Manufacturing Materials
Manufacturing Materials - Related Videos
06:53
Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography
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Cited by 40 •
2019
This manuscript describes the processing of single multifunctional ceramic components (e.g., combinations of dense-porous structures) additively manufactured by stereolithography.
07:42
Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material
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2024
A quasistatic load-to-fracture test with a non-fixed stainless steel ball was developed to determine the fracture strength of minimally invasive posterior computer-aided design and manufacturing restorations cemented to dentin analog materials. This test models the typical loading regime responsible for the fracture of dental restorations.
08:29
Multi-material Ceramic-Based Components – Additive Manufacturing of Black-and-white Zirconia Components by Thermoplastic 3D-Printing (CerAM - T3DP)
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Cited by 27 •
2019
Here we describe a protocol for additively manufacturing black-and-white zirconia components by Thermoplastic 3D-Printing (CerAM - T3DP) and co-sintering defect-free.
11:23
Material Constants
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA In contrast to the production of cars or toasters, where millions of identical copies are made and extensive prototype testing is possible, each civil engineering structure is unique and very expensive to reproduce (Fig.1). Therefore, civil engineers must extensively rely on analytical modeling to design their structures. These models are simplified abstractions of reality and are used to...
07:40
Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
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Cited by 1 •
2020
This protocol provides a detailed list of steps to be performed for the manufacturing, control and evaluation of the climbing performance of a gecko-inspired soft robot.
FAQs
Mathematical analysis connects measured material properties with forces, heat, pressure, and deformation. Engineers use equations to estimate how a material may respond before it enters production, then examine outcomes such as strength, wear, and dimensional change. These predictions help identify whether a proposed material and design are likely to meet performance requirements under expected conditions.
Measurements provide the material data needed for analysis, while equations describe relationships among loads, temperature, pressure, deformation, and performance. Statistical models help interpret variation in material behavior and manufacturing quality rather than relying on a single observation. Together, these tools support more consistent predictions and reveal whether a process is producing reliable results.
Optimization methods compare possible choices against several goals, including performance, cost, energy use, waste, and reliability. Instead of evaluating one property in isolation, engineers can use mathematical analysis to examine trade-offs among material selection, processing conditions, and product requirements. This supports decisions that balance functional performance with efficient and more sustainable manufacturing.
Before production, mathematical analysis can estimate strength, wear, dimensional changes, energy use, and aspects of manufacturing quality. These outcomes give engineers a basis for comparing designs and materials without waiting for full-scale production. Early estimates can also expose likely performance or process problems, allowing adjustments that reduce waste and improve the reliability of finished goods.
A mathematics-based workflow begins by identifying the product's required performance, cost, and reliability goals. Engineers then use material data, measurements, and equations to evaluate responses to relevant forces, heat, pressure, or deformation. They can compare alternatives with statistical models and optimization methods before production, selecting a combination that fits design needs and manufacturing constraints.
Material data and mathematical models help engineers monitor whether manufacturing conditions are producing the intended quality. Equations describe expected responses, measurements provide evidence from the process, and statistical models help assess variation. When results depart from expectations, this information supports process adjustments before defects or inconsistent dimensions become widespread, improving production reliability and reducing material waste.
Mathematical analysis can reduce unnecessary trial production by predicting material and process outcomes in advance. Comparing energy use, waste, cost, dimensional stability, and reliability helps engineers choose more efficient combinations of materials and processing decisions. The resulting improvements support dependable products while using resources more effectively, connecting manufacturing performance with broader sustainability goals.