The similarity between communicating about an object or product through a dimensioned drawing and a written description is that they are both used to describe it, and they can both be descriptive. The main difference is that a drawing is able to show a picture of it.
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The similarity between communicating about an object or product through a dimensioned drawing and a written description is that they are both used to describe it, and they can both be descriptive. The main difference is that a drawing is able to show a picture of it.
the radius
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Limit, Bilateral, and Unilateral
Lines that extend off the object to show what is being dimensioned
It show dimension of given dig. Ex. length meter etc. > chain dimensioning as opposed to datum dimensioning. chain dimensioning: 4 holes in line on a drawing, first hole position dimensioned from the edge, remainder dimensioned from the centre of the previous hole. datum dimensioning: all holes dimensioned from the edge. the cumulative tolerance build up on the chain dimensioning allows for greater overall tolerance build up.
It is essential for engineers to ensure that drawings are fully dimensioned to provide clear and precise specifications for manufacturing and construction. Fully dimensioned drawings eliminate ambiguity, reduce the risk of errors, and ensure that all components fit together correctly. This clarity is crucial for effective communication among team members and stakeholders, ultimately leading to successful project execution and adherence to safety and quality standards.
Incorrect sketching could result an incorrect final product.
Seems to be a joke question...Of course properly and well dimensioned insulatedwires
its important because i dont know
Four-dimensional data often includes time as the fourth dimension, such as in weather modeling, where three spatial dimensions (latitude, longitude, altitude) are combined with time. Multi-dimensional data extends beyond four dimensions, commonly seen in complex datasets like customer behavior analysis, where variables like age, income, purchase history, and geographical location are analyzed simultaneously. High-dimensional data is prevalent in fields like genomics, where each gene represents a dimension, resulting in datasets with thousands of dimensions that can complicate analyses and require specialized techniques for interpretation.