Prediction Quality Attributes For Mixtures From Single Component Data
|
|
- Darrell Bruce
- 5 years ago
- Views:
Transcription
1 Prediction Quality Attributes For Mixtures From Single Component Data John Strong, PhD Sean Garner, PhD Global Pharmaceutical Sciences AbbVie, North Chicago, IL CPDG Sep 27th, 2016
2 AbbVie: Our reach is global 170+ Countries 12 Manufacturing Sites 6 R&D Sites Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 2 2
3 Our name was launched in 2013 but our story begins in years of patient care Addressing complex and serious diseases Making a remarkable impact on patients lives Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 3 3
4 We offer medicines to treat the world s challenging diseases Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 4 4
5 John s Bio BS Mech Eng., Portland State 1991 M.S. Chem Eng, Yale 1993 Ph.D. Biochem Eng, UMBC 1997 Joined Abbott 1997, AbbVie 2013 Married w/ 1 son in Lindenhurst, IL Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 5
6 Drug Product Development (DPD) the material From API molecule to solid dosage form (tablet, capsule) Multi scale Multi discipline Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 6
7 DPD development cycle Reduction in effort and cost if more predictive approaches can be used early on with empirical confirmation later. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 7
8 Two philosophies for DPD formulation/process design Just Measure It! Quick material sparing experimental approaches exist for predicting manufacturability (e.g., flow, tableting) Employing prior knowledge, test candidate formulations for manufacturability using trusted methods. Experimentally observe which ones work, discard the rest. Model It First! Utilize database of relevant material properties and a suite of predictive models using individual component data Put in silico formulation in the hands of the formulators. Generate development space guidance for further formulation development. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 8
9 The AbbVie DPD approach Embracing model based prediction wherever applicable and valueadded, as early as possible The intent is to meet the vision of QbD by interrogating a comprehensive database of relevant material properties to yield inputs to relevant models for multicomponent blends: Major effort (over 2 years) to develop the database & user interface Mean values for excipient material properties (from multi lot repeat testing) are provided to predictive models Estimates of lot to lot excipient variability can be calculated and propagated through models to obtain confidence intervals on predictions MATLAB implementation makes most of these models available to formulators through user interface Examples: Powder blend flow Blend tabletability Blend uniformity FEM tablet predictions Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 9
10 Powder blend flow
11 Motivation Good powder flowability is essential to pharmaceutical processes. Powder flowability affects: Mixing/blending Segregation Feeding/transfer/hopper flow Coating Fluidization Capsule filling Die filling/tableting Unacceptable content uniformity Variable tablet/capsule weights Variable potency Complicates/prevents manufacturing Reduces manufacturing efficiency Complicates product/process development Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 11
12 Motivation Powder flow (along with most powder behavior) is extremely difficult to model and predict. Modeling has been purely empirical which limits understanding and predictive capability. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 12
13 Quantifying flow Flow Function Coefficient: ff : consolidation stress : unconfined yield stress ff c is an excellent metric to quantify a powder s flowability in unconfined flow situations: Jenike Classification System Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 13
14 Theory what influences flow at particle level? Adhesive force between two particles: Particle Property Particle size, d p Hamaker constant, A Asperity size/surface roughness, d asp Effect on F ad with increase in property Chen et al., AIChE Journal 54 (2008) Possible effect on powder flow Separation distance, l 0 Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 14
15 Theory what influences flow at particle level? Adhesion force alone does determine powder cohesiveness! Inter particle cohesion parameter (Granular Bond Number): Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 15
16 Theory what influences flow at particle level? Inter particle cohesion parameter (Granular Bond Number): Particle Property Effect on F ad with increase in property Effect on Bo g with increase in property Possible effect on powder flow Particle size, d p Particle Density, ρ p N/A Hamaker constant, A Asperity size/surface roughness, d asp Separation distance, l 0 Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 16
17 Establishing Bo g ff c Relationship Material Granular Bond Number, Bo g APAP Avicel PH Avicel PH Avicel PH Avicel PH Flow Function Coefficient, ff c APAP (1 wt% R972) Avicel PH 105 (1 wt% R972) Avicel PH 101 (1 wt% R972) Avicel PH 102 (1 wt% R972) Avicel PH 200 (1 wt% R972) Higher interparticle cohesiveness (high B og ) correlates to poor flow (low ff c ) Capece, Ho, Strong, Gao (2015) Prediction of powder flow performance using a multi component granular Bond Number, Powder Tech. 286: Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 17
18 Establishing Bo g ff c Relationship (Cont.) ff c B og relationship: ff 1 α=15.7 β=0.27 (Maximum ff c constrained to 13.7) Flow function coefficient can be empirically related to the bond number. Cohesive non cohesive boundary (Bo g = 1) separates free flowing powders from all others. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 18
19 Bond Number for Powder Blends ff c B og relationship: Single component 1 ff ff c B og relationship: Powder blend ff, 1, Bond number of a mixture.. Weighted harmonic mean: 1,.,, Interaction between all component of a mixture are considered. Material properties of all component in the blend are related to the bulk powder behavior. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 19
20 Predicting ff c of Binary Mixtures Binary blends are well predicted. Average absolute deviation is Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 20
21 Predicting ff c of Binary Mixtures Binary blends for coated (surface modified) materials are also well predicted. Average absolute deviation is Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 21
22 Predicting ff c of Ternary Mixtures Average absolute deviation is 0.25 for as received materials. Average absolute deviation is 1.67 for coated (surface modified) materials. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 22
23 Population dependent granular Bond Number,, Accounts for the multitude of interactions between particles of different sizes within a powder Provides prediction for how ff c changes with modifications to particle size distributions of mixture components Flow Function Coefficient, ff c (-) Non-cohesive Cohesive As-Received Surface Modified Sieved Eq. (15): = 14.8, = % CI Population-Dependent Granular Bond Number, Bo* g (-) Capece, Silva, Sunkara, Strong, Gao, On the Relationship of Interparticle Cohesiveness and Bulk Powder Behavior: Flowability of Pharmaceutical Powders, J. Pharm. (in press) Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 23
