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In an automotive braking system, Vacuum pump is used to generate vacuum in the vacuum servo or brake booster in order to enhance the safety and comfort to the driver. The vacuum pump operation in the braking system varies from conventional to electric vehicles. The vacuum pump is connected to the alternator shaft or CAM shaft in a conventional vehicle, operates continuously at engine speed and supplies continuous vacuum to the brake servo irrespective of vacuum requirement. To sustain continuous operation, these vacuum pumps are generally oil cooled. Whereas in electric vehicles, the use of a motor-driven vacuum pump is very much needed for vacuum generation as there is no engine present. Thus, with the assistance of an electronic control unit (ECU), the vacuum pump can be operated only when needed saving a significant amount of energy contributing to fuel economy and range improvement and emission reduction.

This paper describes a multi-disciplinary damage detection methodology that can aid in detecting and diagnosing a damage in a given structural system, not limited to the example of a rotating gear presented here. Damage detection is performed on the gear stress data corresponding to the steady state conditions. The normal and damage data are generated by a finite-difference solution of elastodynamic equations of velocity and stress in generalized coordinates1. The elastodynamic solution provides a knowledge of the stress distribution over the gear such as locations of stress extrema, which in turn can lead to an optimal placement of appropriate sensors over the gear to detect a potential damage. The damage detection is performed by a multi-function optimization that incorporates Tikhonov kernel regularization reinforced by an added Laplacian regularization term as used in semi-supervised machine learning. Damage is mimicked by reducing the rigidity of one of the gear teeth.

Despite the increasing number of electrified vehicles the transportation system still largely depends on the use of fossil fuels. One way to more rapidly reduce the dependency on fossil fuels in transport is to replace them with biofuels. Evaluating the potential of different biofuels in different applications requires knowledge of their physicochemical properties. In chemistry, message passing neural networks (MPNNs) correlating the atoms and bonds of a molecule to properties have shown promising results in predicting the properties of individual chemical components. In this article a machine learning approach, developed from the message passing neural network called Chemprop, is evaluated for the prediction of multiple properties of organic molecules (containing carbon, nitrogen, oxygen and hydrogen). A novel approach using transfer learning based on estimated property values from theoretical estimation methods is applied.

Dynamic skip fire (DSF) has shown significant fuel economy improvements via reduction of pumping losses that generally affect throttled spark-ignition engines. For production readiness, DSF engines must meet regulations for on-board diagnostics (OBD-II), which require detection and monitoring of misfire in all passenger vehicles powered by an internal combustion engine. Numerous misfire detection methods found in the literature, such as those using peak crankshaft angular acceleration, are generally not suitable for DSF engines due to added complexity of skipping cylinders. Specifically, crankshaft acceleration traces may change abruptly as the firing sequence changes. This article presents a novel method for misfire detection in a DSF engine using machine learning and artificial neural networks. Two machine learning approaches are presented.

This paper discusses an emerging area of applying machine learning (ML) methods to augment traditional Computational Fluid Dynamics (CFD) simulations of road vehicle aerodynamics. ML methods have the potential to both reduce the computational effort to predict a new geometry or car condition and to explore a greater number of design parameters with the same computational budget. Similar to traditional CFD methods, there exists a broad range of approaches. In particular, the accuracy and computational efficiency of a CFD simulation vary greatly depending on the choice of turbulence model (DNS, LES, RANS) and the underlying spatial and temporal numerical discretizations. Similarly, the end-user must select the correct ML method depending on the use-case, the available input data, and the trade-off between accuracy and computational cost. In this paper, we showcase several case studies using various data-driven ML methods to highlight the promise of these approaches.

This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.

Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences.

This paper presents a detailed overview of the advantages and benefits of using 2-D barcodes, called Data Matrix codes, on Wing Fastening System (WFS) Tooling. This project was conducted on, but not limited to, the 777 Wing Fastening System (GEMCOR) tooling including the drills, fingers, and button dies. This paper will show how using Data Matrix codes to identify tooling will: Eliminate excessive downtime due to the operator using the incorrect tooling for a given tool setup. Reduce the cost associated with panel rework due to the use of incorrect tooling. Reduce the cost associated with excessive tool inventory or last minute ordering to keep up with production needs. Track tool life information for each specific tool. Provide operators with an easy to use tool setup reference document. And provide the factory with the ability to trace panel damage or defects back to the specific machine and exact tooling used.

Methods of testing brake linings by sample machine have been evolved to a standard of consistency which enables the performance of a brake to be evaluated by consideration of the geometry of the system and the coefficient of friction of the lining. This situation does, however, only hold if the way in which the lining is tested bears a close relationship to the duty cycle employed on an actual brake. In this paper the correlation between brake performance and estimates based on scale testing shows that a classification can be employed to simplify the choice of replacement linings when a single material is employed. Duo servo brakes are least amenable to this technique because of their high sensitivity.

