Search Results for model quality management

This article provides guidelines for the assessment of model quality in materials science and engineering. It discusses the fundamentals of model quality assessment and the calibration of mechanistic material models. The article reviews the considerations for the model verification during software implementation planning to identify suitable programs, software components, and programming languages. It describes the validity tests used in model validation, including boundary-value tests, degenerate problem tests, sensitivity tests, and benchmarking. The article also presents an example of model calibration, verification, and validation for the prediction of martensite start temperature of steels.

The objective of dimensional management is to create a design and process that absorbs as much variation as possible without affecting the function of the product. This article describes the steps followed by the dimensional management process. These include defining product dimensional requirements, determining process and product requirements, ensuring accurate documentation, developing a measurement plan that validates product requirements, establishing manufacturing capabilities versus design intent, and establishing production-to-design feedback loop. The article discusses the simulation model in terms of a functional feature product model, component part variation, assembly method variation, measurement schemes, and assembly sequences.

Additive manufacturing (AM) creates parts layer by layer directly from three-dimensional computer-aided design data. This article discusses systematic ways to address the challenges in AM data integration by exploring various AM-specific data-integration scenarios that can improve the current AM ecosystem. Representative AM data sources are also described. A reference framework that captures the heterogenous AM data sources and existing data-integration mechanisms are used. General data-integration practices—based on existing manufacturing data and lab information system integration experiences—are recommended to automate AM data flow, operations, and development. Lastly, the article discusses the seven steps in the big-data-integration workflow.

The qualification of additive manufacturing (AM) processes and the certification of AM parts is recognized as a significant impediment to the rapid, low-cost deployment of AM manufacturing. The challenges are multifaceted; however, it is an attempt to apply conventional qualification approaches to an inherently different process that has caused the most difficulty. This article examines the conventional qualification methodology and explores how the unique characteristics of AM pose a set of qualification challenges. The extant approach to the qualification of AM processes is described, followed by a discussion on a possible future state.

This article defines quality, quality assurance, quality control, and quality management of processes, products, and services. It describes the evolution of quality control and quality assurance in the coatings industry. The article also discusses the standards, quality programs, and certifications in the coatings industry.

This article details findability, accessibility, interoperability, and reusability (FAIR) additive manufacturing data management principles and examines related motivations, benefits, and challenges. It explains opportunities to advance the state of the AM community efforts in fostering FAIR data management practices/principles and outlines the consequence of such efforts on technology maturation and industrialization for AM technologies.

The purpose of accreditation is to evaluate and assure high-quality results from suppliers of products or services without incurring the costs associated with auditing each supplier. This article describes laboratory accreditation based on the general requirements of International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) Guide 25, "General Requirements for the Competence of Calibration and Testing Laboratories." The Guide 25 is a balanced standard that addresses quality system requirements of ISO 9000 and the technical requirements needed to perform testing or calibration. The article describes an accreditation process that would enhance the international acceptance of test data for mechanical testing laboratories.

This article focuses on the technologies and applications of additive manufacturing (AM) in the oil and gas industry. It then presents the challenges of AM and the oil and gas industry. The article provides a detailed description of the critical steps in the AM process chain, including part selection, design optimization, and process planning, control, and inspection. Qualification and certification standardization is discussed, as is a commitment to reduce the carbon footprint of the manufacturing sector through AM. It ends with the future outlook of AM in the oil and gas industry.

Tooling and assembly methodologies for advanced composites have steadily improved as a result of advancements in materials, through the use of computer-aided design/computer-aided manufacturing technology, and through application of sophisticated design for manufacturing and assembly concepts. This article reviews techniques and technologies that are used to control the quality of tooling and assembly methods for composite components.

In situ process monitoring includes any technologies that monitor or inspect during an additive manufacturing (AM) process. This article presents the types, process considerations, and challenges of in situ monitoring technologies that can be implemented during an AM process. The types include system health monitoring, melt pool monitoring, and layer monitoring. The article discusses data analysis, and provides an overview of the integration of sensors into AM machines.

To improve the performance of additive manufacturing (AM) systems, a critical need exists for collaborative generation, acquisition, curation, and analysis of digital AM data across the product life cycle. This article discusses the history, development, and potential benefits of the AM Common Data Dictionary (AM-CDD), along with future directions.