24 Population dependent granular Bond Number Flow Function Coefficient, ff c (-) Experimental Prediction Experimental and predicted flow function coefficients for powder blends containing various loadings of APAP. All blends contain 1 wt% silica (Aerosil R972) wt% 40 wt% 60 wt% 80 wt% 99 wt% APAP Loading Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 24
25 Tablet tensile strength
26 Introduction A Partial History Fell & Newton, 1970 Predicted tensile strength for various forms of lactose of same PSD Sheikh Salem & Fell, 1981 also showed linear relationship for some materials Leuenberger, 1982 showed deviation from linearity, proposed interaction term to account for deviations Leuenberger, 1985 predicted interaction term using solubility parameters for limited materials Jetzer, 1986 interactions occur with dissimilar materials Ilkka 1993 Proposed the following trends Plastic + plastic linear relationship between components Plastic + brittle dictated by plastic (lower yield pressure), slightly non linear Brittle + brittle dictated by higher yield pressure component (skeleton effect), non linear Kuentz & Leuenberger, 2000 Used percolation theory to explain deviations van Veen, 2000 Showed that two plastic materials can be very non linear if their densification and relaxation mechanisms are different Van Veen, 2004 Used intermediate data point to calculated tensile strength for non linear materials Michrafy, 2007 Tried additive and geometric mixing rules with Ryshkewitch Duckworth equation, no clear conclusion on which works better Etzler, 2011 Justified a binary geometric mixing rule weighted by volume fraction via analogy to van der Waals binary interactions Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 26
27 Tensile Strength Mixture Model Problem 1: Most drug compounds aren t very compressible, even in a simulator under slow compression. How is the pure drug tensile strength estimated? 3 Drug m n i1 i v i, i f ) ( i 1 2 Excipient 1 Excipient 2 Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 27
28 Tensile Strength Mixture Model Problem 1: Most drug compounds aren t very compressible, even in a simulator under slow compression. Solution: Measure tensile strength of a 50:50 drug-excipient ratio for each excipient. Then determine a pure drug tensile strength which fits the 50:50 data point. Take mean of estimates. 1 1,3 3 Drug i1 Excipient 1 Excipient 2 m 3 2,3 n i v i, i i i,3 v,1 1 v, D Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 28
29 Tensile Strength Mixture Model Problem 2: Different excipients may exhibit very different yield strength, i.e., different porosities at a given pressure. Would a single tablet relative density then be sufficiently indicative of the relative extent of deformation of each of the components? Solution:? We haven t investigated this yet! m i n i1 f ) ( i v i, i Relative Density STARCH 1500 & DCP COMPRESSIBILITY Starch DCP Compaction Pressure (MPa) Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 29
30 Ternary geometric mixing results Avicel DCP APAP Excipients varying 0 100%, APAP varying 0 50% Avicel Lactose APAP Mannitol Starch APAP Despite being somewhat simplistic, the geometric mixing model seems to work well enough to be useful! Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 30
31 Blend Uniformity
32 Example API particle size distribution Desired State: Design Space Paradigm The design space for a given set of process parameters/controls (e.g., API particle size distribution) is the intersection of all parameter spaces for the quality attributes they impact. QA: Dissolution QA: Content Uniformity Mechanism: random mixing QA: Content Uniformity Mechanism: Segregation Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 32
33 API particle size distribution In addition to flow and compaction, it directly impacts blend uniformity Quantitative understanding of this relationship can aid in defining particle size distribution ranges Early identification of workable range in particle size can reduce development effort and promote more collaboration between API development and drug product development Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 33
34 Example API particle size distribution USP 29 Content Uniformity Calculator USP 29 Content Uniformity Calculator Inputs Target potency 100 %LC Sample size 30 no. tablets Sample mean potency 98 %LC Sample RSD potency 3.6 % Confidence level 95 % Calculations Sample mean upper bound %LC Sample mean lower bound %LC Sample SD upper bound 4.74 %LC Upper Bound: Stage 1 Stage 2 I1: 63.19% 91.62% I2: 12.93% 3.52% I3: 14.73% 4.86% Probability of Passing 90.85% 99.99% Maximum probability 99.99% Lower Bound: Stage 1 Stage 2 I1: 4.81% 0.24% I2: 0.01% 0.00% I3: 64.67% 97.74% Probability of Passing 69.50% 97.90% Maximum probability 97.90% Statistical model based on USP <905> tells us what CU we need to achieve Excel spreadsheet statistical model Calculates the probability of passing at a given confidence level Based on Berghum s statistical analysis ` Lower bound probability % Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 34
35 Example API particle size distribution USP 29 Content Uniformity Calculator Probability of passing (or % success rate) for a given sample mean and sample RSD Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 35
36 Example API particle size distribution USP 29 Content Uniformity Calculator Sample std dev as a function of sample mean for a given passing probability or success rate Six-sigma criteria requires NMT 3% RSD Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 36
37 Example API particle size distribution Blend uniformity vs Content uniformity A content uniformity of 3% RSD seems to offer sufficient robustness You might expect a ~1% difference between blend and content uniformity for a well controlled drug product process (process scientist should confirm experimentally) So what kind of API particle size distribution gives us a 2% blend RSD? Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 37
38 Example API particle size distribution Blend uniformity RSD vs. drug loading, diameter RSD 100% d 6M 3 1 p p Not realistic Useful general guideline BU not really a concern past 5-10% DL Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 38
39 Example API particle size distribution Calculating blend uniformity from API size For a measured distribution (Stange Poole equation): RSD 100% fimi xm i x For a lognormal distribution (Rohrs et al, 2006): RSD 100% 6xM exp 4.5ln 10 g 1 2 g x M f i m i g g mass fraction of API in blend mass of drug product mass fraction of diameter i mass of a single particle in mass fraction f i geometric mean diameter geometric standard deviation Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 39
40 Example API particle size distribution Actual distributions vs. lognormal approximation Example from ABT-894 project Matched d(0.5) and d(0.9) Good fit to measured data with some discrepancy in the tailing g g 1 ( d d d(0.5) 0.9) (0.5) Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 40
41 Example API particle size distribution BU vs d(0.5), d(0.9) Reference Charts Charts are a design range with dimensions of d(0.5) vs. d(0.9) Don t depend on excipient particle size at low drug loading Offers more insight regarding API PSD, useful at early stage formulation Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 41
