Tool steel play a vital role in modern manufacturing industries due to its excellent properties. AISI D3 is a cold work tool steel which possess high strength, more hardenability and good wear resistance properties. It has a wide variety of applications in automobile and tool and die making industries such as blanking and forming tools, high stressed cutting, deep drawing and press tools. The novel ways of machining these steels and finding out the optimum process parameters to yield good output is of practical importance in the field of research. This research work explores an attempt to identify the optimized process parameter combinations in end milling of AISI D3 steel to yield low surface roughness and maximum dish angle using trochoidal milling tool path, which is considered as a novelty in this study. 20 experimental trials based on face centered central composite design (CCD) of response surface methodology (RSM) were executed by varying the input process factors such as cutting speed, feed rate and trochoidal step. Analysis of variance (ANOVA) was adopted to study the significance of selected process parameters and its relative interactions on the performance measures. Desirability-based multiple objective optimization was performed and the mathematical models were developed for prediction purposes. The developed mathematical model was statistically significant with optimum conditions of cutting speed of 41m/min, feed rate of 120 mm/min and trochoidal step of 0.9 mm. It was also found that the deviation between the experimental and predicted values is 6.10% for surface roughness and 1.33% for dish angle, respectively.

Enhancing Impact Strength of Fused Deposition Modeling Built Parts using Polycarbonate Material

Growing awareness of electric vehicles insists on the necessity of Permanent Magnet Brushless Direct Current Motor (PMBLDCM) worldwide. This paper involves building a simulation model of the complete PMBLDCM drive system using Matlab/Simulink to control the speed and Torque. Using the Simulink model, speed parameters with various controllers of the drive system are studied and analyzed. The conventional PI controller is capable of controlling the speed of the PMBLDC motor. H1owever, it cannot give assurance of the stability of the motor throughout the load variation. In a speed loop, Improved Brain Emotional Learning Based Intelligent Controller (IBELBIC) is proposed in this paper. The proposed IBELBIC-based PMBLDC motor drive system with a rating of 3 phase, 30 V, 400 W, 3000 RPM is implemented using Spartan 3 Field Programmable Gate Array (FPGA) from Xilinx. This paper presents an IBELBIC design for enhancing the speed performance of the drive system at various set speeds and different load conditions of 1.2 Nm load, 0.6 Nm load, and no load. The proposed PMBLDCM drive system is realized with the help of a Very high speed integrated circuit Hardware Description Language (VHDL) programming algorithm of digital Pulse Width Modulation (PWM) generator topology. Various test cases are evaluated under different operating conditions to demonstrate the learning capability and the applicability of the proposed controller. In this paper, simulation validation is done using hardware setup, and experimental results are monitored on a computer using a customized program developed using LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench). Instead of a Data Acquisition (DAQ) card, the Virtual Instrument Software Architecture (VISA) tool of LabVIEW software is used in this work and results in cost minimization of the experimental setup.

The key challenge in the innovation of hybrid electric vehicles is the energy management control strategies to split the energy between the sources. The objective of adding an auxiliary source of power to vehicles is to minimize battery stress as well as energy consumption rate (ECR) of the main source and to increase the range of the vehicle. Based on Taguchi and grey relational analysis, the optimal control factors were determined for each of the desired performance factors. In this experimental design, control strategy, vehicle model, battery SOC, and UC SOC are considered as control factors, and ECR, range of the vehicle, and battery stress remain as vehicle performance factors. The confirmatory results show the minimal error between the initial set and the optimum factor to be 7.68 for ECR, 8.54 for range, and 5.49 for battery stress, respectively. The results also reveal the importance of fuzzy-based strategy on hybridization of energy storage systems in electric vehicles and also identified the most and the least influencing factors that affect the vehicle performance.