Nowadays virtual simulations are commonly used to solve problems regarding worker well-being or productivity in manufacturing companies. However, when it comes to finding a solution to one of these two objectives, the other usually tends to be secondary. In order to solve this problem, the Ergonomics in Production Platform (EPP) has been developed within research efforts at University of Skövde, which through the use of optimizations is able to obtain solutions where both objectives are taken into account. In turn, in order to address worker well-being, EPP makes use of the digital human modelling (DHM) tool. DHM tools are often used to evaluate simulations focused on studying human-machine interaction. However, as these software evolve and start to be able to reproduce complete motions, before they were only considering frames, new methods are needed to be able to assess risk factors such as time and prevent the occurrence of musculoskeletal disorders (MSDs). In order to assist in the development of EPP optimizations for simulations carried out in DHM tools, the time-based observational method RAMP was implemented, specifically the posture-related criteria of RAMP II. Using the Design and Creation research methodology, a welding gun case study located in China offered by Volvo Cars was used to evaluate the results of the optimizations carried out with EPP. For the evaluation of this case study, a manikin family of 10 members representing key cases of the Asian population was created for this task. Later, this task was recreated in IPS IMMA, where the 10 cases interacted with 3 welding guns to weld different spots on a piece. The analysis of this case study consisted of two distinct phases where the results of RAMP II implemented in EPP could be evaluated. The first phase focused on analyzing initial results of three different trajectories for all members of the family. The second phase consisted of optimizing one of the trajectories analyzed in the previous phase in such a way as to find the best welding angle of the gun to improve the results of the worst case in the first analysis. Three different factors were evaluated in this phase: RAMP II results versus the new angle, RAMP II results versus the results of other methods and the effect of productivity versus worker well-being. The results showed that welding angles of 116º and 80º were able to improve the values of the RAMP II criteria for the most disadvantaged manikin in the welding task. At the same time, it was observed that the higher the percentage of value added time, the higher the risk obtained in the analysis, worsening the worker's well-being.

Future manufacturing will be characterized by the complementarity between humans and automation (human-robot collaboration). This requires new methods and tools for the design and operation of optimized manufacturing workplaces in terms of ergonomics, safety, efficiency, complexity management and work satisfaction. There have been some efforts in the recent years to propose a tool for determining optimal human-automation levels for load balancing. Although the topic is quite new, it shares some similarities with some of the existing research in the area of robotic assembly line balancing. Therefore, it is crucial to review the existing literature and find the most similar models and methods to facilitate the development of new optimization models and algorithms. One of the two contributions that this thesis gives to the research world in the RALBP context is a literature review that involves high quality articles from 1993 to beginning 2018. This literature review includes visual and comprehensive tables—and a label system— where previous research patterns and trends are highlighted. Visualization of data and results obtained by assembly line optimization tools is a very important topic that has rarely been studied. Data visualization would provide a: 1. better comprehension of patterns, trends and qualitative data 2. more constructive information absorption 3. better visualization of relationships and patterns between operations, and 4. better contribution to data manipulation and interaction. The second contribution to research found in this thesis is the use of a human modelling (DHM) tool (called IPS), which is proposed as an assessment to the ergonomic risk that a robotic assembly line may involve. This kind of studies are necessary in order to reduce one of the most frequent reasons of work absence in our today society i.e. musculoskeletal disorders (MSDs). MSDs are often the result of poor work environments and they lead to reduced productivity and quality losses at companies. In view of the above, IPS was used in order to resolve the load handling problem between human and robot, depending on their skills and availability, while fulfilling essential ISO standards i.e. 15066 and 10218:1 and :2. The literature review made it possible to select highly useful documents in developing assumptions for the experiment and contributed to consider real features detected in the industry. Results show that even though IPS is not capable of calculating an entire robotic assembly with human-robot collaboration, it is able to simulate a workstation constituted of one robot and one human. Finite and assembly motions for both human and robot are expected to be implemented in future versions of the software. Finally, the main advantages of using DHM tools in assessing ergonomic risks in RALB can be extracted from the results of this thesis. This advantages include 1. ergonomic evaluation for assembly motions 2. ergonomic evaluation for a full working day (available in future version) and 3. essential ISO standard testing (available in future version).

The focus of the present work is natural human like grasping, for realistic performance simulations in digital human modelling (DHM) environment. The performance simulation for grasping in DHM is typically done through high level commands to the digital human models (DHMs). This calls for a natural and unambiguous scheme to describe a grasp which would implicitly accommodate variations due to the hand form, object form and hand kinematics. A novel relational description scheme is developed towards this purpose. The grasp is modelled as a spatio-temporal relationship between the patches (a closed region on the surface) in the hand and the object. The task dependency of the grasp affects only the choice of the relevant patches. Thus, the present scheme of grasp description enables a human like grasp description possible. Grasping can be simulated either in an interactive command mode as discussed above or in an autonomous mode. In the autonomous mode the patches have to be computed. It is done using a psychological concept, of affordance. This scheme is employed to select a tool from a set of tools. Various types of grasps a user may adopt while grasping a spanner for manipulating a nut is simulated. Grasping of objects by human evolves through distinct naturally occurring phases, such as re-oreintation, transport and preshape. Hand is taken to the object ballpark using a novel concept of virtual object. Before contact establishment hand achieves the shape similar to the global shape of the object, called preshaping. Various hand preshape strategies are simulating using an optimization scheme. Since the focus of the present work is human like grasping, the mechanism which drives the DHMs should also be anatomically pertinent. A methodology is developed wherein the hand-object contact establishment is done based on the anatomical observation of logarithmic spiral pattern during finger flexion. The effect of slip in presence of friction has been studied for 2D and 3D object grasping endeavours and a computational generation of the slip locus is done. The in-grasp slip studies are also done which simulates the finger and object response to slip. It is desirable that the grasping performance simulations be validated for diverse hands that people have. In the absence of an available database of articulated bio-fidelic digital hands, this work develops a semi-automatic methodology for developing subject specific hand models from a single pose 3D laser scan of the subject's hand. The methodology is based on the clinical evidence that creases and joint locations on human hand are strongly correlated. The hand scan is segmented into palm, wrist and phalanges, both manually and computationally. The computational segmentation is based on the crease markings in the hand scan, which is identified by explicitly painting them using a mesh processing software by the user. Joint locations are computed on this segmented hand. A 24 dof kinematic structure is automatically embedded into the hand scan. The joint axes are computed using a novel palm plane normal concept. The computed joint axes are rectified using the convergence, and intra-finger constraints. The methodology is significantly tolerant to the noise in the scan and the pose of the hand. With the proposed methodology articulated, realistic, custom hand models can be generated. Thus, the reported work presents a geometric framework for comprehensive simulation of grasping performance in a DHM environment.