In the design process of products and production systems, the activity to systematically evaluate initial alternative design concepts is an important step. The digital human modeling (DHM) tools include several different types of assessment methods in order to evaluate product and production systems. Despite this, and due to the fact that a DHM tool in essence is a computer-supported design and analysis tool, none of the DHM tools provide the functionality to, in a systematic way, use the results generated in the DHM tool to compare design concepts between each other. The aim of this paper is to illustrate how a systematic concept evaluation method is integrated in a DHM tool, and to exemplify how it can be used to systematically assess design alternatives. Pugh´s method was integrated into the IPS software with LUA scripting to systematically compare design concepts. Four workstation layout concepts were generated by four engineers. The four concepts were systematically evaluated with two methods focusing on human well-being and two methods focusing on system performance and cost. The result is very promising. The demonstrator illustrates that it is possible to perform a systematic concept evaluation based on human well-being, overall system performance, and other parameters, where some of the data is automatically provided by the DHM tool and other data manually. The demonstrator can also be used to evaluate only one design concept, where it provides the software user and the decision maker with an objective and visible overview of the success of the design proposal from the perspective of several evaluation methods.
Human safety and sitting comfort in the car depend on a complex set of interactions between the human body, its clothing, and the automotive environment in the meaning of sit elements, belt, solid parts, and air. The design of any functional clothing requires elastic human body models, too, but the detailed FEM models for car crash simulations are unnecessarily accurate, computationally intensive, and not practicable for the clothing development process. For correct simulation of the mechanical interaction, full-scale FEM models of humans with enough suitable accuracy and complexity are required. This work presents the development steps and current state of an algorithm for automatic solid FEM mesh generator for human bodies, based on 3D scan data. The data from the 3D scanner is used for two purposes: (a) for detection of the main sizes of the bones of the human body and (b) the surface mesh is used as a basis for the building of the inner solid layers of the skin, inner soft structures, and the bones. The automatically created solid mesh can then be used for the evaluation of the mechanical interaction between the human body, its clothing, and the environment within commercial or open-source FEM software. The model does not accurately build the interaction within the human body (between bones, muscles, and skin), but allows evaluation of the mechanical interactions and the sitting comfort for different body types and sizes in significantly reduced time.
The ergonomic evaluation of several production tasks is still manual filling of evaluation sheets where the results might differ even with the same dataset. To improve this manual process, motion capture (MOCAP) systems, such as intrusive (inertial sensor-based) and non-intrusive (vision-based) systems, have been intensively tested over the years for precise data collection and analysis. This study combines the strengths of two different MOCAP systems, an AI-based vision system and an inertial sensor-based system, for the human motion posture evaluation on an automotive production use case. For the experiments, a few workstations were selected and used throughout. First, an AI-based vision system with multiple algorithms and cameras was evaluated. Second, inertial sensor-based systems were evaluated. Later, an AI-based vision system and an inertial sensor-based system were combined and evaluated. The results have shown that the AI-based vision system has provided better performance with a minimal number of cameras when combined with a few inertial sensors.
This paper presents an early-design methodology to quantify vision obstruction caused by halo-type cockpit safety equipment introduced into Formula One (F1) racing in 2018. The halo is a curved bar that surrounds the driver's head over the cockpit opening and offers additional protection to drivers. However, the halo's introduction has raised concerns over vision-obstruction-related issues due to its vertical and horizontal bars (pillar-like elements) sitting in front of the cockpit. This study assesses vision obstructions by exploring the driver's forward field of view based on the coverage zone analysis. This research utilizes digital manikins inserted in a digital F1 racecar mockup to assess the effects of halo concept variants on vision obstruction. The preliminary results showed that the vision obstruction was not only affected by the halo geometry and size but also the orientation of the F1 car in different racetrack segments. The methodology discussed in this study is critical for other early-stage product design and development challenges, where designers demand "quick-and-dirty" ergonomics evaluation of vision obstruction before building time-consuming and costly physical mockups.
