IMPROVING DISTANCE LEARNING PROCESS IN ENGINEERING EDUCATION USING DESIGN OF EXPERIMENTS: RE-DESIGN OF AN ONLINE INDUSTRIAL ENGINEERING COURSE DURING AND BEYOND THE PANDEMIC COVID-19

Abstract: This study aims to improve the quality of the learning process in engineering education. The ‎COVID-19 ‎health crisis pushed the scientific community to review teaching practices and reconsider ‎their effectiveness. ‎Engineering education and learning were not an exception to that. This article ‎introduces a case study using the Design of Experiments method to improve ‎engineering education quality, especially ‎in the distance learning process. In this case study, we focused on ‎designing the process of distance ‎learning and its quality by working on the case of two industrial ‎engineering classes (2021 and 2022 ‎classes) in a Moroccan public engineering school. ‎The collaboration between the teacher and these two engineering students’ classes in their third year of ‎industrial engineering enabled us to identify factors influencing ‎learning ‎quality. Then, we determined the optimal ‎combinations of these factors for better quality by ‎analyzing the results of the experiments.‎ The Design of Experiments successfully implemented in ‎manufacturing can also be applied to ‎engineering education settings. The result of this study ‎would help teachers and decision-makers ‎understand the factors that influence the quality of ‎learning to improve the distance learning process.

Design of experiments for non-manufacturing processes : benefits, challenges and some examples

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments

Comparative study of Sustainability Metrics for Face Milling AISI 1045 in different Machining Centers

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)The objective of this study is to compare a set of sustainability metrics between different manufacturing resources applied to high performances machining centers. The research compares distributed scenarios in order to find the optimal conditions that allow the minimum consumed power and the minimum roughness when performing face milling operations of AISI 1045 steel. The set of experiments for the surface machining was carried out considering different path strategies in three main directions for two dimensional movements of the tool. The selected experiments considered the main axis movement, the perpendicular axis movement and a 45 degrees movement. Besides, it was considered the feed rate speed and the cutting depth. The design of experiments was developed with the Taguchi method considering an orthogonal matrix of L27 design type, and three levels of experimental design, and the analysis of variance and noise signal were performed. The methodology to determine the lowest power consumed and the best surface quality allowed to establish the working condition in the most sustainable machining. The results show how the cutting parameters influence in each manufacturing resource

Affects of student attendance on performance in undergraduate materials and manufacturing modules

This paper investigates the class attendance of second year, third year and fourth year students and their overall performance at the school of Mechanical and Manufacturing Engineering in Dublin City University (DCU). An investigation was recently conducted into the delivery of different module which was presented to a group of second year, third year and fourth year engineering students at DCU. Attendance in the class was recorded and the continuous assessment results and the final overall performances were investigated with their attendance. Student performance on Strength of materials – part 1 (SM1), Strength of materials part - 2 (SM2), Mechanics of Materials and Machine (MMM) and Advanced Materials and Manufacturing Processes (AMMP) modules are presented in this paper. This paper presents an examination of some of the factors affecting the overall results of these students. Factors evaluated include attendance of the student, as well as individual performance in continuous assessment and examination. Overall attendance at the lecture, the organised seminar series, and practical work were recorded. Results indicate a direct link between attendance and marks awarded. Students with higher attendance achieved better grades

Simulation Based Study of Safety Stocks under Short-Term Demand Volatility in Integrated Device Manufacturing.

© IEOM Society InternationalA problem faced by integrated device manufacturers (IDMs) relates to fluctuating demand and can be reflected in long-term demand, middle-term demand, and short-term demand fluctuations. This paper explores safety stock under short term demand fluctuations in integrated device manufacturing. The manufacturing flow of integrated circuits is conceptualized into front end and back end operations with a die bank in between. Using a model of the back-end operations of integrated circuit manufacturing, simulation experiments were conducted based on three scenarios namely a production environment of low demand volatility and high capacity reliability (Scenario A), an environment with lower capacity reliability than scenario A (Scenario B), and an environment of high demand volatility and low capacity reliability (Scenario C). Results show trade-off relation between inventory levels and delivery performance with varied degree of severity between the different scenarios studied. Generally, higher safety stock levels are required to achieve competitive delivery performance as uncertainty in demand increases and manufacturing capability reliability decreases. Back-end cycle time are also found to have detrimental impact on delivery performance as the cycle time increases. It is suggested that success of finished goods safety stock policy relies significantly on having appropriate capacity amongst others to support fluctuations

Bidirectional optimization of the melting spinning process

This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities