The effect of cyclic twist angle on mechanical properties for AISI 1038 medium carbon steel

A group of 11 specimens AISI 1038 Medium carbon steel alloy fabricated according to ASTM standard D790-02 torsion test were twisted cyclically one in positive another to negative angle in range of angles (0o-50o), step 5 degrees for each specimen. The data from torsion test device help to get actual torques and shear stresses, later the specimens tested the tensile test to figure out the effects of cyclic angle of twist on mechanical properties for AISI 1038 Medium carbon steel. The results showed a good agreement between the theoretical and actual data (torque, shear stress) for specimens with positive angle of twist by the percentage: 98%, 91%, 96%, 93%, 91%, 89%, 88%, 85%, 82%, 81%, 80%. In other side the results for experimental tests showed a dangerous decrements in mechanical properties for cyclic or negative twist angles, the yield stress for reference specimen without twist angle is 490 Mpa, yield stress increased for angels (5o,10o,15o) by 1%, 3%, 6%, then decreased for angels (20o,25o,30o,35o,40o,45o) by 3%, 5%, 13%, 18%, 24% and 35% Respectively and the final specimen with 50o angle of twist had been broken torsional before tensile test as a result specimens groups consequent of the extrusion – intrusion defects concomitant from twisting load

Effect of Steering Wheel Vibration on drivers Hands in a Two-Wheel Drivers Hand tractor

The paper presents research results of the vibration transmitted from the steering wheel of the tractor with a 2-wheel drive to the driver’s hands. The vibration measurements were carried out on the tractor randomly chosen from the collage of agriculture / university of Baghdad. Before testing the tractor was examined and adjusted following the producer’s recommendations. The vibration levels were measured during the operation tillage at idling and at full load .The field was 3١٫٧ m above level sea. Soil was treated at soil constant moisture (1٧-20 %) with depth of plowing (١٧ cm). During operation the weather temperature was measured (15 C) and humidity was ( 27 % ) The vibration level on the steering wheel was measured and analyzed .The frequency-weighted acceleration(RMS) , given in m/ sec2 , was calculated. The vibration total value was defined as the root-mean-square of the three component value

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an optimized hierarchical distributed k-medoid symmetric clustering algorithm for big data spatial query processing. To increase the k-medoid method’s efficiency and create more precise clusters, a hybrid approach combining the k-medoid and Chemical Reaction Optimization (CRO) techniques is presented. CRO is used in this approach to broaden the scope of the optimal medoid and improve clustering by obtaining more accurate data. The suggested paradigm solves the current technique’s issue of predicting the accurate clusters’ number. The suggested approach includes two phases: in the first phase, the local clusters are built using Apache Spark’s parallelism paradigm based on their portion of the whole dataset. In the second phase, the local clusters are merged to create condensed and reliable final clusters. The suggested approach condenses the data provided during aggregation and creates the ideal clusters’ number automatically based on the dataset’s structures. The suggested approach is robust and delivers high-quality results for spatial query analysis, as shown by experimental results. The proposed model reduces average query latency by 23%