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Conventional methods of physicochemical models require various experts and a high measurement demand to achieve the required model accuracy. With an additional request for faster development time for diagnostic algorithms, this method has reached the limits of economic feasibility. Machine learning algorithms are getting more popular in order to achieve a high model accuracy with an appropriate economical effort and allow to describe complex problems using statistical methods. An important point is the independence from other modelled variables and the exclusive use of sensor data and actuator settings. The concept has already been successfully proven in the field of modelling for exhaust gas aftertreatment sensors. An engine-out nitrogen oxide (NOX) emission sensor model based on polynomial regression was developed, trained, and transferred onto a conventional automotive electronic control unit (ECU) and also proves real-time capability.

Accurate determination of driveshaft torque is desired for robust control, calibration, and diagnosis of propulsion system behaviors. The real-time knowledge of driveshaft torque is also valuable for vehicle motion controls. However, online identification of driveshaft torque is difficult during transient drive conditions because of its coupling with vehicle mass, road grade, and drive resistance as well as the presence of numerous noise factors. A physical torque sensor such as a strain-gauge or magneto-elastic type is considered impractical for volume production vehicles because of packaging requirements, unit cost, and manufacturing investment. This paper describes a novel online method, referred to as Virtual Torque Sensor (VTS), for estimating driveshaft torque based on Machine-Learning (ML) approach. VTS maps a signal from Inertial Measurement Unit (IMU) and vehicle speed to driveshaft torque.

Predicting airbag deployment geometries is an important task for airbag and vehicle designers to meet safety standards based on biomechanical injury risk functions. This prediction is also an extraordinarily complex problem given the number of disciplines and their interactions. State-of-the-art airbag deployment geometry simulations (including time history) entail large, computationally expensive numerical methods such as finite element analysis (FEA) and computational fluid dynamics (CFD), among others. This complexity results in exceptionally large simulation times, making thorough exploration of the design space prohibitive.

Electric Vehicles are subject to effects that lead to more or less rapid degradation of functions. This can cause hazards for the drivers and uninvolved road participants. For this reason, the must be detected and mitigated, to maintain the vehicle function even in critical situations until a safe operating mode can be established. This publication presents an intelligent digital twin, located in the edge and connected with an electric vehicle via 5G. That can improve the operation of electrified vehicles by enabling the online detection of abnormal situations in the electrified powertrain and vehicle dynamics. Its core component is the fault detection system, which is implemented based on a 1-Nearest Neighbor algorithm. It is initially trained on synthetic data, generated in CarMaker for real-world powertrain issues such as demagnetization and open-/short-switch failures, using detailed mathematical models.

Encapsulation of electric powertrains is a booming topic with the electrification of vehicles. It is an efficient way of reducing noise radiated by the machines even in later stages of the design and without altering the electromagnetic performance. However, it is still difficult to define the best possible treatment. The locations, thicknesses and material compositions need to be optimized within given constraints to reach maximum noise reduction while keeping added mass and cost at minimum. In this paper, a methodology to design the encapsulation based on numerical vibro-acoustic simulations is presented. In a first step, the covered areas are identified through post-processing of a finite element acoustic radiation model of the bare powertrain. In a second step, a design of experiment is performed to assess the influence of various cover parameters on the acoustic radiation results.

The use of standardized “bar-code” labels on all new vehicles would help improve the effectiveness of vehicle emission inspection programs. The label could contain information on the vehicle and its emission control system. This information could be used by automated emission testing equipment to select test standards and identify the types of emission control equipment present on the vehicle being tested. More accurate inspections result. The machine-readable labels could also be used by compatible equipment to provide servicing specifications which could result in more effective and efficient repairs.

New manufacturing technologies such as high speed machining (HSM) are being developed to produce high quality aerospace components. While our developing understanding of machining dynamics is enabling precise control of cutting tools to provide for high dimensional accuracy, residual stresses present in aluminum mill products can compromise the ability to machine dimensionally accurate components from these stock materials. The advantages of precise tool control can be lost if the metal being cut moves during machining. And, even a perfectly machined part that distorts when it is released from the machine bed will cause problems upon assembly. Thus, ensuring the quality of the mill product becomes an enabling technology for advanced manufacturing approaches such as HSM.

The Russians have developed a wide range of equipment for the forest industry. This report provides a profile of this general equipment line. The author concludes that the Russian power saws, track skidders and falling machines, trucks, and lower landings offer much in innovative design to American industry. On the other hand, their winchs, spars, and rubber-tired skidders need redesign.

Cost data are presented which provide justification for making systematic studies of all types of machining processes in order to select the most applicable tool materials. Full tool life tests are suggested for obtaining reliable machining data. Specific tool life test data are supplied for the drilling, reaming, and tapping of 4340 steel at 42 Rc, for the face milling of 32510 and 60003 grades of malleable iron, and for the comparative evaluation of T-15, and ultrahard high speed steels, C-8, titanium carbide base, and ceramic cutting tools. Sources of machining information are referenced along with a description of the Air Force Machinability Data Center.

Grey iron (G3000) is a class of iron that is used to manufacture a wide variety of components throughout the world. More than 32 million tons were poured in 1996 (1). The machinability of cast iron at various times is difficult and often cannot readily be linked to the manufacturing or casting processes. This recurring machinability problem coupled with an inability to positively identify its cause has been very costly. A closer look at the microstructural differences in castings revealed that there is a qualitative difference in the coarseness of the pearlite between parts that machine well and those that were difficult to machine in a production setting.

The paper discusses two methods to implement error compensation framework for NC machining. In the first case a novel real-time co-interpolator is utilized that has been developed and demonstrated on a machine tool equipped with an open architecture controller (OAC). The second solution features near-time reprocessing of the NC programs, that is more suitable for existing machining systems. A P-type, iterative learning control (ILC) algorithm is also presented for calculating the error compensation values. The paper concludes with the results of machining tests, showing the effectiveness of the error compensation methodologies.

Magnesium Products Limited has been very successful in converting from machining magnesium die castings dry to machining with oil-in-water emulsions, more commonly called water soluble oils. When machining with water soluble coolants one can expect; 1) excellent fire protection; 2) improved tool life; 3) ease of housekeeping, and 4) improved productivity. Problems previously encountered with water soluble coolants such as splitting out of solution, hydrogen gas generation, and salt deposits on tools and machines have been singled out and addressed, with solutions tested in production environments.

The wide assortment of metals and alloys used for structural purposes in the aerospace industry is classified in terms of machinability. Applications of these materials to important aerospace components, as well as major machining operations on these components, are indicated. The preponderance of metal removal is accomplished by conventional machining methods. Recommended cutting conditions for milling, turning, and drilling of important alloys are presented. Electronic machining methods are applicable to special operations. A summary of these is presented, which includes applications, surface finish, dimensional tolerance capability and practical rates of metal removal.

This paper describes the University of Illinois machining system research program. This program focuses on the development of mechanistic models for machining process simulation and the use of these models for the simultaneous engineering of products and processes. Models are presented for end milling, face milling, and cylinder boring which take into account the cutting conditions, tool geometry, workpiece geometry, and system element dynamics. Furthermore, these models explicitly recognize the presence of machining process noise factors such as cutter runout and tool wear. Representative applications for these models are given. A methodology is described for the simultaneous engineering of products and manufacturing processes which incorporates models for the unit manufacturing processes, the manufacturing system, and the product to be produced.

Three typical PM-forged steels made from Höganäs powders have been studied with regard to their machining behaviour, utilizing a special machinability test developed and utilized since 1960 by Volvo AB in Sweden. This test permits to rate steels relative to a standardized free-machining steel. Guided by the results from this test, recommendable cutting data for turning, milling, drilling, tapping and reaming of the three PM-forged materials have been established empirically. It is concluded that all three materials machine notably better than a common tough-hardening steel, two of them as good and one of them better than a common constructional steel. Measured cutting and machine parameters for the PM-forged materials scattered notably less than those for comparable wrought steels.

STANDARDS of accuracy in forging are subject to constant revision. Accuracy depends on the equipment used, and the limit of forging accuracy was thought to have been reached because of the structural limitations in machines of existing types. However, the development of a new type of pressure machine has again caused a revision of our ideas of the accuracy attainable. Finish forging on this machine can be done on the heat remaining from forging or annealing, at a temperature below that at which scale is formed. Cold coining is also done with this machine with a high degree of accuracy and uniformity. What may be referred to as pressure machining of forgings eliminates roughing cuts, reduces the number of handlings and, in some cases, entirely eliminates further machining. Other economies resulting from uniformity are the facility with which work fits into chucks, jigs and hoppers and the uniformity in weight of parts such as connecting-rods.

Particulate reinforced metal matrix composites (PMMCs) based on aluminium alloys are used in automotive industry because of their low cost and improved mechanical properties at high temperature. Automotive producers are testing prototypes in aluminium PMMCs such as brake disk and drum, callipers, piston and cylinder liners. For many components the production of a good surface finish is essential and therefore will necessitate some machining. The presence of hard abrasive ceramic particulates results in rapid tool wear and high machining cost. Today, this cost effective problem is the main obstacle to aluminium PMMCs wider acceptance in the automotive market. Machining practices have not been optimised, but some studies has stated that roughing with carbide tools (especially uncoated WC tools) and finishing with polycrystalline diamond (PCD) tools are the most economical way in machining aluminium PMMCs.

A machine tool with four-control axis for machining of engine cams is presented and built to improve a machining efficiency in this study. A workpiece is fixed at a spindle chuck and placed between two cutting tools on the machine tool. Positions of the cutting direction of two cutting tools are controlled independently with two linear servomotors set on a linear rail guide. The linear servomotors are controlled with a PC, various products with non-circular cross sections can be then machined by the machine tool. Machining experiments shows that the machine tool is effective to reduce machining time and improve machining accuracy.