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# Applied Mechanics and Design

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DESIGN OF MACHINE MEMBERS – I

UNIT -I INTRODUCTION:
General considerations in the design of Engineering Materials and their properties ±selection ±Manufacturing consideration in design. Tolerances and fits -BIS codes of steels.
STRESSES IN MACHINE MEMBERS:
Simple stresses-Combined stresses- Torsional and bending stresses ± impact stresses ± stress strain relation -Various theories of failure - factor of safety - Design for strength and rigidity -preferred numbers. The concept of stiffness in tension, bending, torsion and combined situations - Static strength design based on fracture toughness.

UNIT –II  STRENGTH OF MACHINE ELEMENTS:
Stress concentration -Theoretical stress Concentration factor - Fatigue stress concentration factor notch sensitivity - Design for fluctuating stresses ± Endurance limit -Estimation of Endurance strength - Goodman's line -Soderberg's line - Modified goodman's line.

UNIT – III:
Riveted and welded joints - Design of joints with initial stresses - eccentric loading

UNIT – IV:
Bolted joints -Design of bolts with pre-stresses - Design of joints under eccentric loading ± locking devices ± both of uniform strength, different seals

UNIT –V KEYS, COTTERS AND KNUCKLE JOINTS:
Design of Keys-stresses in keys-cottered joints-spigot and socket, sleeve and cotter, jib and cotter joints-Knuckle joints.

UNIT –VI:
SHAFTS : Design of solid and hollow shafts for strength and rigidity -Design of shafts for combined bending and axial loads - Shaft sizes- BIS code. Use of internal and external circlips, Gaskets and seals(stationary & rotary).

UNIT - VII SHAFT COUPLING:
Rigid couplings - Muff, Split muff and Flange couplings. Flexible couplings -Flange coupling (Modified).

UNIT - VIII Mechanical Springs:
Stresses and deflections of helical springs -Extension -compression springs -Springs for fatigue loading-natural frequency of helical springs -Energy storage capacity -helical torsion springs -Co-axial springs, leaf springs.

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to "dial" by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker's utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to "dial" by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker's utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

DESIGN OF MACHINE MEMBERS – I

TEXT BOOKS :
1. Machine Design, V.Bandari Tmh Publishers
2. Machine Design, S MD Jalaludin, AnuRadha Publishers
3. Design Data hand Book, S MD Jalaludin, AnuRadha Publishers

REFERENCES :
1. Design of Machine Elements / V.M. Faires
2. Machine design / Schaum Series.
3. Machine design -Pandya & shah

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