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The practical and proven use of computers in forming technology include: CAD/CAM for die making; transfer of geometric data from the customer's CAD/CAM system to that of the supplier and vice versa; application of artificial intelligence and expert systems for part and process design; simulation of metal flow to eliminate forging defects; prediction and optimization of process variables; and analysis of stresses in dies as well as prevention of premature die failure. Intelligent use of this information can lead to significant gains in product quality and productivity. This paper presents three examples of application of process simulation to forming : rolling, ring rolling and forging.

Springback affects the dimensional accuracy and final shape of stamped parts. Accurate prediction of springback is necessary to design dies that produce the desired part geometry and tolerances. Springback occurs after stamping and ejection of the part because the state of the stresses and strains in the deformed material has changed. To accurately predict springback through finite element analysis, the material model should be well defined for accurate simulation and prediction of stresses and strains after unloading. Despite the development of several advanced material models that comprehensively describe the Bauschinger effect, transient behavior, permanent softening of the blank material, and unloading elastic modulus degradation, the prediction of springback is still not satisfactory for production parts. Dies are often recut several times, after the first tryouts, to compensate for springback and achieve the required part geometry.

Recent studies in sheet metal forming, conducted at universities world wide, emphasize the development of computer aided techniques for process simulation. To be practical and acceptable in a production environment, these codes must be easy to use and allow relatively quick solutions. Often, it is not necessary to make exact predictions but rather to establish the influence of process variables upon part quality, tool stresses, material flow, and material thickness variation. In cooperation with its industrial partners, the ERC for Net Shape Manufacturing of the Ohio State University has applied a number of computer codes for analysis and design of sheet metal forming operations. This paper gives a few selected examples taken from automotive applications and illustrates practical uses of computer simulations to improve productivity and reduce tool development and manufacturing costs.

The current Si/polymeric medical diagnostic sensors that are on the market only feature a one-point calibration system [1]. Such a measurement results in less accurate sensing and more in-factory sensor rejection. The two-point calibration fluidic method introduced here will alleviate some of the shortcomings of such current miniature analytical systems. Our fluidic platform is a disposable, multi-purpose micro analytical laboratory on a compact disc (CD) [2, 3]. This system is based on the centrifugal force, in which fluidic flow can be controlled by the spinning rate of the CD and thus a whole range of fluidic functions including valving, mixing, metering, splitting, and separation can be implemented. Furthermore, optical detection such as absorption and fluorescence can be incorporated into the CD control unit to obtain signals from pre-specified positions on the disc.

With the availability of advanced machine designs and controls, tube hydroforming has become an economic alternative to various stamping processes. The technology is relatively new so that there is no large “knowledge base” to assist the product and process designers. This paper reviews the fundamentals of tube hydroforming technology and discusses how various parameters, such as tube material properties, pre-form geometry, lubrication and process control affect product design and quality. In addition, relations between process variables and achievable part geometry are discussed. Finally, using examples, the status of the current technology and critical issues for future development are reviewed.

The evolution of the flow field inside an IC engine during the intake stroke was studied using 2 different experimental techniques, namely the 2–D Particle Image Velocimetry (2–D PIV) and 3–D Particle Tracking Velocimetry (3–D PTV) techniques. Both studies were conducted using a water analog engine simulation rig. The head tested was a typical pent–roof head geometry with two intake valves and one exhaust valve, and the simulated engine operating point corresponded to an idle condition. For both the 2–D PIV and 3–D PTV experiments, high–speed CCD cameras were used to record the motion of the flow tracer particles. The camera frame rate was adjusted to correspond to 1/4° of crank angle (CA), hence ensuring excellent temporal resolution for velocity calculations. For the 2–D PIV experiment, the flow field was illuminated by an Argon–ion laser with laser–sheet forming optics and this laser sheet was introduced through a transparent piston crown to illuminate the center tumble plane.

Process simulation for product and process design is currently being practiced in industry. However, a number of input variables have a significant effect on the accuracy and reliability of computer predictions. A study was conducted to evaluate the capability of finite element method (FEM) simulations for predicting part characteristics and process conditions in forming complex-shaped, industrial parts. In industrial applications, there are two objectives for conducting FEM simulations of the stamping process: (1) to optimize the product design by analyzing formability at the product design stage and (2) to reduce the tryout time and cost in process design by predicting the deformation process in advance during the die design stage. For each of these objectives, two kinds of FEM simulations are applied.

Over the last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

Roundabout is a unique approach of managing traffic at intersections because it relies on driver’s instincts of safety. Roundabouts are considered safer than other ways of intersection traffic management due to low speed limits, smoother merging, and reduced fatal accidents. Despite their benefits and increasing usage, there is lack of clear understanding of the roundabouts, particularly due to scarcity of data and simulation models and the complexity of the structure. Real-time and offline traffic data recorded at a roundabout provides a basis for 1) identification of the safety issues, 2) understanding unexpected and risky driver behavior, 3) proposing potential mobility solutions, and 4) developing simulation models. The processed data may be used in controlling metered roundabouts, communicating with connected and automated vehicles (CAVs) etc. In this paper an approach to obtain useful traffic information from video feed data at a roundabout is presented.

This study is focused on exploring the possibilities of using camera and route planner images for autonomous driving in an end-to-mid learning fashion. The overall idea is to clone the humans’ driving behavior, in particular, their use of vision for ‘driving’ and map for ‘navigating’. The notion is that we humans use our vision to ‘drive’ and sometimes, we also use a map such as Google/Apple maps to find direction in order to ‘navigate’. We replicated this notion by using end-to-mid imitation learning. In particular, we imitated human driving behavior by using camera and route planner images for predicting the desired waypoints and by using a dedicated control to follow those predicted waypoints. Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.