Transmenu powered by JoomlArt.com - Mambo Joomla Professional Templates Club

• Machine elements (e.g. Rotating disk, Shafts/Axle, Couplings, and more)
• Drives/Transmission (e.g. Spur Gear drive, Belt drive, Cam drive and more)
• Bearings (e.g. Ball/roller bearing, Rotary Sliding bearing/bushing, and more)
• Actuators (e.g. Hydraulic/Pneumatic line actuator, Electric Motors)
• Fastener/Fixing elements (e.g. Screws, Pins, Press fit and more)

The MRS consists of the following main modules: Physical, material properties, geometric forms, sizes, surfaces, thermal treatment properties, environment properties, static and dynamic loads (forces, torque moments, revolving speed) and their related statistical distributions Mechanical Reliability modeling using component functional interrelations and failure models of individual components Stochastic dissipation simulator for all input parameters Procedures of time to failure calculation using theoretical and experimental dependences During mechanical system design, it is essential to provide critical failure information to determine the potential modes of failures (physical event/mechanism that gave rise to a failure e.g. corrosion/fatigue/wear) a product might encounter during its lifetime. When new products are being considered and designed, this knowledge and information is expanded upon to help designers extrapolate, based on similarity with existing products and the potential design tradeoffs. The MRS provides an apportionment library facility, which allows designers the ability to generate basic failure modes and failure causes for each mechanical component within the system. The characteristics of failure modes includes hidden, predictable, preventable and random. The cause ratio for any particular failure mode must be assessed according to designers experience or through a Field Failure Analysis (a systemic study of the nature of various modes of material failure).

Mechanical failure mode defines the manner (physical event/mechanism) by which a failure is observed. A screw can have several failure modes such as, Fatigue crack, Shearing etc. The failure cause is typically design defects, improper material, improper heat treatments or other processes that are the basic reason for failure or which initiate the physical process by which deterioration proceeds to failure. There are many different kinds of mechanical failures.
 

Mechanical Failures Typical mechanical failures may include: Ductile fracture - Failure that involves a significant amount of plastic deformation prior to fracture Brittle fracture- Failure without a significant amount of macroscopic plastic deformation prior to fracture. Fatigue failure- failure associated with slow crack growth due to changing stress states. Corrosion-fatigue failure- Failure due the combined actions of changing stress and corrosive environments. Stress-corrosion cracking- Failure in which a steady tensile stress leads to the initiation and propagation of fracture in a relatively mild chemical environment. Wear failure- Broad range of relatively complex, surface related damage phenomena. Liquid-erosion failure- Type of wear failure in which liquid is responsible for removal of material. Liquid-metal embitterment- Involves the material losing some degree of ductility below its yield strength due to its surface being wetted by a lower-melting point liquid metal. Creep and stress rupture failures. Failure due to continued strain growth under steady load.

Fatigue method used to predict life

The majority of structural failures accrue due to fluctuating or cyclic loads, for this reason, designers must address the implications of repeated loads, fluctuating loads, and rapidly applied loads. As a result, fatigue analysis has become an early driver in the product development processes of a growing number of companies. The MRS analyzes fatigue in three methodologies: Commonly referred to as total life S-N or nominal stress Crack initiation method; strain-life modeling (ε-N) Crack growth or damage tolerance analysis which is concerned with the number of cycles until fracture. The MRS provides also failure analysis for components where crack initiation or crack growth modeling are not appropriate (composites, welds, plastics, and other non-ferrous materials). Additional Fatigue factors used for MTBF prediction are: Environments: Air, Fresh water, Seawater. Surface finish: Polishing, Grinding, Fine turn, Coarse turn Surface treatment: Plastic deformation, Chemical/thermal treatment, Special thermal treatment

Fatigue crack growth

By applying fracture mechanics principles it is possible to predict the number of cycles spent in growing a crack to some specified length or to final failure. This method allows quantification of the relationships between material properties, stress level, the presence of crack-producing flaws, and crack propagation mechanisms.

The fatigue mechanism:

 Crack Initiation:
Crack originates at some point of high stress concentration such as a scratch, fillet, keyway, thread, dent, corner or micro flaw.
 Crack Propagation:
Phase 1 -Ductile and smooth propagation of crack
Phase 2- Crack reaches critical size and the crack propagation rate increases. The direction of the crack propagation may change to be perpendicular the stress direction.
 Failure:
Failure occurs rapidly when remaining cross section can no longer carry the load. Number of cycles to failure is Nf = Ni + Np where i is the crack initiation phase and p is the crack propagation phase.

Wear and failure prediction.

Wear is the erosion of material which interaction of surface(s) or bounding face(s) of a solid with the working environment results in the dimensional loss of the solid, with or without loss of material. Typical wear are:

• Surface fatigue
• Adhesive wear
• Abrasive wear
• Corrosive wear.
  
  Wear environment includes load type (unidirectional sliding, reciprocating, rolling, impact), speed, temperatures, counter bodies (solid, liquid, gas) and type of contact (single phase or multiphase: liquid plus solid particles plus gas bubbles). The volume loss gives a truer picture than weight loss particularly when comparing wear resistance properties of materials with large variations in density.
  Wear along with other aging processes, such as, fatigue, creep and fracture toughness cause progressive degradation of materials with time leading to failure of material at an advanced age. Under normal operating parameters, the property changes during usage normally occur in three different stages as follows:
  
• Stage 1, where rate of changes can be high.
• Stage 2, where a steady rate of aging process is maintained. The useful or working life is comprised mostly of the life span at this stage.
• Stage 3, where rapid rate of aging leads to early failure.

