Component Characteristics

Components and enclosures are allocated an ingress protection (IP) number which denotes their protection against the ingress of foreign bodies or water as shown in Table 3.1. It is important to note that the IP rating is also affected by the method of fixing used, method of sealing and cable entries, not just the enclosure itself. 

A third digit relating to impact protection is occasionally used. This is defined in Table 3.2. 

0 No protection against accidental contact, no protection against intrusion of solid foreign bodies. 0 No protection against water. 

1 Protection against solid objects with diameter greater than 50 mm, hands or large tools for example 1 Protection against vertically falfing water drips. 

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Each product is derived from individual pieces of material, individual components and individual assembly processes. The properties of these individual elements have a probability of deviating from the ideal or target value. In turn, the designer defines allowable tolerances on component characteristics in anticipation of the manufacturing variations, but more often than not, with limited knowledge of the cost... 

The data used to generate the maps is taken from a simple statistical analysis of the manufacturing process and is based on an assumption that the result will follow a Normal distribution. A number of component characteristics (for example, a length or diameter) are measured and the achievable tolerance at different conformance levels is calculated. This is repeated at different characteristic sizes to build up a relationship between the characteristic dimension and achievable tolerance for the manufacture process. Both the material and geometry of the component to be manufactured are considered to be ideal, that is, the material properties are in specification, and there are no geometric features that create excessive variability or which are on the limit of processing feasibility. Standard practices should be used when manufacturing the test components and it is recommended that a number of different operators contribute to the results. 

Validation of the Component Manufacturing Variability Risk, is essential to CA in determining estimates for component characteristics at the design stage. Collecting component parts from various industrial sources with known statistical histories was central to this. The components were taken from a number... 

Figure 2.15(a) shows the relationship between and Cp for the component characteristics analysed. Note, there are six points at q = 9, Cp = 0. The correlation coefficient, r, between two sets of variables is a measure of the degree of (linear) association. A correlation coefficient of 1 indicates that the association is deterministic. A negative value indicates an inverse relationship. The data points have a correlation coefficient, r = —0.984. It is evident that the component manufacturing variability risks analysis is satisfactorily modelling the occurrence of manufacturing variability for the components tested. 

P = probability of a fault in the component/characteristic d = probability that the fault will escape detection... 

In the special case when d = 1 and of = 1, that is, when if a fault occurs it will not be detected and that failure is certain, occurrence equates directly to the probability of a fault. The probability of a fault in turn depends on the capability of the process used for the component/characteristic. 

A standard for the minimum acceptable process capability index for any component/characteristic is normally set at = 1.33, and this standard will be used later to align costs of failure estimates. If the characteristics follow a Normal distribution, Cp = 1.33 corresponds to a fault probability of ... 

On the other hand, consider the case of a secondary back-up (external to the system) for a component/characteristic of severity S = 1. If the designer assumes the back-up is designed for Cp = 1.33, then the analysis would reduce that for the internal back-up S = 9 case above. The acceptable design limits on Figure 2.21... 

Once the variability risks, and q, have been calculated, the link with the particular failure mode(s) from an FMEA for each critical characteristic is made. However, determining this link, if not already evident, can be the most subjective part of the analysis and should ideally be a team-based activity. There may be many component characteristics and failure modes in a product and the matrix must be used to methodically work through this part of the analysis. Past failure data on similar products may be useful in this respect, highlighting those areas of the product that are most affected by variation. Variation in fit, performance or service life is of particular interest since controlling these kinds of variation is most closely allied with quality and reliability (Nelson, 1996). 

The latter is, except for a couple of terms related to solvent reorganization, the Marcus equation. The central idea is that the activation energy can be decomposed into a component characteristic of the reaction type, the intrinsic activation energy, and a correction due to the reaction energy being different from zero. Similar reactions should have similar intrinsic activation energies, and the Marcus equation obeys both the BEP... 

Ultrafiltration of micellar solutions combines the high permeate flows commonly found in ultrafiltration systems with the possibility of removing molecules independent of their size, since micelles can specifically solubilize or bind low molecular weight components. Characteristics of this separation technique, known as micellar-enhanced ultrafiltration (MEUF), are that micelles bind specific compounds and subsequent ultrafiltration separates the surrounding aqueous phase from the micelles [70]. The pore size of the UF membrane must be chosen such, that the micelles are retained but the unbound components can pass the membrane freely. Alternatively, proteins such as BSA have been used in stead of micelles to obtain similar enan-tioselective aggregates [71]. 

Since early antiquity, spices and resins from animal and plant sources have been used extensively for perfumery and flavor purposes, and to a lesser extent for their observed or presumed preservative properties. Fragrance and flavor materials vary from highly complex mixtures to single chemicals. Their history began when people discovered that components characteristic of the aroma of natural products could be enriched by simple methods. Recipes for extraction with olive oil and for distillation have survived from pre-Christian times to this day. 

The complexity of the molecular mixture in various environments has severely limited analytical efforts to resolve DOM into its constituent molecules for structural determination (4). DOM consists of thousands to millions of compounds mixed as solutes in varying aqueous systems. Even if the structures could be determined at the molecular level, DOM chemistry could not be described as the sum of the characteristics of the individual components because of molecular interactions both within the DOM and with other aqueous species that modify component characteristics (4, 5). 

Doppler hroadening effects also have been used to separate back scattered lidar signals into molecular and aerosol components. Characteristics of some lidar systems are summarized in Table 2. 

The N(ls) spectrum from this surface obtained at an exit angle of 15° (Fig. 12) shows the high binding energy component characteristic of amines that have been protonated by the oxide hydroxyls. The low intensity of the N(ls) spectra obtained at steeper exit angles prevented accurate curve fitting of these spectra. 

Active Componeni Characteristics Inactive Component Characteristics Safety Evaluation Product Composition Material Specificai ions Analytical Methods Development Stability Reports... 

However, the complex is thermally unstable, and rapidly decomposes as its solution is warmed to RT. As measured by GC-MS, the volatile products of the reaction contain Et3SiCl and (Et3Si)20 as two major components, characteristic of heterolytic cleavage of the r 2-Si H bond. The (Et3Si)20 was presumably formed by the reaction of Si Et, with adventitious H20, and Et3SiCl was formed by attack of SiEt, on CD2C12 solvent. Pd black also forms, possibly due to methane elimination from an unobserved Pd methyl hydride complex and instability of the resulting Pd(0) species. 

This tunable source was used to investigate the transient absorption kinetics of hypericin in DMSO as a function of pump wavelength (Fig. 1.10). The startling result is that using pump wavelengths from 495 to 600 nm, the data can be fit globally by a sum of two exponentials, which except for two traces exhibits the 10-ps component characteristic of H-atom transfer. Fit results are compiled in Table 1.1. 

The glassy state of materials refers to a nonequilibrium, solid state, such as is typical of inorganic glasses, synthetic noncrystalline polymers and food components. Characteristics of the glassy state include transparency, solid appearance and brittleness (White and Cakebread 1966 Sperling 1992). In such systems, molecules have no ordered structure and the volume of the system is larger than that of crystalline systems with the same composition. These systems are often referred to as amorphous (i.e., disordered) solids (e.g., glass) or supercooled liquids (e.g., rubber, leather, syrup) (Slade and Levine 1991 Roos 1995 Slade and Levine 1995). 

Patterns of exposure can be described using models that combine abiotic ecosystem attributes, stressor properties, and ecological component characteristics. Model selection is based on the model s suitability for the ecosystem or component of interest, the availability of the requisite data, and the study objectives. Model choices range from simple, screening-level procedures that require a minimum of data to more sophisticated methods that describe processes in more detail but require a considerable amount of data. 

A strong EPR spectrum with a pattern of nine components, characteristic for this ion radical 116,117), has been observed by us 78, 78a) for the adsorbed perylene vapor, in accordance with the EPR spectrum of perylene adsorbed on silica-alumina from solutions 107,108). 

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