On the application of uncertainty models in copying machine maintenance

2012 ◽  
Vol 7 (2) ◽  
pp. 147-152 ◽  
Author(s):  
Samir A. Abass ◽  
Marwa Sh. Elsayed
Keyword(s):  
Machine Maintenance ◽  
Copying Machine ◽  
Uncertainty Models

Perencanaan Pengendalian Kualitas Produk Pakaian Bayi dengan Metode Six Sigma Pada CV. AGP

JEMAP ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Albertus Reynaldo Kurniawan ◽  
Bayu Prestianto
Keyword(s):  
Quality Control ◽  
Continuous Improvement ◽  
Six Sigma ◽  
Screen Printing ◽  
Control Method ◽  
Operational Performance ◽  
Machine Maintenance ◽  
Quality Product ◽  
Break Time ◽  
And Control

Quality control becomes an important key for companies in suppressing the number of defective produced products. Six Sigma is a quality control method that aims to minimize defective products to the lowest point or achieve operational performance with a sigma value of 6 with only yielding 3.4 defective products of 1 million product. Stages of Six Sigma method starts from the DMAIC (Define, Measure, Analyze, Improve and Control) stages that help the company in improving quality and continuous improvement. Based on the results of research on baby clothes products, data in March 2018 the percentage of defective products produced reached 1.4% exceeding 1% tolerance limit, with a Sigma value of 4.14 meaning a possible defect product of 4033.39 opportunities per million products. In the pareto diagram there were 5 types of CTQ (Critical to Quality) such as oblique obras, blobor screen printing, there is a fabric / head cloth code on the final product, hollow fabric / thin fabric fiber, and dirty cloth. The factors caused quality problems such as Manpower, Materials, Environtment, and Machine. Suggestion for consideration of company improvement was continuous improvement on every existing quality problem like in Manpower factor namely improving comprehension, awareness of employees in producing quality product and improve employee's accuracy, Strength Quality Control and give break time. Materials by making the method of cutting the fabric head, the Machine by scheduling machine maintenance and the provision of needle containers at each employees desk sewing and better environtment by installing exhaust fan and renovating the production room.

scholarly journals Using the Variable Geometry in a Planar Inductor for an Optimised Performance

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 721
Author(s):  
Maha Aldoumani ◽  
Baris Yuce ◽  
Dibin Zhu
Keyword(s):  
Smart Cities ◽  
Theoretical Concept ◽  
Displacement Sensor ◽  
Sensor Systems ◽  
Performance Modelling ◽  
Integrated Sensor ◽  
Complex Information ◽  
Electromagnetic Sensor ◽  
The Individual ◽  
Uncertainty Models

In this paper, the performance, modelling and application of a planar electromagnetic sensor are discussed. Due to the small size profiles and their non-contact nature, planar sensors are widely used due to their simple and basic design. The paper discusses the experimentation and the finite element modelling (FEM) performed for developing the design of planar coils. In addition, the paper investigates the performance of various topologies of planar sensors when they are used in inductive sensing. This technique has been applied to develop a new displacement sensor. The ANSYS Maxwell FEM package has been used to analyse the models while varying the topologies of the coils. For this purpose, different models in FEM were constructed and then tested with topologies such as circular, square and hexagon coil configurations. The described methodology is considered an effective way for the development of sensors based on planar coils with better performance. Moreover, it also confirms a good correlation between the experimental data and the FEM models. Once the best topology is chosen based on performance, an optimisation exercise was then carried out using uncertainty models. That is, the influence of variables such as number of turns and the spacing between the coils on the output inductance has been investigated. This means that the combined effects of these two variables on the output inductance was studied to obtain the optimum values for the number of turns and the spacing between the coils that provided the highest level of inductance from the coils. Integrated sensor systems are a pre-requisite for developing the concept of smart cities in practice due to the fact that the individual sensors can hardly meet the demands of smart cities for complex information. This paper provides an overview of the theoretical concept of smart cities and the integrated sensor systems.

scholarly journals Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Sehrish Malik ◽  
DoHyeun Kim
Keyword(s):  
Smart Factory ◽  
Prediction Algorithm ◽  
Utilization Rate ◽  
Task Management ◽  
Management Mechanism ◽  
Machine Maintenance ◽  
Machine Utilization ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Prediction Mechanism

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

Can machine learning be used to forecast the future uncertainty of military teams?

2021 ◽  
pp. 154851292199911
Author(s):  
Ronald H Stevens ◽  
Trysha L Galloway
Keyword(s):  
Machine Learning ◽  
Sensory Information ◽  
Fundamental Property ◽  
Transition Matrices ◽  
Team Members ◽  
Student Teams ◽  
The Future ◽  
Network States ◽  
Uncertainty Models ◽  
Military Teams

Uncertainty is a fundamental property of neural computation that becomes amplified when sensory information does not match a person’s expectations of the world. Uncertainty and hesitation are often early indicators of potential disruption, and the ability to rapidly measure uncertainty would have implications for future educational and training efforts by targeting reflective discussions about past actions, supporting in-progress corrections, and generating forecasts about future disruptions. An approach is described combining neurodynamics and machine learning to provide quantitative measures of uncertainty. Models of neurodynamic information derived from electroencephalogram (EEG) brainwaves have provided detailed neurodynamic histories of US Navy submarine navigation team members. Persistent periods (25–30 s) of neurodynamic information were seen as discrete peaks when establishing the submarine’s position and were identified as periods of uncertainty by an artificial intelligence (AI) system previously trained to recognize the frequency, magnitude, and duration of different patterns of uncertainty in healthcare and student teams. Transition matrices of neural network states closely predicted the future uncertainty of the navigation team during the three minutes prior to a grounding event. These studies suggest that the dynamics of uncertainty may have common characteristics across teams and tasks and that forecasts of their short-term evolution can be estimated.

Analyzing Massive Machine Maintenance Data in a Computing Cloud

2012 ◽  
Vol 23 (10) ◽  
pp. 1831-1843 ◽  
Author(s):  
Arshdeep Bahga ◽  
Vijay K. Madisetti
Keyword(s):  
Machine Maintenance
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