Corrosion - Most structural alloys corrode merely from exposure to moisture in the air, but the process can be strongly affected by exposure to certain substances. Corrosion can be concentrated locally to form a pit or crack, or it can extend across a wide area to produce general deterioration. The MRS provides built in corrosion deterioration models to encompass material reactions with its surroundings.

Choosing Distribution for a Random Input Variable 

 

The MRS uses unique analytical prediction and probabilistic method to analysis the reliability of each mechanical element within the system. In each machine element there are uncertain input parameters (e.g. geometry, material properties, loads, environment). The variations of these input parameters are defined as random input variables and are characterized by their distribution type (e.g. Exponential, Normal, etc) and by their distribution parameters (e.g. mean values, standard deviation).
Failure time for each mechanical element is calculated as random output parameter using Monte Carlo simulation as an influence time dependent failures modes e.g. wear, fatigue, corrosion, and creep.
Goodness of Fit is then used to selects the optimal (significance level is the greatest) distribution type and accordingly the distribution parameters and MTBF for each mechanical element.
 

Geometric Dimension and Tolerances 

The MRS allows designers to insert actual geometric dimension and tolerances. With the MRS actual dimensions of the manufactured parts would be somewhere within the manufacturing tolerances. In this case it is reasonable to use a uniform distribution, where the tolerance bounds provide the lower and upper limits of the distribution function. Sometimes the manufacturing process generates a skewed distribution; for example, one half of the tolerance band is more likely to be hit than the other half. This is often the case if missing half of the tolerance band means that rework is necessary, while falling outside the tolerance band on the other side would lead to the part being scrapped. In this case a Beta distribution is more appropriate. Often Normal distribution is used. The fact that the normal distribution has no bounds (it spans minus infinity to infinity), is theoretically a severe violation of the fact that geometrical extensions are described by finite positive numbers only. However, in practice this is irrelevant if the standard deviation is very small compared to the value of the geometric extension, as is typically true for geometric tolerances. Materials Data Very often the scatter of material data is described by a Gaussian distribution. In some cases the material strength of a part is governed by the "weakest-link-theory". The "weakest-link-theory" assumes that the entire part would fail whenever its weakest spot would fail. For material properties where the "weakest-link" assumptions are valid, then the Weibull distribution might be applicable.


Typical Distribution Functions

Exponential Distribution:
The exponential distribution is useful in cases where there is a physical reason that the probability density function is strictly decreasing as the random input variable value increases. The distribution is mostly used to describe time-related effects; for example, it describes the time between independent events occurring at a constant rate. It is therefore very popular in the area of systems reliability and lifetime-related systems reliability, and it can be used for the life distribution of non-redundant systems. Typically, it is used if the lifetime is not subjected to wear-out and the failure rate is constant with time. Wear-out is usually a dominant life-limiting factor for mechanical components, which would preclude the use of the exponential distribution for mechanical parts. However in cases where preventive maintenance exchanges parts before wear-out can occur, then the exponential distribution is still useful to describe the distribution of the time until exchanging the part is necessary.

Normal Distribution:
The Gaussian or normal distribution is a very fundamental and commonly used distribution for statistical matters. It is typically used to describe the scatter of the measurement data of many physical phenomena. Strictly speaking, every random variable follows a normal distribution if it is generated by a linear combination of a very large number of other random effects, regardless which distribution these random effects originally follow. The Gaussian distribution is also valid if the random variable is a linear combination of two or more other effects if those effects also follow a Gaussian distribution.

Lognormal Distribution:
The lognormal distribution is a basic and commonly used distribution. It is typically used to describe the scatter of the measurement data of physical phenomena, where the logarithm of the data would follow a normal distribution. The lognormal distribution is very suitable for phenomena that arise from the multiplication of a large number of error effects. It is also correct to use the lognormal distribution for a random variable that is the result of multiplying two or more random effects (if the effects that get multiplied are also lognormal distributed). It is often used for lifetime distributions; for example, the scatter of the strain amplitude of a cyclic loading that a material can endure until low-cycle-fatigue occurs is very often described by a lognormal distribution.

Uniform Distribution:
The uniform distribution is a very fundamental distribution for cases where no other information apart from a lower and an upper limit exists. It is very useful to describe geometric tolerances. It can also be used in cases where there is no evidence that any value of the random variable is more likely than any other within a certain interval. In this sense it can be used for cases where "lack of engineering knowledge" plays a role.

Weibull Distribution:
In engineering, the Weibull distribution is most often used for strength or strength-related lifetime parameters, and it is the standard distribution for material strength and lifetime parameters for very brittle materials (for these very brittle material the "weakest-link-theory" is applicable).
 

Key Features

• Fast Analytical prediction of MTBF for each type of mechanical component.
• Wide range of machine elements, drives/transmissions, bearings, fasteners and actuators – enables faster creation of various mechanical assemblies for further reliability prediction
• Wide range of distribution enabling description of real world load conditions and material properties
• Link element enables: automatic transmission of kinematics and load parameters: Torque moments, Speeds and Forces between connected components – resulting in reduced time-for-input data and user mistakes
• Calculate MTBF and failure distribution for customized component
• Large Component material properties library
• Automated generated basic failure modes and failure causes for each mechanical component to be used in FMEA/FMECA, FTA, RBD analyses
• Enables export of Reliability distribution for automatic Preventive Maintenance Optimization using SAMO.
• Coupled Motion, Structure, Creep, Thermal, Wear, and Corrosion Fatigue analysis with new developed analytical equations