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Huang W.,University of Massachusetts Dartmouth | Liu J.,University of Massachusetts Dartmouth | Chalivendra V.,University of Massachusetts Dartmouth | Ceglarek D.,University of Warwick | And 2 more authors.
IIE Transactions (Institute of Industrial Engineers) | Year: 2014

A Statistical Modal Analysis (SMA) methodology is developed for geometric variation characterization, modeling, and applications in manufacturing quality monitoring and control. The SMA decomposes a variation (spatial) signal into modes, revealing the fingerprints engraved on the feature in manufacturing with a few truncated modes. A discrete cosine transformation approach is adopted for mode decomposition. Statistical methods are used for model estimation, mode truncation, and determining sample strategy. The emphasis is on implementation and application aspects, including quality monitoring, diagnosis, and process capability study in manufacturing. Case studies are conducted to demonstrate application examples in modeling, diagnosis, and process capability analysis. © 2014 Taylor and Francis Group, LLC.


Liu J.,University of Massachusetts Dartmouth | Huang W.,University of Massachusetts Dartmouth | Kong Z.,Oklahoma State University | Zhou Y.,Dimensional Control Systems , Inc.
ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) | Year: 2013

Geometric dimensioning & tolerancing (GD&T) and process capability indices (PCIs) play critical roles in quality assurance. Conventional PCIs, when used together with GD&T, strongly rely on certain assumptions (e.g. normality and regularity of specification region). GD&T requirements often involve interrelated tolerances, creating irregular tolerance regions. Violation of these assumptions misleads the results (18) and interpretation in applications. A non-conformity (NC) index is developed based on nonparametric distribution model and numerical assessment techniques. Kernel is used for probability density (pdf) estimation and Monte Carlo integration algorithm is adopted for NC analysis, i.e. integration of a pdf over a specification region. The method is validated by case study. Copyright © 2013 by ASME.


Bastani K.,Oklahoma State University | Kong Z.,Oklahoma State University | Huang W.,University of Massachusetts Dartmouth | Huo X.,Georgia Institute of Technology | Zhou Y.,Dimensional Control Systems , Inc.
IEEE Transactions on Automation Science and Engineering | Year: 2013

Dimensional integrity has a significant impact on the quality of the final products in multistation assembly processes. A large body of research work in fault diagnosis has been proposed to identify the root causes of the large dimensional variations on products. These methods are based on a linear relationship between the dimensional measurements of the products and the possible process errors, and assume that the number of measurements is greater than that of process errors. However, in practice, the number of measurements is often less than that of process errors due to economical considerations. This brings a substantial challenge to the fault diagnosis in multistation assembly processes since the problem becomes solving an underdetermined system. In order to tackle this challenge, a fault diagnosis methodology is proposed by integrating the state space model with the enhanced relevance vector machine (RVM) to identify the process faults through the sparse estimate of the variance change of the process errors. The results of case studies demonstrate that the proposed methodology can identify process faults successfully. © 2004-2012 IEEE.


Patent
Dimensional Control Systems , Inc. and Cool Grind Technologies Llc | Date: 2014-03-12

A programmable coolant nozzle system and method for grinding wheel machines. The system comprises a fluid manifold block that automatically or manually follows the wear of the grinding wheel, to position coolant jets tangential to the wheel surface throughout the life of the grinding wheel. The positioning is by an arcuate motion, through a parallelogram mechanism, to ensure that the coolant jets remain at the same angle to the grinding wheel surface throughout the entire range of motion.


Bastani K.,Virginia Polytechnic Institute and State University | Kong Z.J.,Virginia Polytechnic Institute and State University | Huang W.,University of Massachusetts Dartmouth | Zhou Y.,Dimensional Control Systems , Inc.
IIE Transactions (Institute of Industrial Engineers) | Year: 2016

Developments in sensing technologies have created the opportunity to diagnose the process faults in multi-station assembly processes by analyzing measurement data. Sufficient diagnosability for process faults is a challenging issue, as the sensors cannot be excessively used. Therefore, there have been a number of methods reported in the literature for the optimization of the diagnosability of a diagnostic method for a given sensor cost, thus allowing the identification of process faults incurred in multi-station assembly processes. However, most of these methods assume that the number of sensors is more than that of the process errors. Unfortunately, this assumption may not hold in many real industrial applications. Thus, the diagnostic methods have to solve underdetermined linear equations. In order to address this issue, we propose an optimal sensor placement method by devising a new diagnosability criterion based on compressive sensing theory, which is able to handle underdetermined linear equations. Our method seeks the optimal sensor placement by minimizing the average mutual coherence to maximize the diagnosability. The proposed method is demonstrated and validated through case studies from actual industrial applications. Copyright © 2016 “IIE”


Jasurda D.,Dimensional Control Systems , Inc.
SAE Technical Papers | Year: 2015

The aerospace industry is continually becoming more competitive. With an aircraft's large number of components, and the large supplier base used to fabricate these components, it can be a daunting task to manage the quality status of all parts in an accurate, timely and actionable manner. This paper focuses on a proof of concept for an aircraft fuselage assembly to monitor the process capability of machined parts at an aircraft original equipment manufacturer (OEM) and their supply chain. Through the use of standardized measurement plans and statistical analysis of the measured output, the paper will illustrate how stakeholders can understand the process performance details at a workcell level, as well as overall line and plant performance in real time. This ideal process begins in the product engineering phase using simulation to analyze the tolerance specifications and assembly process strategy, with one of the outputs being a production measurement plan. This establishes clear guidelines for consistency in the inspection process. The measured data generated during the inspections is aggregated, analyzed and reported as a process capability index. This index is monitored in real time to track quality status across the organization. Issues are identified, reported and resolved using root cause drill downs to find the source within seconds or minutes of the measurements being made. By showing production variation based on data, aircraft manufacturers are creating actionable reporting and quality tracking for process capability at their production sites, on a continuous and nearly instantaneous basis. Copyright © 2015 SAE International.