42 Example API particle size distribution Can also function as API Design Space Predicted Blend Uniformity vs d(0.5), d(0.9) Predicted BU correlated to actual measured CU API Predicted BU (RSD %) Fine Medium Coarse Measured CU (RSD %) Green known good Red - unknown? Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 42
43 Example API particle size distribution What about controlling de mixing? Design space is common overlap of individual design ranges! De mixing or segregation occurs with significant size/density differences between API and excipients Unfortunately no mechanistic models to predict it yet To control segregation, design space can be modified by putting lower limit on d(0.5). Empirical at this point. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 43
44 FEA
45 Finite Element Analysis: Drucker Prager Cap (DPC) Model Shear failure surface Cap surface (densification) The ability to perform DPC analysis on multi component blend estimates would add to the early modeling toolbox and provide additional early guidance on choice of tablet tooling design and choice of excipients/grades for investigation. Capable of predicting stress and density distribution in compacted tablet Applications in formulation development: Tooling design Identifying high stress risk zones for prediction of tablet defects Optimizing tablets for improved friability Garner, Ruiz, Strong, Zavaliangos (2014) Mechanism of crack formation in die compacted powders during unloading and ejection: An experimental and modeling comparison between standard straight and tapered dies, Powder Tech. 264: Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 45
46 Finite Element Analysis: Drucker Prager Cap (DPC) Model Die Compaction zz rr Describes the limit to elastic deformation One curve per relative density (denser=stronger) Described on a hydrostatic (p) and Mises (q) stress plane Calibrated easily by press simulator experiments Diametral and Simple Compression Strength Tests Two tests per density level Fully Instrumented Die Compaction Experiments Only one experiment required for all densities Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 46
47 DPC required inputs Defining the yield surface Cohesion d Diametral & Simple Friction angle β Compression Cap eccentricity R Die Compaction Hydrostatic yield stress p b Other inputs Elastic modulus Poisson ratio Can these inputs for a blend be estimated (well enough to be useful!) from individual Die Compaction components? Is this approach viable? Cunningham, Sinka, Zavaliangos (2004) Analysis of Tablet Compaction. I. Characterization of Mechanical Behavior of Powder and Powder/Tooling Friction, Powder Tech. 283: Garner, Strong, Zavaliangos (2015) The extrapolation of the Drucker Prager/Cap material parameters to low and high relative densities, Powder Tech. 283: Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 47
48 Tertiary Mixture: 33% MCC + 33% Lactose + 33% Mannitol Failure Surface Parameters Cohesion [MPa] Tertiary Mixture Mixture Model Friction Angle [degrees] Tertiary Mixture Mixture Model Relative Density Relative Density Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 48
49 Tertiary Mixture: 33% MCC + 33% Lactose + 33% Mannitol Cap Surface Parameters Cap Eccentricity Tertiary Mixture Mixture Model Relative Density Hydrostatic Yield Stress [MPa] Tertiary Mixture Mixture Model Relative Density Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 49
50 Elastic parameters Extraction of Elastic Parameters Axial Stress [MPa] Increasing RD Volumetric Strain Linear estimates of unloading slopes for axial and radial stress versus volumetric strain are taken from the beginning stage of unloading Poisson s Ratio 1 1 Young s Modulus 1 1 σ zz = Axial stress σ rr = Radial stress ε = Strain 12 Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 50
51 Elastic Properties from Mixtures Young's Poisson's Modulus Ratio [GPa] :1 Mixture Model :1 Mixture 1:1 Mixture 1:1 MCC:Lactose :1 MCC:Lactose :1 Mixture Model Relative Density Relative Density Young's Modulus [GPa] Poisson's Ratio 8 Tertiary Mixture Tertiary Mixture 7 Tertiary Mixture Mixture Model Tertiary Mixture Mixture Model Relative Density Relative Density Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 51
52 Finite element simulation setup Tooling was modeled as cylindrical flat face Axisymmetric geometry allowed for 2D simulation The mesh that represented powder material consisted of 3,000 4 node bilinear axisymmetric elements Material modeled as MCC and lactose mixtures Upper Punch Die Wa Equivalent Stress [MPa] Axis of Symmetry Powder Material Hydrostatic Stress [MPa] Lower Punch Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 52
53 Comparison of measured vs. modeled Relative Density Contours DPC Parameter Extraction from Measurement Comparison of Tertiary Mixture DPC Parameter Extraction from Mixture Model End of Unloading End of Unloading Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 53
54 Building on the blend model approach
55 Constructing an initial development space Acceptable tensile strength composition space Acceptable flowability composition space Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 55
56 Constructing an initial development space Acceptable tensile strength composition space Acceptable flowability composition space Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 56
57 Constructing an initial development space Can this multicomponent blend modeling be applied to more advanced modeling techniques? Acceptable flowability and tensile strength composition space Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 57
58 Final Notes & Conclusions Multi component blend modeling has the potential to help unload the early stages of development in regards to experimental effort, but material sparing measurements are a parallel approach. The first model it! approach is not easily or quickly implemented, requiring a comprehensive database of material properties to fully fuel the effort. Nor is it the complete solution. But once implemented, the approach would exemplify the QbD vision. Prediction of Blend Quality Parameters CPDG 09/22/2016 Copyright 2016 AbbVie 58
59
Formulation of Low Dose Medicines - Theory and Practice
Hashim Ahmed, Ph.D. and Navnit Shah, Ph.D. Pharmaceutical and Analytical R&D, Hoffmann-La Roche Inc., Nutley NJ Formulation of Low Dose Medicines - Theory and Practice Progress in pharmaceutical research
More informationChapter 7. Highlights:
Chapter 7 Highlights: 1. Understand the basic concepts of engineering stress and strain, yield strength, tensile strength, Young's(elastic) modulus, ductility, toughness, resilience, true stress and true
More informationAPPLICATION OF COMPACTION EQUATIONS FOR POWDERED PHARMACEUTICAL MATERIALS
APPLICATION OF COMPACTION EQUATIONS FOR POWDERED PHARMACEUTICAL MATERIALS Maroš ECKERT, Peter PECIAR, Alexander KROK,2, Roman FEKETE Institute of Process Engineering, Faculty of Mechanical Engineering,
More information3 Flow properties of bulk solids
3 Flow properties of bulk solids The flow properties of bulk solids depend on many parameters, e.g.: particle size distribution, particle shape, chemical composition of the particles, moisture, temperature.
