Buckling is one of the most complicated concepts in mechanical engineering. Buckling often happens by compressive loads on thin structures. Thermal gradient between two ends of a column may cause a deflection in it. This will add an extra deformation to the one provided by compressive loads on the column. This phenomenon occurs when two ends of the column are at different temperatures, which can be seen at various structures. Because of the considered temperature gradients, the critical load of the column will decrease. In the current paper, various columns are modeled and the effect of thermal gradient and compressive load and other parameters on the Bi-material columns are studied. In other words, influences of compressive load and temperature gradient on critical load of Bi-material columns with interface crack are investigated. Effect of change in each parameter on critical load of column and crack opening was investigated. First, the thermal gradient was only applied to the model and in the next step; only the effect of mechanical loading was studied. Furthermore, artificial neural network (ANN) was used to extend the results to a bigger range of temperature conditions through the columns. Based on the results, ANN and finite element results are in a good agreement and the thermal effects may have a significant role in buckling of the column.

With the development of faster means of communication, better quality computers and rapid transportation systems, manufacturing is no longer restricted at local level, but has become global in character. As a manufacturing company has to become competitive for its survival, it has to supply products of consistent high quality at reliable and reduced delivery time. Market also demands more product variants that means reduced lot size and high flexibility in operations. Manpower cost has also risen. All these factors tend to increase the product cost. However, the industry has to maintain the cost at a reasonable level. Confronting these challenges, companies world-wide are forced to find ways to reduce costs, improve quality, and meet the ever-changing needs of their customers. One successful solution has been the adoption of Just-in-time (JIT) manufacturing strategy in which many functional areas of a company such as manufacturing, engineering, marketing, purchasing etc. are involved. In this paper, literature review on research works based on JIT was carried out and presented. The introductory section deals with the philosophy of JIT, and the concept involved in Kanban optimization and later this paper reviews literature on optimization Technique in JIT implementation.

Climate change and global warming have led to many risks and changes for the Earth, including the increase in natural fires, floods, air pollution, unusual seasons, etc. The increase in the trend of global warming may bring many countries underwater. For instance, the populous Asian nation of Bangladesh is most vulnerable to rising sea levels. It is estimated that a rise of just one meter in sea level is enough to submerge 30,000 square kilometres of the coastal areas of Bangladesh and displace 15 million people. Therefore, it is crucial to determine the effects of different factors on global warming and take possible actions to reduce the damage. Among various factors, natural fires are believed to be responsible for up to 20% of greenhouse gas production in the world. The source of 80% of fresh water in the United States is believed to be forest lands, which means the effect of natural fires can be disastrous not only for drinking water but also for aquatic habitats. In this paper, we present a survey on the impacts of forest fires on global warming.

A multiproduct pipeline provides an economic way to transport large volumes of refined petroleum products over long distances. In such a pipeline, different products are pumped back−to−back without any separation device between them. The sequence and lengths of such pumping runs must be carefully selected in order to meet market demands while minimizing pipeline operational costs and satisfying several constraints. The production planning and scheduling of the products at the refinery must also be synchronized with the transportation in order to avoid the usage of the system at some peak−hour time intervals. In this paper, we propose a multi−period mixed integer nonlinear programming (MINLP) model for an optimal planning and scheduling of the production and transportation of multiple petroleum products from a refinery plant connected to several depots through a single pipeline system. The objective of this work is to generalize the mixed integer linear programming (MILP) formulation proposed by Cafaro and Cerdá (2004, Computers and Chemical Engineering) where only a single planning period was considered and the production planning and scheduling was not part of the decision process. Numerical examples show how the use of a single period model for a given time period may lead to infeasible solutions when it is used for the upcoming periods. These examples also show how integrating production planning with the transportation and the use of a multi−period model may result in a cost saving compared to using a single−period model for each period, independently.

Functionally Graded Steels (FGSs) are possible solutions to improve the properties of steels made by Martensite and Bainite brittle phases. These phases are usually present in the interface between the carbon ferritic steel and the stainless austenitic steel. FGSs materials are widely investigated in the recent literature but only few works have been devoted to investigate the impact energy in the case of crack arresters. To partially fill this gap, the effect of the distance between the notch tip and the position of the median phase on the Charpy impact energy is investigated in the present paper. The results show that when the notch apex is close to the median layer the impact energy reaches its maximum value due to the increment of the absorbed energy by plastic deformation ahead of the notch tip. On the other hand, when the notch apex is far from the median layer, the impact energy strongly decreases. Keeping into account the relationship between the Charpy impact energy and the plastic volume size, a new theoretical model has been developed to link the composite impact energy with the distance from the notch apex to the median phase. The results of the new model show a sound agreement with previous results taken from the literature.

A series of pyrido[2,3-b]pyrazine derivatives were synthesized in good to excellent yields by condensation reactions of arylglyoxals with 2,3-diaminopyridine in dimethylformamide and ethanol at 90 oC.

The purpose of this paper is to predict the S & P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature.

The evolving technology has pushed machine learning techniques to replace human smartness. A machine learning model is capable of learning and replicating like our brain. This approach of data-driven model is implemented to predict the cutting force in machining of Ti6Al4V. Titanium alloys are commonly used in high strength applications due to their excellent properties. These same properties make the machining of the titanium alloy complicated. An attempt has been made for finding the cutting force under minimum quantity lubrication (MQL). MQL is a sustainable manufacturing-based lubrication system. Taguchi’s approach was used to attain a full factorial design for combination of different parameters. Accordingly, a neural network (NN) model was developed which was capable of predicting cutting forces based on the trained model. The proposed model could be implemented to find optimal parameters in shortest duration, thereby eliminating the need for experimental computations.

Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This paper presents an improved variant of TLBO algorithm, called Improved Teaching–Learning-Based Optimization (ITLBO). A performance comparison of the proposed method is provided against the original TLBO and some other algorithms. The improved TLBO algorithm shows a marked improvement in performance over the traditional TLBO on several benchmark optimization problems.

The simultaneous scheduling decisions between production systems and material handling systems are highly significant for a substantial reduction in makespan and improvement in throughput of flexible manufacturing system resources. In the absence of appropriate scheduling of production resources, the optimum utilization of FMS resources is not harnessed which turns into wastage of resources. In the present study, investigations are carried out for the sustainable multi-objective scheduling of automatic guided vehicle and flexible manufacturing system by the application of a grey wolf optimization algorithm (GWO). Initially the Giffler and Thompson (GT) algorithm [Giffler, B., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations research, 8(4), 487-503.] along with four different priority hybrid dispatching rules (PHDRs) are applied for the development of the production center schedule thereafter the grey wolf optimization algorithm is applied for the yield of the sustainable multi-objective schedul-ing of automatic guided vehicles (AGVs) and the FMS together with an objective to minimize the total distance travel and number of backtracking of cruising automatic guided vehicle in the U type flexible manufacturing system facility. The applied methodology is evaluated by conducting computational experiments on a benchmark flexible manufacturing system configuration considered from the literature. The results obtained from the computational experiments clearly show that the proposed application of grey wolf optimization algorithm outperforms the other applied procedures in the literature.