Search

The present study investigates the thermo-hydraulic characteristics of a microchannel sink with novel trefoil Shaped ribs. The motivation for this form of rib shape is taken from the design of lung alveoli that exchange oxygen and carbon dioxide. This study has been conducted numerically by using a code from the commercially available Fluent software. The trefoil shaped ribs were mounted on the centerline of different walls of the microchannel in three different configurations. These consisted of base wall trefoil ribs (MC-BWTR), sidewall trefoil ribs (MC-SWTR), all wall trefoil ribs (MC-AWTR) and smooth channel (MC-SC) having no ribs on its wall. The streamline distance between the ribs was kept constant at 0.4 mm, and the results were compared by using pressure drop (∆p), Nusselt number (Nu), thermal resistance (Rth) and thermal enhancement factor (η). The results indicated that the addition of trefoil ribs to any wall improved heat transfer characteristics at the expense of an increase in the friction factor. The trends of the pressure drop and heat transfer coefficient were the same, which indicated higher values for MC-AWTR followed by MC-SWTR and a lower value for MC-BWTR. In order to compare the thermal and hydraulic performance of all the configurations simultaneously, the overall performance was quantified in terms of the thermal enhancement factor, which was higher than one in each case, except for MC-AWTR, in 100 < Re < 200 regimes. The thermal enhancement factor in the ribbed channel was the highest for MC-SWTR followed by MC-BWTR, and it was the lowest for MC-AWTR. Moreover, the thermal enhancement factor increases with the Reynolds number (Re) for each case. This confirms that the increment in the Nusselt number with velocity is more significant than the pressure drop. The highest thermal enhancement factor of 1.6 was attained for MC-SWTR at Re = 1000, and the lowest value of 0.87 was achieved for MC-AWTR at Re = 100.

Increasing the energy efficiency of machining operations can contribute to more sustainable manufacturing. Therefore, there is a necessity to investigate, evaluate, and optimize the energy consumed during machining operations. The research highlights a method employed to prioritize the most energy-intensive machining operation and highlights the significance of electric parameters as predictors in power estimation of machining operations. Multi regression modeling with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations. The absolute error and the relative error both decreased when the active power was measured by the power analyzer for each of the identified machining operations, compared to the standard power equation and that obtained from the modeled regression equations. Furthermore, to determine energy-intensive machining operation, a hybrid decision-making technique based on TOPSIS (a technique for order preference by similarity to ideal solution) and DoM (degree of membership) was utilized. Allocation of weights to energy responses was carried out using three methods, i.e., equal importance, entropy weights, and the AHP (analytical hierarchy process). Results revealed that a drilling process carried out on material ST 52.3 is energy-intensive. This accentuates the significance of electric parameters in the assessment of active power during machining operations.

Sustainability assessments of cooling/lubrication-assisted advanced machining processes has been demanded by environment control agencies because it is an effective management tool for improving process sustainability. To achieve an effective and efficient sustainability evolution of machining processes, there is a need to develop a new method that can incorporate qualitative indicators to create a quantifiable value. In the present research work, a novel quantifiable sustainability value assessment method was proposed to provide performance quantification of the existing sustainability assessment methods. The proposed method consists of three steps: establishing sustainable guidelines and identifying new indicators, data acquisition, and developing an algorithm, which creates the Overall Performance Assessment Indicator (OPAI) from the sustainability assessment method. In the proposed algorithm, initially, both quantitative and qualitative sustainability indicators are normalized. After weight assignment and aggregation, the OPAI is obtained. The developed algorithm was validated from three literature case studies, and optimal cutting parameters were obtained. The present methodology provides effective guidelines for a machinist to enhance process performance and achieve process optimization. The study also offers a relationship between sustainable and machining metrics for the support of industrial sustainability.