More informationConstitutive model development for granular and porous materials and modelling of particulate processes
Constitutive model development for granular and porous materials and modelling of particulate processes Csaba Sinka ics4@le.ac.uk Department of Engineering Mechanics of Materials Group Geophysics modelling
More informationEnhancing Prediction Accuracy In Sift Theory
18 TH INTERNATIONAL CONFERENCE ON COMPOSITE MATERIALS Enhancing Prediction Accuracy In Sift Theory J. Wang 1 *, W. K. Chiu 1 Defence Science and Technology Organisation, Fishermans Bend, Australia, Department
More informationASAP concept and case studies
ASAP concept and case studies Sabine Thielges Stability Testing for Pharmaceuticals 20-21 March 2013 Background: Traditional Approaches to Stability Studies (ICH) 1. Long-term e.g. 25 C/60%RH, 30 C/75%RH
More informationQbD QUANTITATIVE MEASUREMENTS OF CQAS IN SOLID DOSAGE FORM UNIT OPERATIONS
QbD QUANTITATIVE MEASUREMENTS OF CQAS IN SOLID DOSAGE FORM UNIT OPERATIONS Particle Size Material Segregation-Flow USP USP USP Ph. Eur. 2.9.31 Ph. Eur. 2.9.36 JP 1 JP 18 Light Diffraction
More informationApplication of Ring Shear Testing to Optimize Pharmaceutical Formulation and Process Development of Solid Dosage Forms
ANNUAL TRANSACTIONS OF THE NORDIC RHEOLOGY SOCIETY, VOL. 21, 2013 Application of Ring Shear Testing to Optimize Pharmaceutical Formulation and Process Development of Solid Dosage Forms Søren V. Søgaard
More informationTowards an Improved Understanding of Strength and Anisotropy. of Cold Compacted Powder. A Thesis. Submitted to the Faculty.
Towards an Improved Understanding of Strength and Anisotropy of Cold Compacted Powder A Thesis Submitted to the Faculty of Drexel University by Wenhai Wang in partial fulfillment of the requirements for
More informationModifications to Johanson's roll compaction model for improved relative density predictions
Purdue University Purdue e-pubs Open Access Theses Theses and Dissertations 4-2016 Modifications to Johanson's roll compaction model for improved relative density predictions Yu Liu Purdue University Follow
More informationThe Frictional Regime
The Frictional Regime Processes in Structural Geology & Tectonics Ben van der Pluijm WW Norton+Authors, unless noted otherwise 1/25/2016 10:08 AM We Discuss The Frictional Regime Processes of Brittle Deformation
More informationThe Effect of Side Constraint in Rolling Compaction of Powders
The Effect of Side Constraint in Rolling Compaction of Powders Wenhai Wang 1, John C. Cunningham 2, and Antonios Zavaliangos 1 1 Drexel University, Department of Materials Science and Engineering, Philadelphia,
More informationExample-3. Title. Description. Cylindrical Hole in an Infinite Mohr-Coulomb Medium
Example-3 Title Cylindrical Hole in an Infinite Mohr-Coulomb Medium Description The problem concerns the determination of stresses and displacements for the case of a cylindrical hole in an infinite elasto-plastic
More informationSHEAR STRENGTH OF SOIL UNCONFINED COMPRESSION TEST
SHEAR STRENGTH OF SOIL DEFINITION The shear strength of the soil mass is the internal resistance per unit area that the soil mass can offer to resist failure and sliding along any plane inside it. INTRODUCTION
More informationTendency of blends for segregation
Tendency of blends for segregation How to study it? Methods for measuring the segregation potential Louk Peffer 1 Outline/Themes Blends unmix Theory or reality Visual inspection Segregation mechanisms
More informationA Digital Design Approach to Prediction of Powder Flowability
A Digital Design Approach to Prediction of Powder Flowability James Elliott, Xizhong Chen ( 陈锡忠 ) and Chunlei Pei ( 裴春雷 ) Macromolecular Materials Laboratory University of Cambridge ADDoPT Work Package
More information1.8 Unconfined Compression Test
1-49 1.8 Unconfined Compression Test - It gives a quick and simple measurement of the undrained strength of cohesive, undisturbed soil specimens. 1) Testing method i) Trimming a sample. Length-diameter
More informationLecture #2: Split Hopkinson Bar Systems
Lecture #2: Split Hopkinson Bar Systems by Dirk Mohr ETH Zurich, Department of Mechanical and Process Engineering, Chair of Computational Modeling of Materials in Manufacturing 2015 1 1 1 Uniaxial Compression
More informationPavement Design Where are We? By Dr. Mofreh F. Saleh
Pavement Design Where are We? By Dr. Mofreh F. Saleh Pavement Design Where are We?? State-of-Practice State-of-the-Art Empirical Mechanistic- Empirical Mechanistic Actual Current Practice?? Inputs Structure
More informationEFFECT OF STRAIN HARDENING ON ELASTIC-PLASTIC CONTACT BEHAVIOUR OF A SPHERE AGAINST A RIGID FLAT A FINITE ELEMENT STUDY
Proceedings of the International Conference on Mechanical Engineering 2009 (ICME2009) 26-28 December 2009, Dhaka, Bangladesh ICME09- EFFECT OF STRAIN HARDENING ON ELASTIC-PLASTIC CONTACT BEHAVIOUR OF A
More informationMeasuring the flow properties of powders. FT4 Powder Rheometer. freemantechnology
Measuring the flow properties of powders FT4 Powder Rheometer freemantechnology Efficiency, quality and productivity Successful powder processing requires the ability to reliably and repeatably predict
