Lean-and-Green Strength Performance Optimization of a Tube-to-Tubesheet Joint for a Shell-and-Tube Heat Exchanger Using Taguchi Methods and Random Forests

Abstract

The failing tube-to-tubesheet joint is identified as a primary quality defect in the fabrication of a shell-and-tube heat exchanger. Operating in conditions of high pressure and temperature, a shell-and-tube heat exchanger may be susceptible to leakage around faulty joints. Owing to the ongoing low performance of the adjacent tube-to-tubesheet expansion, the heat exchanger eventually experiences malfunction. A quality improvement study on the assembly process is necessary in order to delve into the tight-fitting of the tube-to-tubesheet joint. We present a non-linear screening and optimization study of the tight-fitting process of P215NL (EN 10216-4) tube samples on P265GH (EN 10028-2) tubesheet specimens. A saturated fractional factorial scheme was implemented to screen and optimize the tube-to-tubesheet expanded-joint performance by examining the four controlling factors: (1) the clearance, (2) the number of grooves, (3) the groove depth, and (4) the tube wall thickness reduction. The adopted ‘green’ experimental tactic required duplicated tube-push-out test trials to form the ‘lean’ joint strength response dataset. Analysis of variance (ANOVA) and regression analysis were subsequently employed in implementing the Taguchi approach to accomplish the multifactorial non-linear screening classification and the optimal setting adjustment of the four investigated controlling factors. It was found that the tube-wall thickness reduction had the highest influence on joint strength (55.17%) and was followed in the screening hierarchy by the number of grooves (at 30.47%). The groove depth (at 7.20%) and the clearance (at 6.84%) were rather weaker contributors, in spite of being evaluated to be statistically significant. A confirmation run showed that the optimal joint strength prediction was adequately estimated. Besides exploring the factorial hierarchy with statistical methods, an algorithmic (Random Forest) approach agreed with the leading effects line-up (the tube wall thickness and the number of grooves) and offered an improved overall prediction for the confirmation-run test dataset.