of the AH-64D Apache main transmission. The technique determines if the primary or secondary clutch system is driving the aircraft accessories through a tachometer speed ratio. The method has identified three aircraft operating on the secondary system allowing Army maintenance crews to replace the affected transmissions and quickly restore the aircraft to operational status.     

Multidroplet Impact Model for Prediction of Residual Stresses in Water Jet Peening of Materials

In this article, a multidroplet impact model, proposed for predicting residual stresses induced on materials subjected to water jet peening, is presented. This approach considers the impact pressure distribution due to high-velocity droplets impinging on the material surface instead of stationary pressure distribution for prediction of residual stresses on water jet-peened surfaces. It makes use of Reichardt's theory for predicting the velocity distribution of droplets and liquid impact theory for predicting the impact pressure and duration of impact of high-velocity droplets. For predicting residual stresses on the surface and subsurface of material subjected to water jet peening, finite element modeling approach was adopted by using transient elastoplastic finite element analysis by considering an impingement of a set of droplets in succession to one another over a certain time period after which this pressure is released. The effectiveness of the proposed approach was demonstrated bv comparing the predicted residual stresses with those predicted by using the single set of droplets approach proposed by Rajesh et al. [6]. Finally, the practical relevance of the proposed approach was shown by comparing the predicted results with the experimental results obtained by water peening of 6063-T6 aluminium alloy.     

Querying websites using compact skeletons

Several commercial applications, such as online comparison shopping and process automation, require integrating information that is scattered across multiple websites or XML documents. Much research has been devoted to this problem, resulting in several research prototypes and commercial implementations. Such systems rely on wrappers that provide relational or other structured interfaces to websites. Traditionally, wrappers have been constructed by hand on a per-website basis, constraining the scalability of the system. We introduce a website structure inference mechanism called compact skeletons that is a step in the direction of automated wrapper generation. Compact skeletons provide a transformation from websites or other hierarchical data, such as XML documents, to relational tables. We study several classes of compact skeletons and provide polynomial-time algorithms and heuristics for automated construction of compact skeletons from websites. Experimental results show that our heuristics work well in practice. We also argue that compact skeletons are a natural extension of commercially deployed techniques for wrapper construction.     

Evaluation of a multimode fiber optic low coherence interferometer for path length resolved Doppler measurements of diffuse light

The performance of a graded index multimode fiber optic low coherence Mach-Zehnder interferometer with phase modulation is analyzed. Investigated aspects were its ability to measure path length distributions and to perform path length resolved Doppler measurements of multiple scattered photons in a turbid suspension of particles undergoing Brownian and translational motion. The path length resolution of this instrument is compared with a system using single mode fibers for illumination and detection. The optical path lengths are determined from the zero order moment of the phase modulation peak in the power spectrum. The weighted first moment, which is equal to the average Doppler shift, shows a linear response for different mean flow velocities within the physiological range.     

Structural evolution and dopant occupancy preference of yttrium-doped potassium sodium niobate thin films

Sodium potassium niobate (KNN) is the most promising candidate for lead-free piezoelectric material, owing to its high Curie temperature and piezoelectric coefficients among the non-lead piezoelectric. Numerous studies have been carried out to enhance piezoelectric properties of KNN through composition design. This research studied the effects of yttrium concentrations and lattice site occupancy preference in KNN films. For this research, the yttrium-doped KNN thin films (mol% = 0, 0.1, 0.3, 0.5, 0.7 and 0.9) were fabricated using the sol-gel spin coating technique and had revealed the orthorhombic perovskite structures. Based on the replacement of Y³⁺ ions for K⁺/ Na⁺ ions, it was found that the films doped with 0.1 to 0.5 mol% of yttrium had less lattice strain, while films with more than 0.5 mol% of Y³⁺ ions had increased strain due to the tendency of Y³⁺ to occupy the B-site in the perovskite lattice. Furthermore, by analysing the vibrational attributes of octahedron bonding, the dopant occupancy at A-site and B-site lattices could be identified. O-Nb-O bonding was asymmetric and became distorted due to the B-site occupancy of yttrium dopants at high dopant concentrations of >0.5 mol%. Extra conduction electrons had resulted in better resistivity of 2.153× 10⁶ Ω at 0.5 mol%, while higher resistivity was recorded for films prepared with higher concentration of more than 0.5 mol%. The introduction of Y³⁺ improved the grain distribution of KNN structure. Further investigations indicated that yttrium enhances the surface smoothness of the films. However, at high concentrations (0.9 mol%), the yttrium increases the roughness of the surface. Within the studied range of Y³⁺ _, the film with 0.5 mol% Y³⁺ represented a relatively desirable improvement in dielectric loss, tan δ and quality factor, Q_m.     

Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and its ‘Projection Based Learning’ (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, namely the cognitive component and the meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.