More informationFrontiers of Fracture Mechanics. Adhesion and Interfacial Fracture Contact Damage
Frontiers of Fracture Mechanics Adhesion and Interfacial Fracture Contact Damage Biology, Medicine & Dentistry The Next Frontiers For Mechanics One of the current challenges in materials & mechanics is
More informationAn Experimental Characterization of the Non-linear Rheology of Rock
An Experimental Characterization of the Non-linear Rheology of Rock G. N. BorrNoTr New England Research Inc. Contract: F49620-95-C-0019 Sponsor: AFOSR ABSTRACT A laboratory experimental program is underway
More information3-D Finite Element Analysis of Instrumented Indentation of Transversely Isotropic Materials
3-D Finite Element Analysis of Instrumented Indentation of Transversely Isotropic Materials Abstract: Talapady S. Bhat and T. A. Venkatesh Department of Material Science and Engineering Stony Brook University,
More informationSHEAR STRENGTH OF SOIL
Soil Failure Criteria SHEAR STRENGTH OF SOIL Knowledge about the shear strength of soil important for the analysis of: Bearing capacity of foundations, Slope stability, Lateral pressure on retaining structures,
More informationCard Variable MID RO E PR ECC QH0 FT FC. Type A8 F F F F F F F. Default none none none 0.2 AUTO 0.3 none none
Note: This is an extended description of MAT_273 input provided by Peter Grassl It contains additional guidance on the choice of input parameters beyond the description in the official LS-DYNA manual Last
More informationFinite element simulations of fretting contact systems
Computer Methods and Experimental Measurements for Surface Effects and Contact Mechanics VII 45 Finite element simulations of fretting contact systems G. Shi, D. Backman & N. Bellinger Structures and Materials
More informationTechnical brochure DuraLac H TABLETING DIRECT COMPRESSION ANHYDROUS LACTOSE
U A AC Technical brochure TABLETING DIRECT COMPRESSION ANHYDROUS LACTOSE MEGGLE anhydrous lactose grade for direct compression: General information Direct compression (DC) tablet manufacture is a popular
More informationUncertainty modelling using software FReET
Uncertainty modelling using software FReET D. Novak, M. Vorechovsky, R. Rusina Brno University of Technology Brno, Czech Republic 1/30 Outline Introduction Methods and main features Software FReET Selected
More informationFinite-Element Analysis of Stress Concentration in ASTM D 638 Tension Specimens
Monika G. Garrell, 1 Albert J. Shih, 2 Edgar Lara-Curzio, 3 and Ronald O. Scattergood 4 Journal of Testing and Evaluation, Vol. 31, No. 1 Paper ID JTE11402_311 Available online at: www.astm.org Finite-Element
More informationExperimental and theoretical characterization of Li 2 TiO 3 and Li 4 SiO 4 pebbles
Experimental and theoretical characterization of Li 2 TiO 3 and Li 4 SiO 4 s D. Aquaro 1 N. Zaccari ABSTRACT Dipartimento di Ingegneria Meccanica Nucleare e della Produzione University of Pisa (Italy)
More informationN = Shear stress / Shear strain
UNIT - I 1. What is meant by factor of safety? [A/M-15] It is the ratio between ultimate stress to the working stress. Factor of safety = Ultimate stress Permissible stress 2. Define Resilience. [A/M-15]
More informationEffect of Strain Hardening on Unloading of a Deformable Sphere Loaded against a Rigid Flat A Finite Element Study
Effect of Strain Hardening on Unloading of a Deformable Sphere Loaded against a Rigid Flat A Finite Element Study Biplab Chatterjee, Prasanta Sahoo 1 Department of Mechanical Engineering, Jadavpur University
More informationLAMINATION THEORY FOR THE STRENGTH OF FIBER COMPOSITE MATERIALS
XXII. LAMINATION THEORY FOR THE STRENGTH OF FIBER COMPOSITE MATERIALS Introduction The lamination theory for the elastic stiffness of fiber composite materials is the backbone of the entire field, it holds
More informationDiscrete Element Modelling of a Reinforced Concrete Structure
Discrete Element Modelling of a Reinforced Concrete Structure S. Hentz, L. Daudeville, F.-V. Donzé Laboratoire Sols, Solides, Structures, Domaine Universitaire, BP 38041 Grenoble Cedex 9 France sebastian.hentz@inpg.fr
More informationPrediction of the bilinear stress-strain curve of engineering material by nanoindentation test
Prediction of the bilinear stress-strain curve of engineering material by nanoindentation test T.S. Yang, T.H. Fang, C.T. Kawn, G.L. Ke, S.Y. Chang Institute of Mechanical & Electro-Mechanical Engineering,
More informationLecture #8: Ductile Fracture (Theory & Experiments)
Lecture #8: Ductile Fracture (Theory & Experiments) by Dirk Mohr ETH Zurich, Department of Mechanical and Process Engineering, Chair of Computational Modeling of Materials in Manufacturing 2015 1 1 1 Ductile
More informationTheory at a Glance (for IES, GATE, PSU)
1. Stress and Strain Theory at a Glance (for IES, GATE, PSU) 1.1 Stress () When a material is subjected to an external force, a resisting force is set up within the component. The internal resistance force
More informationStress-Strain Behavior
Stress-Strain Behavior 6.3 A specimen of aluminum having a rectangular cross section 10 mm 1.7 mm (0.4 in. 0.5 in.) is pulled in tension with 35,500 N (8000 lb f ) force, producing only elastic deformation.