Jasurda D.,Dimensional Control Systems , Inc.
SAE Technical Papers | Year: 2012

Users of a well-thought-out dimensional engineering (DE) process and the latest simulation-based tolerance analysis tools can greatly reduce the need for physical prototypes through virtual analysis. This presentation will highlight how tolerance analysis tools used as part of a DE process enable users to complete the development and launch of new and enhanced products in far less time than the competition. Don Jasurda, an experienced industry speaker, will describe how simulation-based tools used in a "closed-loop" DE process enable users to identify potential engineering issues in the virtual world instead of using physical prototypes. He will highlight real-life case study examples where automotive OEMs and suppliers have been able to use such tools to: Quickly predict and respond to the affects of variation and its impact on product quality.Identify and fix engineering problems in the earliest phases of product development when the costs of changes are low, minimizing engineering and tooling changes needed later in the program stages when the financial impact is dramatically higher.Address the three key "fit, finish and function" questions that drive the perceived quality of their products:Do things fit together as intended?Does the product look like it supposed to look?Does the product function as intended?Link cost factors with tolerance adjustments, enabling users to determine the optimal trade-offs between cost and quality, precisely meeting their quality requirements while avoiding unnecessarily tight tolerances that can prevent them from meeting their cost goals. Those companies that make the best use of the DE process and tolerance analysis tools are able to completely understand dimensional fit characteristics and quality status before commencing the build process. This has resulted in shorter launch cycles, improved process capabilities, reduced scrap and less production downtime. Copyright © 2012 SAE International.


Jasurda D.,Dimensional Control Systems , Inc.
SAE Technical Papers | Year: 2012

Quality itself is no longer a differentiator among aerospace manufacturers. High quality is expected and achievable. With enough time and money, any manufacturer can turn around a high-quality product. Around the globe, the focus of manufacturing quality is shifting to a discussion about the cost of quality and how to manage it. The question being asked by manufacturers is no longer how to achieve quality, but how to achieve it within cost and time constraints. The aerospace manufacturer that can achieve quality with the least expense, while producing products the fastest, is the one that will win in today's tough, global market. This paper will describe the "closed-loop" approach to dimensional engineering, utilizing virtual simulations and tolerance analyses, and how such an approach can link cost factors with tolerance adjustments so that users have the data they need to make the most strategic business decisions regarding the balance between quality and cost. With such an approach, users are able to determine how to precisely meet their quality requirements by identifying and focusing on the key points affecting quality while avoiding unnecessarily tight tolerances that can prevent them from achieving cost and time goals. Copyright © 2012 SAE International.


Simulation-based tolerance analysis is the accepted standard for dimensional engineering in aerospace today. Sophisticated 3D model-based tolerance analysis processes enable engineers to measure variation in complex, often large, assembled products quickly and accurately. Best-in-class manufacturers have adopted Quality Intelligence Management tools for collecting and consolidating this measurement data. Their goal is to completely understand dimensional fit characteristics and quality status before commencing the build process. This results in shorter launch cycles, improved process capabilities, reduced scrap and less production downtime. This paper describes how to use simulation-based approaches to correlate the theoretical tolerance analysis results produced during engineering simulations to actual as-built results. This allows engineers to validate or adjust as-designed simulation parameters to more closely align to production process capabilities. They can apply this knowledge to validate, capture and reuse best practices on future programs. Not only does simulation-based tolerance analysis enable engineers to completely understand the dimensional fit characteristics and quality status of products before commencing the build process, it can be used to correlate the theoretical tolerance analysis results produced during the engineering simulation to the actual as-built results determined by the Quality Intelligence Management process. This, in turn, enables engineers to validate or adjust the as-designed simulation parameters to more closely align to the production process capability. Using real-life examples, this paper describes how this "closed-loop" approach provides today's aerospace manufacturers with the opportunity to validate, capture and reuse best practices on future programs for both engineering simulation and manufacturing. As aerospace manufacturers face tighter program cycles and budgets, they can apply this correlation approach to reuse existing and proven manufacturing process elements on new and redesign programs - improving efficiency, reducing costs, and completing program development and enhancements faster than the competition. Copyright © 2011 SAE International.


Jasurda D.,Dimensional Control Systems , Inc.
SAE Technical Papers | Year: 2015

The effects of thermal expansion and gravity on assembly processes in automotive manufacturing can and often do cause unexpected variation. Not only do these effects cause assembly issues, they can also create non-conformance and warranty problems later in the product lifecycle. Using 3D CAD models, advances in simulation allow engineers to design out these influences through a combination of tooling, process and tolerance changes to reduce costs. This whitepaper examines the process of simulating the effect of both thermal expansion and gravity on automotive structures. Using real life examples, a number of solutions were determined and tested in a simulated environment to reduce product variation and account for unavoidable environmental variation. © 2015 SAE International.

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