This paper describes CYBERBIKES, a real-time simulation of an enterprise that produces bicycles. The system is designed as a virtual environment. It is based on a realistic 3-D model of an industrial enterprise coupled with the representation and control of production processes. Furthermore, the flow of information like production data is visualized by means of clear metaphors. The user is immersed in the virtual environment by using a head mounted display through which he/she can explore the various departments of the industrial enterprise and learn how information technology influences modern production processes. This system is intended to train students of industrial engineering in the workings of a modern industrial enterprise. It will also be used for research in production technology as well as in the practical study of virtual reality technology.

The process of free-form thick-layered object manufacturing (FF-TLOM) has been developed with the aim of supporting physical concept modeling and rapid prototyping Of large sized, morphologically complex, dominantly free-form industrial engineering products. The FF-TLOM process involves the following activities: (i) morphological segmentation of the CAD model, (ii) tool profile generation, (iii) slicing the segments based on higher order shape approximation, and (iv) tool position and tool path calculation, (v) layer manufacturing, and (vi) assembly of the physical model. Due to the number of factors that influence the decisions, the otherwise highly automated computations must be accompanied by an in-process visualization. This paper proposes a ‘virtual prototyping’ for pre-implementation testing of the actual physical concept modeling and/or rapid prototyping process. It explains the operation of the developed calculation software tools and presents the results of the visualization tools. Based on the visualization of the process and the artifact, possible errors of the physical model can be explored and eliminated, furthermore, parameters of the process can be optimized.

Designing for manufacturing encourages designers to tailor products for manufacturing constraints, assembly requirements, and limited resources. The additive manufacturing (AM) process challenges traditional manufacturing constraints by building material layer-by-layer, providing opportunities for increased complexity, mass customization, multifunctional embedding, and multi-material production, which were previously difficult with traditional manufacturing (TM) processes. With its application as an effective prototyping and manufacturing tool, AM is prevailing in the educational and industrial engineering design process. For proper utilization of the potential it offers, AM has created a need for an effective Designing for AM (DfAM) curriculum. This exploratory study examines how current formal education on DfAM considerations influence creative concept generation as compared to designing for TM (DfTM). A design study was conducted in two different classrooms, one with and one without formal training in DfAM. It was found that the ideas generated for AM on average were significantly more elegant than the ideas generated for TM. On the other hand, ideas generated for TM scored higher than AM in feasibility. These results indicate that AM significantly aids in generating aesthetically appealing ideas, but not necessarily in the generation of feasible ideas, compared to TM. We use these findings to provide recommendations for design education.

Human-like motion prediction and simulation is an important task with many applications in fields such as occupational-biomechanics, ergonomics in industrial engineering, study of biomechanical systems, prevention of musculoskeletal disorders, computer-graphics animation of articulated figures, prosthesis and exoskeletons design as well as design and control of humanoid robots, among others. In an effort to get biomechanical insight in many human movements, extensive work has been conducted over the last decades on human-motion prediction of tasks as: walking, running, jumping, standing from a chair, reaching and lifting. This literature review is focused on the STS motion and the LLM. STS is defined as the process of rising from a chair to standing up position without losing stability balance, it is the most ubiquitous and torque-demanding daily labor and it is closely related to other capabilities of the human body. LLM is defined as the activity of raising a load, generally a box, from a low to a higher position while stability is maintained, this task produces a high number of incidences of low-back pain and injuries in many industrial and domestic activities. In order to predict STS and LLM, two methods have been identified: these are the OBMG method and the CBMG method.

Developing reliable navigation strategies is mandatory in the field of Underwater Robotics and in particular for Autonomous Underwater Vehicles (AUVs) to ensure the correct achievement of a mission. Underwater navigation is still nowadays critical, e.g. due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS): an AUV typically proceeds for long time intervals only relying on the measurements of its on-board sensors, without any communication with the outside environment. In this context, the filtering algorithm for the estimation of the AUV state is a key factor for the performance of the system; i.e. the filtering algorithm used to estimate the state of the AUV has to guarantee a satisfactory underwater navigation accuracy. In this paper, the authors present an underwater navigation system which exploits measurements from an Inertial Measurement Unit (IMU), Doppler Velocity Log (DVL) and a Pressure Sensor (PS) for the depth, and relies on either an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) for state estimation. A comparison between the EKF approach, classically adopted in the field of underwater robotics and the UKF is given. These navigation algorithms have been experimentally validated through the data related to some sea tests with the Typhoon class AUVs, designed and assembled by the Department of Industrial Engineering of the Florence University (DIEF) for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS and European ARROWS projects. The comparison results are significant as the two filtering strategies are based on the same process and sensors models. At this initial stage of the research activity, the navigation algorithms have been tested offline. The presented results rely on the experimental navigation data acquired during two different sea missions: in the first one, Typhoon AUV #1 navigated in a Remotely Operated Vehicle (ROV) mode near Livorno, Italy, during the final demo of THESAURUS project (held in August 2013); in the latter Typhoon AUV #2 autonomously navigated near La Spezia in the framework of the NATO CommsNet13 experiment, Italy (held in September 2013). The achieved results demonstrate the effectiveness of both navigation algorithms and the superiority of the UKF without increasing the computational load. The algorithms are both affordable for online on-board AUV implementation and new tests at sea are planned for spring 2015.

Due to expensive experimental testing costs, in most industrial engineering applications, only limited statistical information is available to describe the input uncertainty model. It would be unreliable to use an estimated input uncertainty model, such as distribution types and parameters including the standard deviations for the distributions, that is obtained from insufficient data for the design optimization. Furthermore, when input variables are correlated, we would obtain non-optimum design if we use the assumption of independency for the design optimization. In this paper, two methods for problems with lack of input statistical information — possibility-based design optimization (PBDO) and reliability-based design optimization (RBDO) with confidence level on the input model — are compared using a mathematical example and Abrams roadarm of an M1A1 tank. The comparison study shows that the PBDO could provide an unreliable optimum design when the number of samples is very small and that it provides an optimum design that is too conservative when the number of samples is relatively large. Furthermore, the optimum design does not converge to the optimum design obtained using the true input distribution as the number of samples increases. On the other hand, the RBDO with confidence level on the input model provides a conservative and reliable optimum design in a stable manner, and the optimum design converges to the optimum design obtained using the true input distribution as the number of samples increases.