Co-continuous composites have potential in friction and braking applications due to their unique tribological characteristics. The present study involves Taguchi grey relational analysis-based optimization of wear parameters such as applied load, sliding speed and sliding distance, and their effect on dry sliding wear performance of AA6063/SiC co-continuous composite manufactured by gravity infiltration. A Taguchi L9 orthogonal array was designed and nine experimental runs were performed based on the designed experiments. The coefficient of wear and specific wear rate were recorded for each experiment. Based on the average responses computed from Taguchi grey relational analysis, an applied load of 60 N, sliding speed of 1 m/s and sliding distance of 1000 m were estimated to be the optimal parameters. An Analysis of Variance (ANOVA) was conducted to identify the predominant factor and established all the three factors as being significant. The sliding distance was found to have the highest significant influence of 61.05% on the wear of the C4 composite. Confirmation experiments conducted using the optimal parameters indicated an improvement of 35.25% in grey relational grade. Analysis of the worn surfaces of the confirmation experiment revealed adhesive and abrasive wear as the governing mechanisms.
In this paper, we present an integrated collaborative modular architecture method for medical device design and development. The methodology is focused on analyzing the input of stakeholder data from existing products and components to achieve an optimal number of modules. The methodology starts by defining a product’s functional and physical decompositions. Product parameters are selected such as quality, reliability, ease of development, and cost. These are prioritized using analytical hierarchy process (AHP) to determine the medical device manufacturers’ focus area. The parameters’ subsequent metrics are selected for performance requirements. Next, we evaluate the candidate modules by acquiring stakeholder data and converting them to crisp values by applying the Sugeno fuzzy-based method. Finally, we determine the subsequent optimal module values using a multi-optimization goal programming model. We present here a proof of concept using a typical glucometer. The implication of this work is the determination of the optimal number of product modules based on stakeholder constraints. Hence, an original equipment manufacturer (OEM) can work on fewer components per module without adversely affecting the integrity, quality, and reliability of the final product. Next is the improved quality of patient care by enabling cost reductions in product design and development, thereby improving patient safety. This methodology helps reduce product cycle time, thereby improving market competitiveness among other factors.
A co-continuous ceramic composite (C4) was manufactured by gravity infiltration. The effect of varying machining parameters namely, speed, feed and depth of cut during end milling of C4, on the multi-responses of surface roughness, tool wear and depth of cut was investigated using response surface methodology. Non-linear regression models were generated and optimal machining parameters were determined using desirability analysis. Confirmation experiments performed, validated the models with a ± 5 % error in prediction.
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