Browse Topic: Bolts

Items (2,386)
Predicting the fatigue life of threaded bolts is crucial in aerospace and mechanical assemblies where cyclic loading can cause early joint failure. Existing studies, like [1], have created S-N curves for high-strength bolts under different pretension and temperature conditions through experimentation. However, there are few numerical methods that can replicate these results, especially for bolts without pretension. This study develops and validates a finite element analysis (FEA) methodology to predict the fatigue performance of pretensioned threaded bolts under axial loading, using the experimentally derived Series-2 S-N data for M20 high-strength bolts with pretension. The approach employs a detailed 3D solid model with explicit thread geometry and a two-step transient structural analysis. This first simulates the bolt tightening process to establish a realistic preload, followed by the application of a service tensile load. Local stress distributions are analyzed to extract peak
K R, LesanthS, Suhail AhmedC, ArunvetrivelP, KrishnakumarP S, PremkumarVasantharaj, C
E-25 General Standards for Aerospace and Propulsion Systems
E-25 General Standards for Aerospace and Propulsion Systems
The main purpose of this study is to develop and validate an accurate calculation model for a hydraulic damper piston valve joint, enabling reliable torque specification and clamp behavior without full prototype iteration. Joint stiffness is a primary interest point. The joint features a bolted interface with a laminated shim stack of many thin disks with varying outer diameters. Analysis of such joints are uncommon in literature, making it challenging to quantify the effects of load distribution, truncation, and surface contact effects between members. The proposed models discussed in this paper are based on frustum load distribution combined with annular-plate bending and elastic-foundation effects to capture the effects of washer cupping. Concrete outputs of the calculator include member load distribution, bolt and member stiffnesses, torque-to-preload relationships, and an external-load simulation that predicts when individual members lose clamp load. Detailed internal hydraulic
Dresen, GabrielVollmar, RaceRoy Chowdhury, Sourav
Automotive seat system is one of the most complex systems in vehicle for its technical and functional requirements. Seat is designed to meet all regulatory requirements subjecting it to multiple tests with loading patterns which caters to the occupant safety. Varied loading and load path for different test requirements cause seat bolts to experience tensile, compressive, bending moments and shear loading. Shearing along bolt length is one of the common failure modes observed during design validation by physical tests. In the world of CAE, there is an industry approach to find the bolt failures at nut and head for all kind of loads. But shear failures along varied bolt lengths are not accurately predictable as multiple sheet metal parts will transfer loads unevenly onto bolt length and it becomes challenge to find which component is leading to shear failure. Hence by adding multiple rupture layers across the bolt length shear and its location could be predicted. Further, to resolve the
RJ, JethendraChiu, Li-Ban
E-25 General Standards for Aerospace and Propulsion Systems
The application of AI/ML techniques to predict truck endgate bolt loosening represents a major innovation for the automotive industry, aligning with the principles of Industry 4.0. Traditional physical testing methods are both expensive and time-consuming, often identifying issues late in the development process and necessitating costly design changes and prototype builds. By harnessing AI/ML, manufacturers can now analyze endgate slam and bolt preload data to accurately forecast potential bolt loosening issues. This predictive capability not only enhances quality and safety standards but also significantly reduces the costs associated with tooling and builds. The AI/ML tool described in this paper can simulate a variety of load conditions and predict bolt loosening with over 90% accuracy, considering factors such as changes in loads, bolt diameters, washer sizes, and unexpected masses added to the endgate. It provides valuable design insights, such as recommending optimal bolt
Sivakrishna, MasaniDas, MahatSingh, AbhinavKarra, ManasaShienh, GurpreetLuebke, Amy
In modern automotive manufacturing, ensuring the integrity of suspension joints under real-world driving conditions is a critical aspect of vehicle safety and performance. These joints endure substantial transverse loads and large vibrations due to irregular road surfaces, dynamic maneuvers, and varying environmental factors. As a result, bolt loosening becomes a significant concern, compromising joint integrity and overall vehicle reliability. This paper delves into the challenges associated with maintaining joint integrity, specifically focusing on pre-load determination, torque application, and production-related issues. The pre-load generated during torquing is the primary factor that ensures a suspension joint remains securely fastened under dynamic road conditions. This pre-load is derived using road load data acquisition (RLDA) inputs, which capture the forces acting on the joint during actual driving scenarios. RLDA inputs provide critical insights into the forces experienced
Kumar, SabeeshVasant Kumar, Jesse DanielMishra, HarshitSenthil Raja, TNayak, BhargavM, SudhanNamani, PrasadVibhute, Shekhar
This specification defines the requirements for A286 CRES T-bolts and eye bolts, with room temperature tensile strength of a minimum of 160000 psi, for use with clamps and V-band couplings at 1000 °F maximum ambient temperature.
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
Items per page:
1 – 50 of 2386