More informationNumerical Modeling of Direct Shear Tests on Sandy Clay
Numerical Modeling of Direct Shear Tests on Sandy Clay R. Ziaie Moayed, S. Tamassoki, and E. Izadi Abstract Investigation of sandy clay behavior is important since urban development demands mean that sandy
More informationANSYS Mechanical Basic Structural Nonlinearities
Lecture 4 Rate Independent Plasticity ANSYS Mechanical Basic Structural Nonlinearities 1 Chapter Overview The following will be covered in this Chapter: A. Background Elasticity/Plasticity B. Yield Criteria
More informationRock Failure. Topics. Compressive Strength Rock Strength from Logs Polyaxial Strength Criteria Anisotropic Rock Strength Tensile Strength
Rock Failure Topics Compressive Strength Rock Strength from Logs Polyaxial Strength Criteria Anisotropic Rock Strength Tensile Strength Key Points 1. When rock fails in compression, the compressive stress
More informationME 2570 MECHANICS OF MATERIALS
ME 2570 MECHANICS OF MATERIALS Chapter III. Mechanical Properties of Materials 1 Tension and Compression Test The strength of a material depends on its ability to sustain a load without undue deformation
More informationMulti-Component Characterization of Strain Rate Sensitivity in Pharmaceutical Materials
Duquesne University Duquesne Scholarship Collection Electronic Theses and Dissertations 10-19-2015 Multi-Component Characterization of Strain Rate Sensitivity in Pharmaceutical Materials Jeffrey Michael
More informationChapter 7 Mixing and Granulation
Chapter 7 Mixing and Granulation 7.1 Mixing and Segregation (Chapter 9) Mixing vs. segregation (1) Types of Mixture * Perfect mixing Random mixing Segregating mixing Figure 9.1 (2) Segregation 1) Causes
More informationCOMPARISON OF SOME PHYSICAL PARAMETERS OF WHOLE AND SCORED LISINOPRIL AND LISINOPRIL/ HYDROCHLORTHIAZIDE TABLETS
& COMPARISON OF SOME PHYSICAL PARAMETERS OF WHOLE AND SCORED LISINOPRIL AND LISINOPRIL/ HYDROCHLORTHIAZIDE TABLETS Edina Vranić¹*, Alija Uzunović² ¹ Department of Pharmaceutical Technology, Faculty of
More informationSTANDARD SAMPLE. Reduced section " Diameter. Diameter. 2" Gauge length. Radius
MATERIAL PROPERTIES TENSILE MEASUREMENT F l l 0 A 0 F STANDARD SAMPLE Reduced section 2 " 1 4 0.505" Diameter 3 4 " Diameter 2" Gauge length 3 8 " Radius TYPICAL APPARATUS Load cell Extensometer Specimen
More informationPlasticity R. Chandramouli Associate Dean-Research SASTRA University, Thanjavur
Plasticity R. Chandramouli Associate Dean-Research SASTRA University, Thanjavur-613 401 Joint Initiative of IITs and IISc Funded by MHRD Page 1 of 9 Table of Contents 1. Plasticity:... 3 1.1 Plastic Deformation,
More informationA Constitutive Framework for the Numerical Analysis of Organic Soils and Directionally Dependent Materials
Dublin, October 2010 A Constitutive Framework for the Numerical Analysis of Organic Soils and Directionally Dependent Materials FracMan Technology Group Dr Mark Cottrell Presentation Outline Some Physical
More informationTesting and Analysis
Testing and Analysis Testing Elastomers for Hyperelastic Material Models in Finite Element Analysis 2.6 2.4 2.2 2.0 1.8 1.6 1.4 Biaxial Extension Simple Tension Figure 1, A Typical Final Data Set for Input
More informationSimulation of the cutting action of a single PDC cutter using DEM
Petroleum and Mineral Resources 143 Simulation of the cutting action of a single PDC cutter using DEM B. Joodi, M. Sarmadivaleh, V. Rasouli & A. Nabipour Department of Petroleum Engineering, Curtin University,
More informationModelling the behaviour of plastics for design under impact
Modelling the behaviour of plastics for design under impact G. Dean and L. Crocker MPP IAG Meeting 6 October 24 Land Rover door trim Loading stages and selected regions Project MPP7.9 Main tasks Tests
More informationStructural Analysis of Large Caliber Hybrid Ceramic/Steel Gun Barrels
Structural Analysis of Large Caliber Hybrid Ceramic/Steel Gun Barrels MS Thesis Jon DeLong Department of Mechanical Engineering Clemson University OUTLINE Merger of ceramics into the conventional steel
More informationThe plastic behaviour of silicon subjected to micro-indentation
JOURNAL OF MATERIALS SCIENCE 31 (1996) 5671-5676 The plastic behaviour of silicon subjected to micro-indentation L. ZHANG, M. MAHDI Centre for Advanced Materials Technology, Department of Mechanical and
More informationModule 5: Failure Criteria of Rock and Rock masses. Contents Hydrostatic compression Deviatoric compression
FAILURE CRITERIA OF ROCK AND ROCK MASSES Contents 5.1 Failure in rocks 5.1.1 Hydrostatic compression 5.1.2 Deviatoric compression 5.1.3 Effect of confining pressure 5.2 Failure modes in rocks 5.3 Complete
More information3D MATERIAL MODEL FOR EPS RESPONSE SIMULATION
3D MATERIAL MODEL FOR EPS RESPONSE SIMULATION A.E. Swart 1, W.T. van Bijsterveld 2, M. Duškov 3 and A. Scarpas 4 ABSTRACT In a country like the Netherlands, construction on weak and quite often wet soils
More informationNumerical Modelling of Blockwork Prisms Tested in Compression Using Finite Element Method with Interface Behaviour
13 th International Brick and Block Masonry Conference Amsterdam, July 4-7, 2004 Numerical Modelling of Blockwork Prisms Tested in Compression Using Finite Element Method with Interface Behaviour H. R.
More informationINTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 1, 2011
Interlaminar failure analysis of FRP cross ply laminate with elliptical cutout Venkateswara Rao.S 1, Sd. Abdul Kalam 1, Srilakshmi.S 1, Bala Krishna Murthy.V 2 1 Mechanical Engineering Department, P. V.
