Experiential learning: integrating learning and experience in shaping the future of the engineers

The industry demands skill-equipped engineering graduates who could be efficient enough to adapt to face the challenges of uncertainty posed by a lack of skills and resources. Accreditation boards have identified problem-solving, teamwork, communication, etc. as the workplace required skills. However, industry/employers feel that the engineers seem to lack problem-solving, teamwork, etc. To groom these skills, experiential learning (EL) platform provides hands-on practice. Thus, the study aims to gain insights into the need of experiential learning to integrate learning and experience. The study, qualitative in nature, focuses on the essential skills, specifically problem-solving skills, against the applicability of experiential learning. Experiential learning allows engineering students to get a hands-on approach to practise their acquired skills to understand industrial needs and constraints. In the given context, problem solving helps in knowing what is learnt and what needs to be learnt

21st-century competencies in engineering education: initiation, evolution, current, and now whither to

The fibre of engineering education has evolved from knowledge to competencies. This is a logical consequence of the technologically advanced and multifaceted learning environment where engineers are expected to be technically acute along with a set of essential non-technical competencies. This change is referred to as a ‘paradigm shift’ in engineering education. Hence, the vision of learning is to immerse a progressive, learner-centric, and competency-based learning environment to face the uncertainties of the 21st century. There are various ways to improve the performance of learners by implementing the available competency frameworks, but the need is to initiate a set of essential competencies according to their nature and purpose that can endure across disciplines. In this paper, the evolution of competencies from the essential to the necessary is reviewed. Finally, the benefits of these competencies in relation to the performance of the engineers are discussed in detail through semi-structured interviews conducted with the engineers. MAXQDA, a qualitative data analysis tool, is used to analyse the data. The findings will help the engineers in grooming their competencies according to the industries

Predictive Modeling for Power Consumption in Machining Using Artificial Intelligence Techniques

AbstractThe objective of this work is to highlight the modeling capabilities of artificial intelligence techniques for predicting the power requirements in machining process. The present scenario demands such types of models so that the acceptability of power prediction models can be raised and can be applied in sustainable process planning. This paper presents two artificial intelligence modeling techniques - artificial neural network and support vector regression - used for predicting the power consumed in machining process. In order to investigate the capability of these techniques for predicting the value of power, a real machining experiment is performed. Experiments are designed using Taguchi method so that effect of all the parameters could be studied with minimum possible number of experiments. A L16 (43) 4-level 3-factor Taguchi design is used to elaborate the plan of experiments. The power predicted by both techniques are compared and evaluated against each other and it has been found that ANN slightly performs better as compare to SVR. To check the goodness of models, some representative hypothesis tests t-test to test the means, f-test and Leven's test to test variance are conducted. Results indicate that the models proposed in the research are suitable for predicting the power

Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

AbstractThis paper develops a predictive and optimization model by coupling the two artificial intelligence approaches – artificial neural network and genetic algorithm – as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters

Exploring the Three Dimensions of Sustainability Related to Clay Cups

Since long before the use of disposable foil, plastic and paper cups, clay cups have been widely used in India as single-use containers for a variety of beverages and foods. This is now changing. The cost, convenience and transportability of non-earthen containers has resulted in their replacing clay containers. This paper discusses the gains and losses from this substitution along the three dimensions of sustainability economic, environmental and social, and shows that the replacement analyses for even such a simple product are complex with tradeoffs in the three dimensions impacting the wellbeing of the producers and users. The paper also presents the life cycle assessment of clay cups in terms of endpoint and midpoint categories using ReCiPe method, and also find the environmental hotspots