The optimal control of assembly deviation for large thin-walled structures based on basic deviation patterns

The deviation vector at arbitrary location of large thin-walled structure caused by manufacturing process is different and has the characteristic of field distribution, which has great influence on the assemble quality. The deviation of each point on the part is not independent, and the final assembly deviation is difficult to be controlled. In this paper, the deviation field of large thin-walled structure is described by the linear combination of a series of basic deviation patterns. The deviation propagation model is established to quantify the contribution of basic deviation patterns between parts and assembly. A new two-step optimization method based on the adjustment of key control points of the part is proposed for the deviation control of large thin-walled structures. Firstly, the effective independent method is employed to obtain the optimal measurement points, which may characterize all basic deviation patterns of the part accurately. Then a new optimization model is developed to determine the key control points for special basic deviation pattern, which have little influence on the other basic deviation patterns. Based on the genetic optimization algorithm, the optimal key control points and the adjusted quantities for special basic deviation pattern are obtained, simultaneously. A case study on the assembly process of two cylindrical thin-walled parts with initial deviations measured by the Laser Scan Device is conducted. The basic deviation pattern with great influence on the deviation of assembly is determined firstly. The key control points and the corresponding adjusted quantities for this basic deviation pattern are calculated. The results indicate that the deviation of the assembled structure may be suppressed by the adjusted deformation of the key control points of parts. It is useful on the deviation control for the assembly process of large thin-walled structures.

CORRELATION BETWEEN STRESS AND TEMPOROMANDIBULAR DISORDER IN ORPHANED ADOLESCENT IN BANJARMASIN

ABSTRACTBackground: Temporomandibular disorder is a temporomandibular joint disturbance characterized with the pain in masticatory muscle and TMJ; clicking in TMJ; and deflection, deviation pattern with limitation in opening of the mouth. TMD can be found in adolescents with prevalence and severity increased along with the age. TMD in adolescent was caused by the increased of the masticatory muscle activity. In adolescent, it was caused by the increase of the emotional state especially stress. Stress is a condition that often experienced by every people including orphaned adolescent, who has more problems than the other adolescent. Purpose: The aim of this study was to analyze the correlation between stress and temporomandibular disorder in orphaned adolescent in Banjarmasin. Method: This study is an analytical observational research with cross sectional design. The study was involving 13-18 years old orphaned adolescents in Banjarmasin with purposive sampling. This study is using PSS for stress examination and RDC/TMD for TMD examination. The correlation between stress and TMD were analyzed with Spearman analysis test. Results: This research showed that 61% of the subject had moderate stress level and TMD was found in 68.3% of the subject, which consists of 58.5% had myofascial pain and 9.8% had disc displacement. Spearman analysis test showed that there is statistical correlation between stress and temporomandibular disorder in orphaned adolescent in Banjarmasin (p<0.05). Conclusion: There is a correlation between stress and temporomandibular disorder in orphaned adolescent in Banjarmasin. Keywords: Myofascial Pain, Stress, Temporomandibular disorder (TMD).

Development of a computerized method for masticatory pattern assessment

Objective: To develop a computerized method to assess the mandibular lateral deviation pattern during mastication. Method: 44 videos of masticatory processes were analysed using the algorithm developed. The individuals were instructed to perform a specific pattern of mastication: only on the left or the right side (group 1), alternating five bites on one side and two on the opposite side (group 2), alternating 3 bites on each side (group 3). The computerized method identified, frame by frame, the lateral displacement of the chin and determined the amplitude and the percentage of mandibular lateral deviation to each side. Results: The groups 1 and 2 showed significantly higher number of cycles on the side of chewing compared to the opposite side and there was no difference between two sides in group 3. The amplitude of cycles showed similar results. In groups 1 and 2, the method identified the preferred chewing side, however, the percentage of the identified cycles in the chewing side was significantly lower than the percentage of cycles required (p <0.001). Conclusion: The proposed computerized method was effective in identifying the bilateral masticatory pattern and recognizing the existence of preference to use one of the sides during the masticatory cycles.

Industrial Gas Turbine Diagnostics Using Fuzzy Logic

In this work a fuzzy based fault diagnosis system for an industrial gas turbine engine is presented. The system compares measured parameters and those calculated by a computer model, which sets the new and clean reference of the equipment. A fuzzy system classifies and quantifies the faults, which includes part load operation and takes into consideration the compressor variable geometry, which implies in a modification of the deviation pattern between the reference and the model. A commercial software, the GSP (Gas turbine Simulation Program), has been used for the process simulation. The reference software used is called NGGT (Natural Gas and Gas Turbine). Both were calibrated using a real industrial gas turbine operation data. Two compressor faults were simulated using GSP, the resulting variables were compared with those generated by NGGT. The fuzzy sets of the entry variables and the inference rules implemented in the fuzzy systems are dedicated to each fault. Thus, it is possible to add new faults and correct existing fault patterns without disturbing the other faults implemented. The system was tested for the two implemented faults by generating faulty test cases to several geometries and levels of damage. The results showed the robustness of the system and that it is possible to clearly identify the faults, for the several cases tested.

Tail Deviation’s Predictive Control of the Tandem Rolling Strip Based on Manifold Learning

In the rolling process, serious deviation will cause product quality drop and rolling equipment fault. This reserch propose tail deviation’s predictive control method of the tandem rolling strip based on manifold learning. Based on real deviation data in the rolling production site,tail deviation patterns are divided according to deviation’s value. Using manifold learning method to deviation data in middle rolling stage , tail deviation pattern and scope are obtained. According to regression model between the control variable and deviation, predictive control strategy of the tandem rolling strip may be implemented. Experiment shows this method may control tail deviation in preconcerted permission range.

Chaos Character of Deviation Signal and Prediction of Tail Deviation in Strip's Rolling

In strip tandem rolling process, there is always deviation between the center line of the strip and the assumed center line, which is called running deviation. In this thesis, based on the actual running deviation data of some aluminum strip rolling industry, we analyzed their chaos and fractal character, proposed one improved pattern classification based on the fuzzy c-average clustering, on basis of this method classified running deviation pattern, constructed adding weight zero-order local prediction method of strip truning deviation signal. After the s actual data test, the forecasting result achieves the desired accuracy.