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Go to Editorial ManagerManual handling of semi-knockdown vehicles in assembly plants is unsafe, time-consuming, inefficient, and prone to quality irregularities. To intervene in addressing these problems, this study develops a prototype of an automated load carrier intelligent navigator. The work centre is analysed for space, material type and handling requirements. This is followed by design and testing, whereby software, hardware and mechanical engineering are integrated in the context of process optimisation. The prototype was tested on rough and smooth surfaces, for no-obstacle and obstacle avoidance conditions. On rough and smooth surfaces with no obstacles, the minimum distance considered is 0.5m, and the average speed and time determined are 0.08m/s and 6.23s, 0.17m/s and 2.97s, respectively. For the maximum distance of 3.0m, the average speeds and times determined are 0.081 m/s and 37.42s, and 0.18 m/s and 17.35s, respectively. The average distance considered for both rough and smooth surfaces is 1.75 m, and the average speed and time at each scenario are 0.081 m/s, 21.78s, and 0.17 m/s, 10.26s. The voltage of the battery drops, with a corresponding decrease in the speed of the motors. The automated carrier prototype makes the best decisions when it encounters an obstacle, giving the best outputs. This paper contributes by providing real-time intelligent navigation data and accurate regulation of the automated carrier for automotive assembly plants. Its novelty lies in conducting experimental investigations using the automated loading/unloading intelligent navigator to explore its advantages compared to manual loading/unloading in automotive assembly plants. In conclusion, building a carrier for assembly operations enhances assembly operational performance, correcting inefficient and unsafe loading and unloading processes.
In an original article, an addition was made to the well-known Taguchi’s methodical design literature by proposing how Poisson distribution may be incorporated into the Taguchi method for enhanced performance analysis in optimization. While the article is recent, it was found compelling enough to apply this novel concept of Poisson distribution to a growing area of maintenance research known as maintenance downtime analysis. Consequently, this paper contributes to the expanding research neighborhood through a Taguchi optimization method based on Poisson distribution related to the maintenance process optimization. A valuable method to optimize maintenance downtime was developed wherein the Poisson distribution was used to achieve the probability of maintenance downtime. An important foundation of the method is the Taguchi scheme. These elements were transformed into the factor-level design of the Poisson enhanced Taguchi scheme while the framework was tested using data from a process industry for validation. Interesting, the Taguchi's signal-to-noise quotient led to an enhanced set of limiting factors for better reliability of the system as G1H1I1J1K3. By interpretation, the following was found: downtime (204.61 mins), probability density function (0.00187), and cumulative density function (0.00776). The combination of these factors and levels will enhance maintenance downtime in the process industry as a result of their contributions. The outcome revealed the competence of the model to optimization schemes.