Abstract: Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.
Abstract: The change of conditions for production companies in
high-wage countries is characterized by the globalization of
competition and the transition of a supplier´s to a buyer´s market. The
companies need to face the challenges of reacting flexibly to these
changes. Due to the significant and increasing degree of automation,
assembly has become the most expensive production process.
Regarding the reduction of production cost, assembly consequently
offers a considerable rationalizing potential. Therefore, an
aerodynamic feeding system has been developed at the Institute of
Production Systems and Logistics (IFA), Leibniz Universitaet
Hannover. This system has been enabled to adjust itself by using a
genetic algorithm. The longer this genetic algorithm is executed the
better is the feeding quality. In this paper, the relation between the
system´s setting time and the feeding quality is observed and a
function which enables the user to achieve the minimum of the total
feeding time is presented.
Abstract: In order to be competitive, companies have to reduce
their production costs while meeting increasing quality requirements.
Therefore, companies try to plan their assembly processes as detailed
as possible. However, increasing product individualization leading to
a higher number of variants, smaller batch sizes and shorter product
life cycles raise the question to what extent the effort of detailed
planning is still justified. An important approach in this field of
research is the concept of determining the economic planning depth
for assembly process planning based on production specific
influencing factors. In this paper first solution hypotheses as well as a
first draft of the resulting method will be presented.
Abstract: Several researches have been conducted to study
consumption of energy in cutting process. Most of these researches
are focusing to measure the consumption and propose consumption
reduction methods. In this work, the relation between the cutting
parameters and the consumption is investigated in order to establish a
generalized energy consumption model that can be used for process
and production planning in real production lines. Using the
generalized model, the process planning will be carried out by taking
into account the energy as a function of the selected process
parameters. Similarly, the generalized model can be used in
production planning to select the right operational parameters like
batch sizes, routing, buffer size, etc. in a production line. The
description and derivation of the model as well as a case study are
given in this paper to illustrate the applicability and validity of the
model.
Abstract: The purpose of this paper is to simulate the production process of a metal stamping industry and to evaluate the utilization of the production line by using ARENA simulation software. The process time and the standard time for each process of the production line is obtained from data given by the company management. Other data are collected through direct observation of the line. There are three work stations performing ten different types of processes in order to produce a single product type. Arena simulation model is then developed based on the collected data. Verification and validation are done to the Arena model, and finally the result of Arena simulation can be analyzed. It is found that utilization at each workstation will increase if batch size is increased although throughput rate remains/is kept constant. This study is very useful for the company because the company needs to improve the efficiency and utilization of its production lines.
Abstract: Modeling of a manufacturing system enables one to
identify the effects of key design parameters on the system performance and as a result to make correct decision. This paper
proposes a manufacturing system modeling approach using a spreadsheet model based on queuing network theory, in which a
static capacity planning model and stochastic queuing model are integrated. The model was used to improve the existing system utilization in relation to product design. The model incorporates few
parameters such as utilization, cycle time, throughput, and batch size.
The study also showed that the validity of developed model is good enough to apply and the maximum value of relative error is 10%, far
below the limit value 32%. Therefore, the model developed in this
study is a valuable alternative model in evaluating a manufacturing system
Abstract: The transient analysis of a queuing system with fixed-size batch Poisson arrivals and a single server with exponential service times is presented. The focus of the paper is on the use of the functions that arise in the analysis of the transient behaviour of the queuing system. These functions are shown to be a generalization of the modified Bessel functions of the first kind, with the batch size B as the generalizing parameter. Results for the case of single-packet arrivals are obtained first. The similarities between the two families of functions are then used to obtain results for the general case of batch arrival queue with a batch size larger than one.