Product designers address the costs of assembly during the design process. Design process can be more efficient if assembly issues could be addressed early in its design process. Doing this requires the ability to assess assemblability among others. Assemblability refers to the ease of assembling the final product from its parts. Assemblability evaluation is applied by product designers for quantitatively estimating the degree of difficulty of the assembly. This helps in identifying areas of improvement, so as to reduce process time and production costs. This work focuses on assessing assemblability in a manual assembly and its importance in the earlier phases of design. Literature contains various methods for assessing assemblability (e.g. Boothroyd-Dewhurst method, the DFA house, Sturges DFA calculator and Sony DFA method). These methods are typically rule-based and their use requires insight and knowledge on the part of the designer. Further, the designer has to interpret and apply them differently in each specific and unique case. Literature also contains methods for ergonomic assessment of manual work and its link with assemblability. Both observation based ergonomic assessment such as RULA, REBA, VIDAR, LUBA and OWAS and instrumentation based ergonomic assessment using electro-goniometer and accelerometer are the techniques reviewed in this thesis for their suitability in assembly assessment. The most recent trend in the area is automation of the evaluation process. This thesis proposes an approach to automated assessment of assemblability using electromagnetic trackers. In spite of advances in industrial automation, manual assembly tasks continue to be an important feature of many industrial operations. The method proposed in the thesis is for the assessment of assemblability of manual assembly that combines both time and postural analysis. The tool used for the static analysis is called Rapid Upper Limb Assessment (RULA); for dynamic analysis a new method of time analysis is proposed that is based on the ratio of time spent in fine and gross motions carried out in an assembly process. The difficulty of assembly of a series of manual assembly tasks are carried out in a laboratory setting. Then by correlating this assessment with the feedback on the difficulty of these assembly task obtained from the subjects who carried out these tasks. The remaining work carried out as a part of this thesis is focussed on automating the process of carrying out the above assessment in an automated manner. Suitability of electromagnetic trackers as a means for automated capture of data necessary for executing the proposed assessment method is studied. Electromagnetic trackers have been used to capture postural data for various limbs of the assembly operators. Data from the limbs are then analysed to identify, to which limb movement signifies which sources of difficulty (i.e. reach, visibility, etc.) in assembly; for example reach difficulty is indicated by torso movement. Finally, the thesis proposes as a part of the future work in possible improvement of the assessment method. Also, its application using a virtual reality (VR) platform assesses in ascertaining ease or difficulties in assembly and many.

The present dissertation develops an invariant framework for 3D gesture comparison studies. 3D gesture comparison without Lagrangian models is challenging not only because of the lack of prediction provided by physics, but also because of a dual geometry representation, spatial dimensionality and non-linearity associated to 3D-kinematics. In 3D spaces, it is difficult to compare curves without an alignment operator since it is likely that discrete curves are not synchronized and do not share a common point in space. One has to assume that each and every single trajectory in the space is unique. The common answer is to assert the similitude between two or more trajectories as estimating an average distance error from the aligned curves, provided that the alignment operator is found. In order to avoid the alignment problem, the method uses differential geometry for position and orientation curves. Differential geometry not only reduces the spatial dimensionality but also achieves view invariance. However, the nonlinear signatures may be unbounded or singular. Yet, it is shown that pattern recognition between intrinsic signatures using correlations is robust for position and orientation alike. A new mapping for orientation sequences is introduced in order to treat quaternion and Euclidean intrinsic signatures alike. The new mapping projects a 4D-hyper-sphere for orientations onto a 3D-Euclidean volume. The projection uses the quaternion invariant distance to map rotation sequences into 3D-Euclidean curves. However, quaternion spaces are sectional discrete spaces. The significance is that continuous rotation functions can be only approximated for small angles. Rotation sequences with large angle variations can only be interpolated in discrete sections. The current dissertation introduces two multi-scale approaches that improve numerical stability and bound the signal energy content of the intrinsic signatures. The first is a multilevel least squares curve fitting method similar to Haar wavelet. The second is a geodesic distance anisotropic kernel filter. The methodology testing is carried out on 3D-gestures for obstetrics training. The study quantitatively assess the process of skill acquisition and transfer of manipulating obstetric forceps gestures. The results show that the multi-scale correlations with intrinsic signatures track and evaluate gesture differences between experts and trainees.

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