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In this paper we present an integrated approach which combines analysis of the effect of simultaneous variations in model input parameters on component or system temperatures. The sensitivity analysis can be conducted by varying model input parameters using specific values that may be of interest to the user. The alternative approach is to use a structured set of parameters generated in the form of a DFSS DOE matrix. The matrix represents a combination of simulation conditions which combine the control factors (CF) and noise factors. CF’s are the design parameters that the engineer can modify to achieve a robust design. Noise factors include parameters that are outside the control of the design engineer. In automotive thermal management, noise factors include changes in ambient temperature, exhaust gas temperatures or aging of exhaust system or heat shields for example.

Automotive companies are studying to add extra value in their vehicles by enhancing powertrain sound quality. The objective is to create a brand sound that is unique and preferred by their customers since quietness is not always the most desired characteristic, especially for high-performance products. This paper describes the process of developing a brand powertrain sound for a high-performance vehicle using the DFSS methodology. Initially the customer's preferred sound was identified and analyzed. This was achieved by subjective evaluations through voice-of-customer clinics using vehicles of similar specifications. Objective data were acquired during several driving conditions. In order for the design process to be effective, it is very important to understand the relationship between subjective results and physical quantities of sound. Several sound quality metrics were calculated during the data analysis process.

A cargo box is a key structure in a pickup truck which is used to hold various items. Therefore, a cargo box must be durable and robust under different ballast conditions when subjected to road load inputs. This paper discusses a Design for Six Sigma (DFSS) approach to improve the durability of cargo box panel in its early development phase. Traditional methods and best practices resulted in multiple iterations without an obvious solution. Hence, DFSS tools were proposed to find a robust and optimum solution. Key control factors/design parameters were identified, and L18 Orthogonal Array was chosen to optimize design using CAE tools. The optimum design selected was the one with the minimum stress level and the least stress variation. This design was confirmed to have significant improvement and robustness compared to the initial design. DFSS identified load paths which helped teams finally come up with integrated shear plate to resolve the durability concern.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

It is a challenging task to find an optimal design concept for a truck frame structure given the complexity of loading conditions, vehicle configurations, packaging and other requirements. In addition, there is a great emphasis on light weight frame design to meet stringent emission standards. This paper provides a framework for fast and efficient development of a frame structure through various design phases, keeping durability in perspective while utilizing various weight reduction techniques. In this approach frame weight and stiffness are optimized to meet strength and durability performance requirements. Fast evaluation of different frame configurations during the concept phase (I) was made possible by using DFSS (Design for Six Sigma) based system synthesis techniques. This resulted in a very efficient frame ladder concept selection process.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

In 2006, the Insurance Institute for Highway Safety (IIHS) released a new Low Speed Bumper Test Protocol for passenger cars1. The new test protocol included the development of a deformable barrier that the vehicle would impact at low speeds. IIHS positioned the new barrier to improve correlation to low speed collisions in the field, and also to assess the ability of the bumper system to protect the vehicle from damage. The bumper system must stay engaged to the barrier to protect other vehicle components from damage. The challenge is to identify the bumper system design features that minimize additional cost and mass to keep engagement to the barrier. The results of the Design for Six Sigma analysis identified the design features that increase the stiffness of the bumper system enable it to stay engaged to the barrier and reduce the deflection.

Nowadays development of automotive HVAC is a challenging task wherein thermal comfort and safety are very critical factors to be met. HVAC system is responsible for the demisting and defrosting of the vehicle’s windshield and for creating/maintaining a pleasing environment inside the cabin by controlling airflow, velocity, temperature and purity of air. Fog or ice which forms on the windshield is the main reason for invisibility and leads to major safety issues to the customers while driving. It has been shown that proper clear visibility for the windshield could be obtained with a better flow pattern and uniform flow distribution in the defrost mode of the HVAC system and defrost duct. Defroster performance has received significant attention from OEMs to meet the specific global performance standards of FMVSS103 and SAE J902. Therefore, defroster performance is seriously taken into consideration during the design of HVAC system and defroster duct.

Geometry Tree is a term describing the product assembly structure and the manufacturing process for the product. The concept refers to the assembly structure of the final vehicle (the Part Tree) and the assembly process and tools for the final product (the Process Tree). In the past few years, the Geometry Tree-based quality process was piloted in the FCA US LLC assembly plants and has since evolved into a standardized quality control process. In the Part Tree process, the coordinated measurements and naming convention are enforced throughout the different levels of detailed products to sub-assemblies and measurement processes. The Process Tree, on the other hand, includes both prominently identified assembly tools and the mapping of key product characteristics to key assembly tools. The benefits of directly tying critical customer characteristics to actual machine components that have a high propensity to influence them is both preventive and reactive.

Vehicle suspension parts are subjected to variable road loads, manufacturing process variation and high installation loads in assembly process. These parts must be robust to usage conditions to function properly in the field. Design for Six Sigma (DFSS) tools and Taguchi Method were used to optimize initial rear suspension trailing arm design. Project identified key control factor/design parameters, to improve part robustness at the lowest cost. Optimized design performs well under higher road loads and meets stringent durability requirements. This paper evokes use of Taguchi Method to design robust rear suspension trailing arm and study effect of selected design parameters on robustness, stress level/durability and part cost.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

This study documents a method developed for dynamically measuring occupant pocketing during various low-speed rear impact, or “whiplash” sled tests. This dynamic pocketing measurement can then be related to the various test parameters used to establish the performance rating or compliance results. Consumer metric and regulatory tests discussed within this paper as potential applications of this technique include, but are not limited to, the Insurance Institute for Highway Safety (IIHS) Low Speed Rear Impact (LSRI) rating, Federal Motor Vehicle Safety Standard (FMVSS) 202a, and European New Car Assessment Program (EURO-NCAP) whiplash rating. Example metrics are also described which may be used to assist in establishing the design position of the head restraint and optimize the balance between low-speed rear impact performance and customer comfort.

