In recent decades, functionally graded porous structures have been utilized due to their light weight and excellent energy absorption. They have various applications in the aerospace, biomedical, and engineering fields. Therefore, the balance between material strength and light weight is the goal of the researchers to decrease the cost. Samples of PLA material were designed and manufactured using a 3D printer according to international standard specifications to study the effect of porosity gradient through thickness. An experimental three-point bending test was performed, and then simulations were performed using ANSYS 2022 R1 software on samples with functionally gradient different porosity layers to verify the experimental results. The results from the experiment and the numerical values were in excellent alignment with an error rate of no more than 13%. The maximum bending load and maximum deflection of the beam were specified experimentally and compared with the numerical solution. The maximum bending and the maximum deflection When the porosity layer in the middle of the beam, matched the ideal maximum bending load (190,194) N experimentally and numerically, respectively. The maximum deflection (5.9,6.4) mm experimentally and numerically, respectively was obtained in samples with varying porous layers.
Productivity improvement in the manufacturing industry of piping is a key challenge facing manufacturers in today's competitive markets. Improving productivity in the pipe manufacturing companies by implementing manufacturing principles that utilize simulation modeling was the purpose of this study. To improve productivity, an approach that focuses on the workstations and workforces process was suggested. The suggested approach’s goal was to increase productivity by providing customer prerequisites and leaving some products for other customers in the store. Based on the data has been gathered from the company of steel pipes, Bansal Ispat Tubes Private Limited in India, a simulation model was utilized to enhance its performance of operational. The investigation methodology consists of a simulation model, acceptable distribution, and data investigation. By simulating individual workstations and evaluating all relevant processes according to the data collected, the simulation model was built. Actual employment data were gathered from the line of manufacturing and supervisory workers, with observations carried out throughout the process of manufacturing. The used method involves videotaping of the process and interviewing workers using a video-camera. The superior continuous distributions were picked to fulfill a convenient statistical model. The results could be helps to ameliorate the manufacturing industry productivity. Furthermore, the outcomes could assist to solve the problems of scheduling in pipe manufacturing "simulating and modeling" which reveals active ways in enhancing pipe manufacturing productivity. Consequently, the findings might support well competition among companies.