In the present experimental study, two dissimilar sister aluminum alloys, 5083 and 5086, are welded through friction stir welding process. Different experiments are conducted on vertical milling machine through a fixture design and firm holding welding plates on it. These experiments are performed by varying welding parameters including welding speed, rpm, different pin profiles, tool tilt angle at five levels each. Two different mechanical properties of the welded specimen (i.e. tensile strength and percentage of elongation) have been tested and compared with the base metals to find out the joint efficiency. Also it is observed that rpm, tool tilt angle and different pin profiles make significant impacts on friction stir welded joints. Tool rpm increases the tensile strength to an extent. Also, tool pin profile affects the stirring or plastic flow of material under tool shoulder. It is shown through visual and optical analysis that surface finish and weld quality of joints depend on tool tilt angle.

A wide range of obstacles faced by people with disabilities, such as visual impairment, motor disability, and communication difficulties, have shown significant promise for being addressed by computer vision. The state-of-the-art in computer vision-based assistive technology is examined in this report along with major future research topics and obstacles. In particular, this study explores how computer vision can be used for object recognition, navigation, facial recognition, sign language interpretation, and gesture-based control interfaces. It also discusses the benefits and drawbacks of various methodologies and technologies and offers examples of how computer vision can be incorporated into current assistive technologies to boost their efficacy. The ethical and privacy issues surrounding the use of computer vision in assistive technologies are covered in this study effort. The study also highlights the need for protocol standardization, better user-centered design, and the assessment of real-world effectiveness as future research objectives for improving the use of computer vision in assistive technology. Overall, this paper sheds light on how computer vision might completely alter the world of assistive technologies for people with impairments.

This paper outlines the vibrational response of a cracked rotor in static and rotating condition through Campbell diagram. An open crack in the rotor changes its stiffness. The effect of which is seen on the natural frequency of the system. The natural frequency of the cracked rotor increases in comparison to un-cracked rotor. Experimental and simulation work is performed in the static condition to study the natural frequency of the rotor. Campbell diagram is generated through Simulation in ANSYS to study the critical speed variation at first (I) and second (II) Engine order (EO) line for cracked and un-cracked rotor.

The application of material handling robots (MHRs) has been commonly observed in flexible manufacturing systems (FMS) for efficient material handling activities. In order to gain maximum throughput, minimum tardiness from the minimum investment of funds for the material handling activities, it is important to determine the optimum numbers of MHRs required for efficient production of jobs in the FMS. In the present work, the requirement of MHRs is optimized for different FMS layouts by using a heuristic procedure. Initially, a mathematical model is proposed to identify the MHRs requirement to perform the material handling activities in the FMS, later on, the model is optimized by simulating a novel heuristic procedure to find the required optimum number of MHRs in the FMS. The proposed methodology is found to be generic enough and can also be applied in various industries employing the MHRs.

This paper deals with the co-ordination of a single producer, multi distributors and multi retailers for a supply chain management to get the maximum profit at minimum investment when shortages is permitted at the retailer‘s end and they are the partially backlogged. Most previous studies on supply chain have dealt with a moderately simpler chain with a single producer and a single buyer. The requirement of the producer is directly proportional to demand of the distributor, while the demand of the distributor is dependent on retailers’ requirement. This passes on rationally to the whole supply chain. The proposed model of this paper considers deteriorating items where the deterioration rate is considered as constant.

Traditional economic production quantity (EPQ) model assumes that the production products are perfect. However, this assumption does not hold for many real production systems due to several weaknesses. This paper considers production inventory model with defective items for deteriorating items. In this paper, production rate is considered to be greater than demand rate. Mathematical model is developed for finding optimal order quantity, cycle time and total profit. Moreover, a numerical example is provided to illustrate the proposed model. Next, sensitivity analysis is established to demonstrate the model developed. Finally, some conclusions and future research directions are proposed.

Authors developed a two-period buyback pricing model which shows a competition between independent repair shop, third party remanufacturer (TPR) and original equipment manufacturer (OEM) for market share in spare parts business after the end of warranty period. Remanufacturing is a profitable option for OEM rather than producing new parts after finishing the warranty period for satisfying the demand of spare parts. OEM acquires damaged/broken parts from local independent repair shop to remanufacture those parts. But if there existed any Third Party Remanufacturer (TPR) then it would lead competition and would decrease the market share of OEM in sales of spare parts. TPR is basically independent remanufacturer. OEM has no control over the activity of the TPR for selling remanufactured spare parts after finishing warranty period of the products. In this paper, authors considered a supply chain model, where independent repair shop is responsible for handling the repair process and both OEM and TPR are remanufacturing spare parts. Repair shop may procure spare parts from both OEM and TPR. A discount is given on the price of the spare parts by TPR which attracts the customers. Repair shop also tries to sell repaired parts at an attractive discounted price. Both TPR and OEM need to collect broken/damaged parts to remanufacture them for maintaining an inventory of spare parts. This paper aimed to develop a deterministic framework for finding optimal buyback price for the OEM and the impact of different parameters on the profitability of spare parts management for individual players of supply chain management.

Suitable selection of various machining parameters for wire electrical discharge machining (WEDM) process heavily relies on the operator’s experience and manufacturer’s technologies because of their numerous and diverse operating ranges. Artificial neural networks have been introduced as an effective tool to predict values of responses and input parameters of different machining processes through forward and reverse modeling approaches respectively. This paper mainly focuses on predicting values of some machining responses, like machining rate, surface roughness, dimensional deviation and wire wear ratio using feed forward back propagation artificial neural network based on six WEDM process parameters, such as pulse on time, pulse off time, peak current, spark gap voltage, wire feed and wire tension. The corresponding reverse model is also developed to recommend the optimal settings of WEDM process parameters for achieving the desired responses according to the requirements of the end users. These modeling approaches are quite efficient to predict the values of machining responses as well as process parameter settings with reduced time and effort which otherwise have to be determined experimentally based on trial and error method. The predicted results are found to be in well congruence with the previously obtained experimental observations.

The performance of a product is generally characterized by more than one response variable. Hence the management often faces the problem of simultaneous optimization of many response variables. This study was undertaken to simultaneously optimize the surface hardness and case depth of carbonitrided bushes. Even though lots of literature has been published on various methodologies for tackling the multi-response optimization problem, the simultaneous optimization of heat treated properties of carbonitrided bushes are not reported yet. In this research the effect of four factors and two interactions on surface hardness and case depth of carbontirded bushes were studied using design of experiments. Based on the experimental results, the expected values of the heat treated properties of the bushes were estimated for all possible combination of factors. Then the best combination which, simultaneously optimized the response variables, was arrived at using desirability function. The study showed that the optimum combination obtained through desirability function approach not only minimized the variation around the targets of surface hardness and case depth but also was superior to the ones obtained by optimizing the response variables separately. Moreover this study provides a useful and effective approach to design the production process to manufacture bushes with customer specified surface hardness and case depth targets.