Search Results

Cost reduction in the automotive industry becomes a widely-adopted operational strategy not only for Original Equipment Manufacturers (OEMs) that take cost leader generic corporation strategy, but also for many OEMs that take differentiation generic corporation strategy. Since differentiation generic strategy requires an organization to provide a product or service above the industry average level, a premium is typically included in the tag price for those products or services. Cost reduction measures could increase risks for the organizations that pursue differentiation strategy. Although manufacturers in the automotive industry dramatically improved production efficiency in past ten years, they are still facing the pressure of cost control. The big challenge in cost control for automakers and suppliers is increasing prices of raw materials, energy and labor costs. These costs create constraints for the traditional economic expansion model.

Structure-borne inputs to hybrid FEA/SEA models could have significant effects on the model prediction accuracy. The purpose of this work was to obtain the structure-borne noise (SBN) inputs using a simplified transfer path analysis (TPA) and identify the significance of the structure-borne and airborne contributions to the spectator sound power of an engine with enclosure for future modeling references. Force inputs to the enclosure from the engine were obtained and used as inputs to a hybrid engine enclosure model for sound prediction.

In this paper, residual stress distributions in rectangular bars due to rolling or burnishing at very high rolling or burnishing loads are investigated by roll burnishing experiments and three-dimensional finite element analyses using ABAQUS. First, roll burnishing experiments on rectangular bars at two roller burnishing loads are presented. The results indicate the higher burnishing load induces lower residual stresses and the higher burnishing load does not improve fatigue lives. Next, in the corresponding finite element analyses, the roller is modeled as rigid and the roller rolls on the flat surface of the bar with a low coefficient of friction. The bar material is modeled as an elastic-plastic strain hardening material with a nonlinear kinematic hardening rule for loading and unloading.

Numerical models are used in this study to investigate the oil flow and heat transfer in the piston gallery of a diesel engine. An experiment is set up to validate the numerical models. In the experiment a fixed, but adjustable steel plate is instrumented and pre-heated to a certain temperature. The oil is injected vertically upwards from an underneath injector and impinges on the bottom of the plate. The reduction of the plate temperature is recorded by the thermocouples pre-mounted in the plate. The numerical models are used to predict the temperature history at the thermocouple locations and validated with the experimental data. After the rig model validation, the numerical models are applied to evaluate the oil sloshing and heat transfer in the piston gallery. The piston motion is modeled by a dynamic mesh model, and the oil sloshing is modeled by the VOF (volume of fluid) multiphase model.

Increasingly, the exchanges of data in complex ECM (Electronic Control Module) systems rely on multiple communication networks across various physical and network layers. This has greatly increased system flexibility and provided an excellent medium to create well-defined exchangeable interfaces between components; however this added flexibility comes with increased network complexity. A system-level approach allows for the optimization of data exchange and network configuration as well as the development of a comprehensive network failure strategy. Many current ECM systems utilize complex multi-network communication strategies to exchange and control data to components. Recently, Caterpillar implemented an HIL (Hardware-In-the-Loop) test system that provides an approach for developing and testing a comprehensive ECM network strategy.

Analyzing the combustion characteristics, engine performance, and emissions pathways of the internal combustion (IC) engine requires management of complex and an increasing quantity of data. With this in mind, effective management to deliver increased knowledge from these data over shorter timescales is a priority for development engineers. This paper describes how this can be achieved by combining conventional engine research methods with the latest developments in process informatics and statistical analysis. Process informatics enables engineers to combine data, instrumental and application models to carry out automated model development including optimization and validation against large data repositories of experimental data.

The automotive industry is dramatically changing. Many automotive Original Equipment Manufacturers (OEMs) proposed new prototype models or concept vehicles to promote a green vehicle image. Non-traditional players bring many latest technologies in the Information Technology (IT) industry to the automotive industry. Typical vehicle’s characteristics became wider compared to those of vehicles a decade ago, and they include not only a driving range, mileage per gallon and acceleration rating, but also many features adopted in the IT industry, such as usability, connectivity, vehicle software upgrade capability and backward compatibility. Consumers expect the latest technology features in vehicles as they enjoy in using digital applications in laptops and mobile phones. These features create a huge challenge for a design of a new vehicle, especially for a human-machine-interface (HMI) system.

A nonlinear, three-dimensional finite element analysis of the cylinder head gasket joint has been developed to allow accurate prediction of global and local joint behavior during engine operation. Nonlinear material properties and load cases that simulate full cycle engine operation are the analysis foundation. The three-dimensional, nonlinear, full-cycle simulation accurately predicts cylinder head gasket joint response to assembly, thermal, and cylinder pressure loading. Predictions correlate well with measured engine test data. Analysis results include local pressure distribution and global load splits. Insight into joint loading and an improved understanding of overall joint behavior provide the basis for informed design and development decisions.

Caterpillar uses heat treatment to enhance the properties of a significant number of parts. Traditional heat treat process optimization is both time consuming and expensive when done by empirical methods. This paper describes a computer simulation of the heat treatment process, developed by Caterpillar, based upon finite element analysis. This approach combines thermal, microstructural, and stress analysis to accurately model material transformation during quenching. Examples are presented to illustrate the program.

The growth of auto sales in emerging markets provides a good opportunity for automakers. Cost is a key factor for any automaker to win in an emerging market. This paper analyzes risks and opportunities in a low cost manufacturing environment. The Chinese auto market is used as an example and three categories of risks are analyzed. A typical risk assessment for cost reduction includes the analysis of environment risks, process risks and strategic risks associated with all phases of a product life. In an emerging market, emission regulations are a rapidly-evolving environment variable, since most countries with less regulated emission codes try to catch up with the newly- developed technologies to meet sustainable growth targets. Emission regulations have a huge impact on product design, manufacturing and maintenance in the automotive industry, and hence the related cost reduction must be thoroughly analyzed during risk assessment.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.