Enhanced tensile properties of a reversion annealed 6.5Mn-TRIP alloy via tailoring initial microstructure and cold rolling reduction

The feasibility of improving the overall performance of medium Mn steels was demonstrated via tailoring the initial microstructure and cold rolling reduction. The combined effects of cooling patterns after hot rolling （HR） and cold rolling （CR） reductions show： （1） as the cooling pattern varied from furnace cooling （FC） to oil quenching （OQ）, the intercritically annealed microstructure was dramatically refined and the fraction of recrystallized ferrite dropped, regardless of CR reductions. This resulted in both high yield/ultimate tensile strengths （YS/UTS） but low total elongation to fracture （El）; （2） as the CR reduction increased from 50% to 75%, the OQ-samples after annealing exhibited a more refined microstructure with relatively higher fractions of retained austenite and sub-structure, leading to higher YS and UTS but lower El; whereas the FC samples appeared to exhibit little difference in overall tensile properties in both cases. The differences in microstructural evolution with cooling patterns and CR reductions were explained by the calculated accumulated effective strain （eAES）, which was considered to be related to degrees of recovery and recrystallization of the deformed martensite （α＇）. The optimal tensile properties of -1 GPa YS and -40 GPa.% UTSxEI were achieved in the OQ-50%CR annealed samples at 650 ℃ for 1 h. This was quite beneficial to large-scale production of ultra-high strength steels, owing to its serious springback during heaw cold working.

Establishment of a Fast Quality Assurance Method for Three-dimensional Afterloading Treatment Plan

Objective:To study the correlation between tumor size,radiation source intensity,prescription dose,and source dwell time in afterloading treatment plan,and to establish a rapid quality control method for afterloading treatment plan.Methods:A total of 181 patients with gynecological tumor were enrolled in our hospital.A total of 84 patients were installed with three tubes of Fletcher'applicator,58 patients with single uterine tube and 39 patients with vaginal applicator.Each patient was scanned with CT before treatment,and the target area and organs were delineated by doctors.The treatment plan was optimized by IPSA.The planned source intensity,prescription dose,source residence time and tumor volume of each case were recorded and the CI,RV,and k value were calculated,The CI distribution characteristics and the relationship with RV value were analyzed.In addition,46 cases of gynecological tumor patients'afterloading plan used this method for quality control verification.Results:The CI of the three kinds of applicators was normal distribution.The average Ci of Fletcher applicator was 0.720±0.067,k=1394,r=0.894,the average CI of Fletcher applicator was 0.697±0.076,k=1428,r=0.940,the average CI of vaginal applicator was 0.742±0.067,k=1362,r=0.909.Conclusion:Using this method,we could quickly evaluate the target volume,radiation source intensity,prescription dose and treatment time,to determine the cause of deviation according to the feedback results,ensuring that the afterloading treatment plan can be implemented efficiently quickly,and accurately in accordance with the clinical requirements.

Predicting Total Dwell Time of IPSA Plan Based on Machine Learning and Dose Calculation Models

Objective To establish two models based on machine learning and dose calculation algorithm that can be used for the prediction of the total dwell time and rapid quality control of brachytherapy plans.Methods A total of 1042 cases of treated gynecologic oncology patients were selected,of which 512 were used as training data to establish the model and the rest were used as test data.Each treatment plan optimized by inverse planning simulated annealing with all three catheters of the Fletcher applicator.The source strength Sk,prescription dose D,source dwell time t,and tumor volume V were recorded for each case.RV was defined as Sk·t/D.In accordance with the prescription dosage calculation formula in the planning system and machine learning method,the following equations were established:RV=kV2/3 and RV=a+b · V+c·V2.The R2 correlation coefficient represents the accuracy of the results.Result The dose calculation algorithm-based model is RV=1272×V2/3,R2=0.959,whereas the machine learning-based model is RV=258.8× V-0.359× V2+5110,R2=0.961.The treatment time prediction of the two models,each having 13 and 15 cases,respectively,has an error rate of more than10%,and the dose calculation algorithm-based method is more accurate.Conclusion The treatment time can be quickly predicted according to the planning target volume,and the two prediction models can be used as a way of quality control.