This paper proposes the modelling of a turning process using a gravitational search algorithm (GSA). GSA is an optimization algorithm based on Newton's law of universal gravitation and mass interactions. In order to sufficiently describe the turning process, at least three independent variables are required: cutting speed, feed-rate, and cutting depth. Independent variables have impacts on dependent variables, which were in our case cutting force, surface roughness, and tool-life. The values of independent and dependent variables obtained by measurements serve as a knowledge database for feeding the GSA optimization process. During our research the GSA was used for optimizing the numerical coefficients of predefined polynomial models for describing the observed output variables. The accuracies of the obtained prediction models were proved by means of a testing data set that was excluded from the training data. The research showed that the obtained results were comparable with the other optimization algorithms such as particle swarm optimization (PSO). However, the optimization time required for GSA optimization was, in certain cases, significantly shorter.
This paper proposes the modelling of a turning process using particle swarm optimization (PSO). The independent input machining parameters for the modelling were cutting speed, feed rate, and cutting depth. The input parameters affected three dependent output parameters that were the main cutting force, surface roughness, and tool life. The values of the independent and dependent parameters were acquired by experimental work and served as knowledge base for the PSO process. By utilizing the knowledge base and the PSO approach, various models could be acquired for describing the cutting process. In our case, three different polynomial models were obtained: models a) for the main cutting force, b) for surface roughness, and c) for tool life. All the models had exactly the same basic polynomial form which was chosen similarly to that in the conventional regression analysis method. The PSO approach was used for optimization of the polynomials' coefficients. Several different randomly-selected data sets were used for the learning and testing phases. The accuracies of the developed models were analysed. It was discovered that the accuracies of the models for different learning and testing data sets were very good, having almost the same deviations. The least deviation was noted for the cutting force, whilst the most deviation, as expected was for tool life. The obtained models could then be used for later optimization of the turning process.
In this paper, we proposed a Gravitational Search Algorithm (GSA) and an NSGA-II approach for multi-objective optimization of the CNC turning process. The GSA is a swarm intelligence method exploiting the Newtonian laws on elementary mass objects interaction in the search space. The NSGA-II is an evolutionary algorithm based on non-dominated sorting. On the basis of varying values of the three independent input machining parameters (i.e., cutting speed, depth of cut, and feed rate), the values of the three dependent output variables were measured (i.e., surface roughness, cutting forces, and tool life). The obtained data set was divided further into two subsets, for the training data and the testing data. In the first step of the proposed approach, the GSA and the training data set were applied to modelling a suitable model for each output variable. Then the accuracies of the models were checked by the testing data set. In the second step, the obtained models were used as the objective functions for a multi-objective optimization of the turning process by the NSGA-II. The optimization constraints relating to intervals of dependent and independent variables were set on the theoretical calculations and confirmed with experimental measurements. The goal of the multi-objective optimization was to achieve optimal surface roughness, cutting forces, and increasing of the tool life, which reduces production costs. The research has shown that the proposed integrated GSA and NSGA-II approach can be implemented successfully, not only for modelling and optimization of the CNC turning process, but also for many other manufacturing processes.
Existential tinkering as a form of inquiry must be brought into the engineering curriculum at the university level, as well as into the education curricula in general, including early childhood education. This paper presents a methodology of education for people of all ages and abilities, including engineering education, through unstructured play, personal involvement (authenticity), expression, and exploration -playful tinkering -as forms of inquiry. Current methods of engineering education have too much emphasis on structure, creating rigidity that destroys the capacity for creativity and radical innovation and invention. We introduce "existinquiry/praxistemology" (existential tinkering as inquiry) as a learning methodology consisting of three parts: learning by thinking, learning by doing, and "learning by being" (existential education). The goal of this learning methodology is to create lateral thinkers who integrate ideas and methodologies normally associated with play, the arts, and the sciences, into the the creative thinking process of engineering and design. Our hope is that (1) existinquiry in engineeing education will create more competitive and versatile thinkers capable of solving more sophisticated problems; and (2) that combining concepts of engineering education with concepts of unstructured play that are normally associated with early childhood education, will result in more groundbreaking inventions. We playfully explore topics of Veillance (surveillance, sousveillance, reciprocal transparency, equiveillance/omniveillance, uberveillance, and dataveillance) and Natural User Interfaces with the fundamental Elements (earth, water, air, etc.). The methodologies are applicable to teaching engineering to children or adults of any age or ability. ENGUCATIONEngineering education helps train young minds with processes and procedures to solve problems. Operating under this schema works well in situations where the problems can be clearly defined in the context of "vertical thinking" and solving specific problems.But true creativity, including lateral-thinking, needs to be personal. Humans have a natural capacity to improvise and detect patterns as they occur. Entrepreneurs work in a similar way. They reorganize resources to create new value. The scope and shape of these resources can be diverse and dynamic depending on which industry they operate in.Engucation (Engineering Education) is an area of study that investigates teaching and learning in an engineering curriculum. Though several focii exist, optimizing student learning is a key goal of this field. Some approaches range from active learning, and increasing levels of agency (Scardamalia and Bereiter), and problem-based learning to the more traditional lecture-based delivery, and more recently, e-learning [1]. Active learning/thinking/roles encompass a personal involvement in each learners trajectory of understanding concepts, and works well with "higher levels of agency for children in knowledge building" [2]. The next level of active learn...
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