A study of thread tensions on a lockstitch sewing machine was made, measuring simultaneously both the needle and bobbin thread tensions. Experiments were carried out under specified sewing conditions, whereby needle and bobbin thread tensioner adjustment, sewing speed, number of plies, fabric quality and sewing thread quality were varied in order to investigate the effect of these factors on the needle and bobbin thread tensions. The patterns of the thread tension traces obtained were analysed as well as the effect of these factors on the peak tensions detected on both thread tension traces. Four significant peak tensions on the needle thread tension trace and two significant peak tensions on the bobbin thread tension trace were detected, during a stitch cycle. It was found that no significant variations occurred as far as the timing and shape of the peak tensions were concerned. However, variations were detected in the peak tensions according to the sewing conditions, as expected. From the analysis of the data obtained, multiple regression equations were derived to predict, with a good degree of accuracy, the peak tensions generated, according to the sewing conditions.

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We combine uniformisation, a powerful numerical technique for the analysis of continuous time Markov chains, with the Markov chain embedding technique to analyze GI/M/s/c queues.The main steps of the proposed approach are the computation of (1) the mixed-Poisson probabilities associated to the number of arrival epochs in the uniformising Poisson process between consecutive customer arrivals to the system; and (2) the conditional embedded uniformised transition probabilities of the number of customers in the queueing system immediately before customer arrivals to the system.To show the performance of the approach, we analyze queues with Pareto interarrival times using a stable recursion for the associated mixed-Poisson probabilities whose computation time is linear in the number of computed coefficients. The results for queues with Pareto interarrival times are compared with those obtained for queues with other interarrival time distributions, including exponential, Erlang, uniform and deterministic interarrival times. The obtained results show that much higher loss probabilities and mean waiting times in queue may be obtained for queues with Pareto interarrival times than for queues with the other mentioned interarrival time distributions, specially for small traffic intensities. (~)