This article briefly introduces the concepts of failure analysis and root cause analysis (RCA), and the role of failure analysis as a general engineering tool for enhancing product quality and failure prevention. It reviews four fundamental categories of physical root causes, namely, design deficiencies, material defects, manufacturing/installation defects, and service life anomalies, with examples. The article describes several common charting methods that may be useful in performing an RCA. It also discusses other failure analysis tools, including review of all sources of input and information, people interviews, laboratory investigations, stress analysis, and fracture mechanics analysis. The article concludes with information on the categories of failure and failure prevention.

The aviation industry has been driving the use of additive manufacturing (AM), moving from one-off demonstrator or pathfinder components toward higher-volume serial production applications. This article presents an introduction to AM in aviation, explaining how aviation requirements apply to AM. It also presents advancements, standards, and future expectations of aviation.

Mathematical models have been used for over five decades in industrial heat-treating operations. Most of these modeling efforts have emanated from academia or research institutes, with the primary approach of mathematically capturing heat-treating processes and validating quality predictions. In this article, a contrarian but more realistic scenario is considered, where two industrial problem descriptions become the starting point. The technical complexity of the industry problem has been elaborated for a deeper understanding of the issue along with elaboration of the approach and potential methods for determining a solution. Then, quantitative analyses of practical industrial problems are demonstrated. Finally, the potential shift in these approaches with the advent of Industry 4.0 is outlined.

This article presents the analytics challenges in additive manufacturing. It discusses the types and applications of data analytics. Data analytics can be classified into four types: descriptive, diagnostic, predictive, and prescriptive. The diverse applications of data analytics and machine learning include design, process-structure-properties (PSP) relationships, and process monitoring and quality control. The article also presents tools used for data analytics.

This article describes the four basic steps of the purchasing process of cast components. These steps include defining requirements and developing a purchasing plan; requesting and evaluating bids from potential sources; selecting a source and negotiating contract terms; and carrying out the contract and pursuing continuous improvement. It provides guidance on purchasing cast components and explains specific issues and approaches that have proven to be useful in purchasing castings. The article presents a list of the most significant considerations when attempting to determine the overall cost and design requirements of a metal casting.

This article briefly introduces the concepts of failure analysis, including root-cause analysis (RCA), and the role of failure analysis as a general engineering tool for enhancing product quality and failure prevention. It initially provides definitions of failure on several different levels, followed by a discussion on the role of failure analysis and the appreciation of quality assurance and user expectations. Systematic analysis of equipment failures reveals physical root causes that fall into one of four fundamental categories: design, manufacturing/installation, service, and material, which are discussed in the following sections along with examples. The tools available for failure analysis are then covered. Further, the article describes the categories of mode of failure: distortion or undesired deformation, fracture, corrosion, and wear. It provides information on the processes involved in RCA and the charting methods that may be useful in RCA and ends with a description of various factors associated with failure prevention.

Cutting tool wear is a production management problem for manufacturing industries. It occurs along the cutting edge and on adjacent surfaces. This article describes steady-state wear mechanisms, tertiary wear mechanisms, and tool replacement. It provides information on tool failure and its consequences. The article details the modeling of tool wear by using the Taylor's tool life equation. The article concludes with information on the requirements of a successful tool life testing program: the test plan objective, designing the test, conducting the test, analyzing the results, and applying the results.

Geochemical modeling is being used to understand and predict scaling, susceptibility to corrosion, atmospheric corrosion rates, acid rain, corrosion film solubility, and environmental impacts of aqueous species in runoff. This article discusses the principles, limitations, and applications of the modeling. It explains how to calculate the chemical equilibrium in geochemical modeling and provides information on modeling features.

Value analysis (VA) is a team problem-solving process to improve the value of a product from the viewpoint of a user. This article presents a comparison between VA and total quality management in materials selection and design. It discusses the key attributes, concepts, and activities of the VA. The application of value engineering in U.S. government contracts and the construction industry is reviewed. The article describes the eight phases of the VA process: preparation, information, analysis, creation, synthesis, development, presentation and report, and implementation and follow-up. It presents case studies that illustrate the materials-related aspects of the VA process.