More informationStresses Analysis of Petroleum Pipe Finite Element under Internal Pressure
ISSN : 48-96, Vol. 6, Issue 8, ( Part -4 August 06, pp.3-38 RESEARCH ARTICLE Stresses Analysis of Petroleum Pipe Finite Element under Internal Pressure Dr.Ragbe.M.Abdusslam Eng. Khaled.S.Bagar ABSTRACT
More informationExamining the Soil Responses during the Initiation of a Flow Landslide by Coupled Numerical Simulations
The 2012 World Congress on Advances in Civil, Environmental, and Materials Research (ACEM 12) Seoul, Korea, August 26-30, 2012 Examining the Soil Responses during the Initiation of a Flow Landslide by
More informationModule-4. Mechanical Properties of Metals
Module-4 Mechanical Properties of Metals Contents ) Elastic deformation and Plastic deformation ) Interpretation of tensile stress-strain curves 3) Yielding under multi-axial stress, Yield criteria, Macroscopic
More informationUniversity of Sheffield The development of finite elements for 3D structural analysis in fire
The development of finite elements for 3D structural analysis in fire Chaoming Yu, I. W. Burgess, Z. Huang, R. J. Plank Department of Civil and Structural Engineering StiFF 05/09/2006 3D composite structures
More informationDYNAMIC ANALYSIS OF PILES IN SAND BASED ON SOIL-PILE INTERACTION
October 1-17,, Beijing, China DYNAMIC ANALYSIS OF PILES IN SAND BASED ON SOIL-PILE INTERACTION Mohammad M. Ahmadi 1 and Mahdi Ehsani 1 Assistant Professor, Dept. of Civil Engineering, Geotechnical Group,
More informationDESCRIBING THE PLASTIC DEFORMATION OF ALUMINUM SOFTBALL BATS
DESCRIBING THE PLASTIC DEFORMATION OF ALUMINUM SOFTBALL BATS E. BIESEN 1 AND L. V. SMITH 2 Washington State University, 201 Sloan, Spokane St, Pullman, WA 99164-2920 USA 1 E-mail: ebiesen@gonzaga.edu 2
More informationGeology 229 Engineering Geology. Lecture 5. Engineering Properties of Rocks (West, Ch. 6)
Geology 229 Engineering Geology Lecture 5 Engineering Properties of Rocks (West, Ch. 6) Common mechanic properties: Density; Elastic properties: - elastic modulii Outline of this Lecture 1. Uniaxial rock
More informationExercise: concepts from chapter 8
Reading: Fundamentals of Structural Geology, Ch 8 1) The following exercises explore elementary concepts associated with a linear elastic material that is isotropic and homogeneous with respect to elastic
More informationElectrostatics and cohesion: Cause or effect?
Electrostatics and cohesion: Cause or effect? Fernando Muzzio Rutgers University Department of Chemical and Biochemical Engineering, 98 Brett Road, Piscataway, NJ 08854 Powder Flow 2009 16 December ENGINEERING
More informationFinite Element Solutions for Geotechnical Engineering
Release Notes Release Date: July, 2015 Product Ver.: GTSNX 2015 (v2.1) Integrated Solver Optimized for the next generation 64-bit platform Finite Element Solutions for Geotechnical Engineering Enhancements
More informationPlane Strain Test for Metal Sheet Characterization
Plane Strain Test for Metal Sheet Characterization Paulo Flores 1, Felix Bonnet 2 and Anne-Marie Habraken 3 1 DIM, University of Concepción, Edmundo Larenas 270, Concepción, Chile 2 ENS - Cachan, Avenue
More informationDEMONSTRATING CAPABILITY TO COMPLY WITH A TEST PROCEDURE: THE CONTENT UNIFORMITY AND DISSOLUTION ACCEPTANCE LIMITS (CUDAL) APPROACH
1 DEMONSTRATING CAPABILITY TO COMPLY WITH A TEST PROCEDURE: THE CONTENT UNIFORMITY AND DISSOLUTION ACCEPTANCE LIMITS (CUDAL) APPROACH Jim Bergum September 12, 2011 Key Responses For Batch Release 2 Potency
More information8.1. What is meant by the shear strength of soils? Solution 8.1 Shear strength of a soil is its internal resistance to shearing stresses.
8.1. What is meant by the shear strength of soils? Solution 8.1 Shear strength of a soil is its internal resistance to shearing stresses. 8.2. Some soils show a peak shear strength. Why and what type(s)
More informationSupplementary Figures
Fracture Strength (GPa) Supplementary Figures a b 10 R=0.88 mm 1 0.1 Gordon et al Zhu et al Tang et al im et al 5 7 6 4 This work 5 50 500 Si Nanowire Diameter (nm) Supplementary Figure 1: (a) TEM image
More informationDurability of bonded aircraft structure. AMTAS Fall 2016 meeting October 27 th 2016 Seattle, WA
Durability of bonded aircraft structure AMTAS Fall 216 meeting October 27 th 216 Seattle, WA Durability of Bonded Aircraft Structure Motivation and Key Issues: Adhesive bonding is a key path towards reduced
More informationPharmaceutical Polymers for Tablets and Capsules
Pharmaceutical Polymers for Tablets and Capsules Edition: March 23, 2010 Wet Granulation Direct compression is not feasible for matrix formulations containing high levels of powder Carbopol polymers (>5%
More informationSimulation of Particulate Solids Processing Using Discrete Element Method Oleh Baran
Simulation of Particulate Solids Processing Using Discrete Element Method Oleh Baran Outline DEM overview DEM capabilities in STAR-CCM+ Particle types and injectors Contact physics Coupling to fluid flow
More informationMaterials for Pharmaceutical Manufacturing
Materials for Pharmaceutical Manufacturing GRACE WHITEPAPER FP Excipients 2-Step Mixing Process Improves API Stability, Flow, and Uniformity Technical Development: Dr. Raghunadha Gupta - Formulation Scientist,
More informationApplication of Three Dimensional Failure Criteria on High-Porosity Chalk
, 5-6 May 00, Trondheim, Norway Nordic Energy Research Programme Norwegian U. of Science and Technology Application of Three Dimensional Failure Criteria on High-Porosity Chalk Roar Egil Flatebø and Rasmus
More informationApplication of Discrete Element Method to Study Mechanical Behaviors of Ceramic Breeder Pebble Beds. Zhiyong An, Alice Ying, and Mohamed Abdou UCLA
Application of Discrete Element Method to Study Mechanical Behaviors of Ceramic Breeder Pebble Beds Zhiyong An, Alice Ying, and Mohamed Abdou UCLA Presented at CBBI-4 Petten, The Netherlands September
More informationLaboratory 4 Bending Test of Materials
