Selection methodologies of materials and manufacturing processes

Santos, Sydney Ferreira; Ferrante, M.

doi:10.1590/s1516-14392003000400010

Cited by 8 publications

(7 citation statements)

References 9 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This simplifies the Boltzmann collision operator and thence this model is widely used in kinetic theory [14][15][16]. The Maxwell model is analytically tractable even when the collisions are inelastic as shown in a number of recent studies of uniform and polydisperse granular gases [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Impurity in a Maxwellian unforced granular fluid

Ben‐Naim

Krapivsky

2002

Eur. Phys. J. E

View full text Add to dashboard Cite

We investigate velocity statistics of an impurity immersed in a uniform granular fluid. We consider the cooling phase, and obtain scaling solutions of the inelastic Maxwell model analytically. First, we analyze identical fluid-fluid and fluid-impurity collision rates. We show that light impurities have similar velocity statistics as the fluid background, although their temperature is generally different. Asymptotically, the temperature ratio increases with the impurity mass, and it diverges at some critical mass. Impurities heavier than this critical mass essentially scatter off a static fluid background. We then analyze an improved inelastic Maxwell model with collision rates that are proportional to the average fluid-fluid and fluid-impurity relative velocities. Here, the temperature ratio remains finite, and the system is always in the light-impurity phase. Nevertheless, ratios of sufficiently high-order moments / may diverge, a consequence of the multiscaling asymptotic behavior.

show abstract

Section: Introductionmentioning

confidence: 99%

Impurity in a Maxwellian unforced granular fluid

Ben‐Naim

Krapivsky

2002

Eur. Phys. J. E

View full text Add to dashboard Cite

show abstract

“…The order of terms in this expansion must be smaller than the order of the singular term, x ν . The coefficients φ n , needed for calculating transport coefficients [53], yield the leading asymptotic behavior of the velocity moments,…”

Section: The Freely Cooling Casementioning

confidence: 99%

“…Historically, it played an important role in the development of kinetic theory [32][33][34][35][36], and it remains the subject of current research [37][38][39]. Recently, it has been realized that the Maxwell model is analytically tractable even for inelastic collisions [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]. In this review, we detail dynamics of uniform inelastic gases, isolated impurities in uniform gases, and binary mixtures.…”

Section: Introductionmentioning

confidence: 99%

The Inelastic Maxwell Model

Ben‐Naim

Krapivsky

2003

Granular Gas Dynamics

View full text Add to dashboard Cite

Dynamics of inelastic gases are studied within the framework of random collision processes. The corresponding Boltzmann equation with uniform collision rates is solved analytically for gases, impurities, and mixtures. Generally, the energy dissipation leads to a significant departure from the elastic case. Specifically, the velocity distributions have overpopulated high energy tails and different velocity components are correlated. In the freely cooling case, the velocity distribution develops an algebraic high-energy tail, with an exponent that depends sensitively on the dimension and the degree of dissipation. Moments of the velocity distribution exhibit multiscaling asymptotic behavior, and the autocorrelation function decays algebraically with time. In the forced case, the steady state velocity distribution decays exponentially at large velocities. An impurity immersed in a uniform inelastic gas may or may not mimic the behavior of the background, and the departure from the background behavior is characterized by a series of phase transitions.

show abstract

“…However, as we will explain in Section 2, developing DSS to assist manufacturing process selection (MPS) has not gained sufficient attention in recent studies. In fact, process selection is more complex and difficult than material selection for the following reasons [10]:…”

Section: Introductionmentioning

confidence: 99%

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Mabkhot

Al-Samhan

Hidri

2019

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

In nowadays industry 4.0 and changeable manufacturing context, designers and manufacturing engineers struggle to determine appropriate quick, accurate (with flawless quality), and cost-effective processes to design highly customized products to meet customer requirements. To determine manufacturing processes, the matching between product features, material characteristics, and process capabilities needs to be optimized. Finding such an optimized matching is usually referred to as manufacturing process selection (MPS), which is not an easy task because of the infinite combinations of product features, numerous material characteristics, and various manufacturing processes. Although problems associated with MPS have received considerable attention, semantic web technologies are still underexplored and their potential is still uncovered. Almost no previous study has considered combining case-based reasoning (CBR) with ontologies, a famous and powerful semantic web enabler, to achieve MPS. In this study, we developed a decision support system (DSS) for MPS based on ontology-enabled CBR. By applying automatic reasoning and similarity retrieving on an industrial case study, we show that ontologies enable process selection by determining competitive matching between product features, material characteristics, and process capabilities and by endorsing effective case retrieval.

show abstract

Selection methodologies of materials and manufacturing processes

Cited by 8 publications

References 9 publications

Impurity in a Maxwellian unforced granular fluid

Impurity in a Maxwellian unforced granular fluid

The Inelastic Maxwell Model

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Contact Info

Product

Resources

About