Through effective utilization of the basic principles outlined in this paper, ECM equipment can become commonplace in manufacturing plants of the future. The three elements of an ECM machine are established by the following requirements for electrochemical machining: 1. The tool (cathode) must be positioned accurately relative to the workpiece (anode) and advanced toward the workpiece at a constant rate. There must be no relative movement between the tool and workpiece except in the direction in which machining is to be done. 2. A constant high velocity flow of clean electrolyte must be maintained in the small gap between the two electrodes. 3. Sufficient capacity of d-c current must be provided at machining gap to maintain the desired metal removal rate.

This paper gives an overview of todays machine vision technology with specific emphasis on microcomputer-based image processing and its potential as a low cost machine vision system. Topics discussed are general hardware requirements, image enhancement and segmentation techniques for binary and gray level images, two dimensional shape analysis, and additional sensors to supplement two dimensional image information.

Machine vision technology is a tool being utilized in the new GMT-400 pickup truck Body Shops for process monitoring and control. These real-time Machine Vision Dimensional Gauging systems monitor 100% of the production's critical build features such as door and window openings, hinge locations, and fender mounting brackets, Traditional gauges typically can provide data on only a small sample of production −1% or less. Correlating the machine vision systems to master gauges allows accurate data to be collected on every job as it is being built. This complete dimensional control data provides information for process monitoring as well as a means to detect tooling adjustment requirements and the ability to detect build problems, even if they occur intermittently. Several methods of performing this correlation have been investigated, with the goal being to define a correlation procedure that works well in the plant environment.

Inlet valve deposits in gasoline engines have a significant effect on engine operation with particular reference to cold starting and driveability. Present methods of quantifying these deposits by weighing them or rating them with the aid of a visual rating scale are recognized as not being reliable indices of the detrimental effect of these deposits. A valve deposit quantification system was developed that relied on the use of machine vision. Algorithms were formulated to track the silhouetted edge profile of a backlit valve from which a valve volume was determined. The valve deposit volume was calculated as the difference in volume between the valve in its clean and coked states. The system was able to detect a minimum coke deposit level of 0.06g at the 95% confidence limit, the accuracy being based on the correlation between the volume as determined by the vision system and the mass of the deposit.

Machine vision systems are becoming an important part of the automotive manufacturing process with applications ranging from inspection through to process monitoring and control. The technology is indeed becoming vital to maintaining and enhancing the quality associated with each component from the smallest assembly to the entire vehicle body. This paper will examine two machine vision applications in the automotive industry. These installations have been recently developed to satisfy the needs of ensuring that the engine assembly process guarantees the highest standards of product quality. The first application discussed in this paper, describes how machine vision is being used to verify the ‘K’ Series engine valve timing gear, prior to undertaking a series of computer controlled automatic tests. The second case study describes an alpha-numeric character recognition system which is central to the selective assembly of bearing shells of the ‘K’ Series engine.

The effectiveness of statistical techniques in the management of manufacturing processes is reviewed, and difficulties in applying these techniques in the automotive stamping and assembly environment are discussed. The use of machine vision measurement to overcome these difficulties is described and examples of functioning installations are shown. The problem encountered in evaluating such systems in terms of quality improvement is explained, costs of product specification conformance and nonconformance are defined, and quality costs for U.S. and Japanese industry are compared. Reduced nonconformance cost is identified as the probable explanation for the Japanese advantage in the cost of quality comparison, and Japanese use of Taguchi's loss function is proposed as one of the mechanisms by which this has been accomplished.

This paper will describe two Machine Vision applications at the John Deere Plow and Planter Works. The Vision Inspection System utilizes three cameras mounted on a three axis Cartesian robot to perform three-dimensional measurements on a variety of formed sheet-metal parts. The system also maintains average & range statistical control charts on all measured dimensions. The Vision Welding System utilizes four stationary cameras and four stationary LASER units to locate the ends of weld seams on a variety of similar parts ranging in length from 990 mm to 2600 mm (39 inches to 102 inches). These end points are then used to offset the weld path which is then welded by a six axis articulated robot.

An experimental machine vision-based patch-sprayer was developed. This sprayer was primarily designed to do real-time weed density estimation and variable herbicide application rate control. However, the sprayer also had the capability to do high-resolution weed mapping if proper mapping techniques were integrated. Two weed mapping methods were developed. One was a GPS signal based off-line weed mapping; another one was radar distance measurement-based on-line weed mapping. The high-resolution weed maps provided evidence to further support the patch-spraying concept. Randomly sampled field images were processed with different nozzle control zone sizes and thresholding methods to simulate sprayer performance. Fundamental system design strategies regarding these two factors were obtained through simulation. System design techniques, including system construction, weed sensing and crop-row detection algorithms were reported.

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.