Department of Materials and Metallurgical Engineering Bangladesh University of Engineering Technology, Dhaka MME 222 Materials Testing Sessional.50 Credits Laboratory 4 Bending Test of Materials. Objective
More informationDetermination of Mechanical Properties of Elastomers Using Instrumented Indentation
Determination of Mechanical Properties of Elastomers Using Instrumented Indentation, Antonios E. Giannakopoulos and Dimitrios Bourntenas University of Thessaly, Department of Civil Engineering, Volos 38334,
More informationIntroduction to Engineering Materials ENGR2000. Dr. Coates
Introduction to Engineering Materials ENGR2 Chapter 6: Mechanical Properties of Metals Dr. Coates 6.2 Concepts of Stress and Strain tension compression shear torsion Tension Tests The specimen is deformed
More informationUsing the Timoshenko Beam Bond Model: Example Problem
Using the Timoshenko Beam Bond Model: Example Problem Authors: Nick J. BROWN John P. MORRISSEY Jin Y. OOI School of Engineering, University of Edinburgh Jian-Fei CHEN School of Planning, Architecture and
More informationGeology 2112 Principles and Applications of Geophysical Methods WEEK 1. Lecture Notes Week 1
Lecture Notes Week 1 A Review of the basic properties and mechanics of materials Suggested Reading: Relevant sections from any basic physics or engineering text. Objectives: Review some basic properties
More informationBIO & PHARMA ANALYTICAL TECHNIQUES. Chapter 5 Particle Size Analysis
BIO & PHARMA ANALYTICAL TECHNIQUES Chapter 5 by Dr Siti Umairah Mokhtar Faculty of Engineering Technology umairah@ump.edu.my Chapter Description Aims Discuss theory, principles and application of analytical
More informationBehaviour of Blast-Induced Damaged Zone Around Underground Excavations in Hard Rock Mass Problem statement Objectives
Behaviour of Blast-Induced Damaged Zone Around Underground Excavations in Hard Rock Mass Problem statement Blast-induced damaged zone can affect the affect stability and performance of tunnel. But, we
More informationLecture #6: 3D Rate-independent Plasticity (cont.) Pressure-dependent plasticity
Lecture #6: 3D Rate-independent Plasticity (cont.) Pressure-dependent plasticity by Borja Erice and Dirk Mohr ETH Zurich, Department of Mechanical and Process Engineering, Chair of Computational Modeling
More informationInfluence of forced material in roller compactor parameters I.
1 Portál pre odborné publikovanie ISSN 1338-0087 Influence of forced material in roller compactor parameters I. Krok Alexander Elektrotechnika, Strojárstvo 24.08.2009 In the chemical, pharmaceutical and
More informationAn Energy Dissipative Constitutive Model for Multi-Surface Interfaces at Weld Defect Sites in Ultrasonic Consolidation
An Energy Dissipative Constitutive Model for Multi-Surface Interfaces at Weld Defect Sites in Ultrasonic Consolidation Nachiket Patil, Deepankar Pal and Brent E. Stucker Industrial Engineering, University
More informationEffect of embedment depth and stress anisotropy on expansion and contraction of cylindrical cavities
Effect of embedment depth and stress anisotropy on expansion and contraction of cylindrical cavities Hany El Naggar, Ph.D., P. Eng. and M. Hesham El Naggar, Ph.D., P. Eng. Department of Civil Engineering
More informationInfluence of Interparticle Forces on Powder Behaviour Martin Rhodes
Influence of Interparticle Forces on Powder Behaviour Martin Rhodes RSC Meeting Powder Flow 2018: Cohesive Powder Flow 12 April 2018 London Interparticle Forces Capillary Forces Due the presence of liquid
More informationNonlinear Finite Element Modeling of Nano- Indentation Group Members: Shuaifang Zhang, Kangning Su. ME 563: Nonlinear Finite Element Analysis.
ME 563: Nonlinear Finite Element Analysis Spring 2016 Nonlinear Finite Element Modeling of Nano- Indentation Group Members: Shuaifang Zhang, Kangning Su Department of Mechanical and Nuclear Engineering,
More informationSize Effects In the Crushing of Honeycomb Structures
45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference 19-22 April 2004, Palm Springs, California AIAA 2004-1640 Size Effects In the Crushing of Honeycomb Structures Erik C.
More informationElastic Properties of Solid Materials. Notes based on those by James Irvine at
Elastic Properties of Solid Materials Notes based on those by James Irvine at www.antonine-education.co.uk Key Words Density, Elastic, Plastic, Stress, Strain, Young modulus We study how materials behave
More informationFig. 1. Different locus of failure and crack trajectories observed in mode I testing of adhesively bonded double cantilever beam (DCB) specimens.
a). Cohesive Failure b). Interfacial Failure c). Oscillatory Failure d). Alternating Failure Fig. 1. Different locus of failure and crack trajectories observed in mode I testing of adhesively bonded double
More informationSwiss Medic Training Sampling
Swiss Medic Training Sampling Paul Sexton Sampling Preparation for Sampling Representative Sample Re-sampling Sampling Part I What to sample? Why sample? Where to sample? Who performs sampling? How to
More informationApplication of nanoindentation technique to extract properties of thin films through experimental and numerical analysis
Materials Science-Poland, Vol. 28, No. 3, 2010 Application of nanoindentation technique to extract properties of thin films through experimental and numerical analysis A. WYMYSŁOWSKI 1*, Ł. DOWHAŃ 1, O.
More informationAn Atomistic-based Cohesive Zone Model for Quasi-continua
An Atomistic-based Cohesive Zone Model for Quasi-continua By Xiaowei Zeng and Shaofan Li Department of Civil and Environmental Engineering, University of California, Berkeley, CA94720, USA Extended Abstract
More informationComputational models of diamond anvil cell compression
UDC 519.6 Computational models of diamond anvil cell compression A. I. Kondrat yev Independent Researcher, 5944 St. Alban Road, Pensacola, Florida 32503, USA Abstract. Diamond anvil cells (DAC) are extensively
More information