Engineering has continuously strived to improve the vehicle development process to achieve high quality designs and quick to launch products. The design process has to have the tools and capabilities to help ensure both quick to the market product and a flawless launch. To achieve high fidelity and robust design, mistakes and other quality issues must be addressed early in the engineering process. One way to detect problems early is to use the math based modeling and simulation techniques of the analysis group. The correlation of the actual vehicle performance to the predictive model is crucial to obtain. Without high correlation, the change management process begins to get complicated and costs start to increase exponentially. It is critical to reduce and eliminate the risk in a design up front before tooling begins to kick off. The push to help achieve a high rate of correlation has been initiated by engineering management, seeing this as an asset to the business.

Current manufacture of alternative energy sources for automobiles, such as fuel cells and lithium-ion batteries, uses repeating energy modules to achieve targeted balances of power and weight for varying types of vehicles. Specifically for lithium-ion batteries, tens to hundreds of identical plastic parts are assembled in a repeating fashion; this assembly of parts requires complex dimensional planning and high degrees of quality control. This paper will address the aspects of dimensional quality for repeated, injection molded thermoplastic battery components and will include the following: First, dimensional variation associated with thermoplastic components is considered. Sources of variation include the injection molding process, tooling or mold, lot-to-lot material differences, and varying types of environmental exposure. Second, mold tuning and cavity matching between molds for multi-cavity production will be analyzed.

The main function of mobile air conditioning system in a vehicle is to provide the thermal comfort to the occupants sitting inside the vehicle at all environmental conditions. The function of ducts is to get the sufficient airflow from the HVAC system and distribute the airflow evenly throughout the cabin. In this paper, the focus is to optimize the rear passenger floor duct system to meet the target requirements through design for six sigma (DFSS) methodology. Computational fluid dynamics analysis (CFD) has been used extensively to optimize system performance and shorten the product development time. In this methodology, a parametric modeling of floor duct design using the factors such as crossectional area, duct length, insulation type, insulation thickness and thickness of duct were created using CATIA. L12 orthogonal design array matrix has been created and the 3D CFD analysis has been carried out individually to check the velocity and temperature.

In an automotive air-conditioning (AC) system, the heater system plays a major role during winter condition to provide passenger comforts as well as to clear windshield defogging and defrost. In order to meet the customer satisfaction the heater system shall be tested physically in severe cold conditions to meet the objective performance in wind tunnel and also subjective performance in cold weather regions by conducting on road trials. This performance test is conducted in later stage of the program development, since the prototype or tooled up parts will not be available at initial program stage. The significance of conducting the virtual simulation is to predict the performance of the HVAC (Heating ventilating air-conditioning) system at early design stage. In this paper the development of 1D (One dimensional) model with floor duct systems and vehicle cabin model is studied to predict the performance. Analysis is carried out using commercial 1D simulation tool KULI®.

In order to meet LEV III, EURO 6C and Beijing 6 emission levels, Original Equipment Manufacturers (OEMs) can potentially implement unique aftertreatment systems solutions which meet the varying legislated requirements. The availability of various washcoat substrates and PGM loading and ratio options, make selection of an optimum catalyst system challenging, time consuming and costly. Design for Six Sigma (DFSS) methodologies have been used in industry since the 1990s. One of the earliest applications was at Motorola where the methodology was applied to the design and production of a paging device which Consumer Reports called “virtually defect-proof”.[1] Since then, the methodology has evolved to not only encapsulate complicated “Variation Optimization” but also “Design Optimization” where multiple factors are in play. In this study, attempts are made to adapt the DFSS concept and methodology to identify and optimize a catalyst for diesel applications.

A vehicle’s exterior fit and finish, in general, is the first system to attract customers. Automotive exterior engineers were motivated in the past few years to increase their focus on how to optimize the vehicle’s exterior panels split lines quality and how to minimize variation in fit and finish addressing customer and market required quality standards. The design engineering’s focus is to control the deviation from nominal build objective and minimize it. The fitting process follows an optimization model with the exterior panel’s location and orientation factors as independent variables. This research focuses on addressing the source of variation “contributed factors” that will impact the quality of the fit and finish. These critical factors could be resulted from the design process, product process, or an assembly process. An empirical analysis will be used to minimize the fit and finish deviation.

As customer dissatisfaction with interior trim components is tracked by the JDPowers question on “surface durability”, there is a need to increase the durability of the parts that are molded in color. In particular, door trim panel lowers are susceptible to surface damage which results in an unfavorable appearance. To address this issue, an assessment of the various factors that can affect surface durability was conducted using talc filled TPO materials in order to determine the optimum set of physical properties. The team used Design for Six Sigma (DFSS) methodology. A Taguchi orthogonal experiment was used and included control system factors of material, grain, gloss, and color. Noise factors included molding process parameters, aging, and piece to piece variation. The output was a measure of the scratch resistance of the molded plaque which was defined by a Delta L calculation.

The present study defines the functional requirements for a liftgate and the body in order to avoid rattle, squeak, and other objectionable noises. A Design For Six Sigma (DFSS) methodology was used to study the impact of various constraint components such as bumpers, wedges, and isolated strikers on functional requirements. These functional requirements include liftgate frequency, acoustic cavity frequency, and the stiffness of the liftgate body opening. It has been determined that the method of constraining the gate relative to the body opening has a strong correlation to the noise generated. The recommended functional performance targets and constraint component selection have been confirmed by actual testing on a vehicle. Recommendations for future liftgate design will be presented.