A Structured Approach to Simulate Manufacturing System In Virtual Environment

Nowadays, the realization of the Virtual Factory (VF) is the strategic goal of many manufacturing enterprises for the coming years. The industrial scenario is characterized by the dynamics of innovations increment and the product life cycle became shorter. Furthermore products and the corresponding manufacturing processes get more and more complex. Therefore, companies need new methods for the planning of manufacturing systems.
To date, the efforts have focused on the creation of an integrated environment to design and manage the manufacturing process of a new product. The future goal is to integrate Virtual Reality (VR) tools into the Product Lifecycle Management of the manufacturing industries.
In order to realize this goal the authors have conducted a study to perform VF simulation steps for a supplier of Industrial Automation Systems and have provided a structured approach focusing on interaction between simulation software and VR hardware tools in order to simulate both robotic and
manual work cells.
The first results of the study in progress have been carried out in the VR Laboratory of the Competence Regional Centre for the qualification of the Transportation Systems that has been founded by Campania Region.

Rotational Molding Cycle Time Reduction Using a Combination of Physical Techniques

Rotational molding is a process used to manufacture hollow plastic products, and has been heralded as a molding method with great potential. Reduction of cycle times is an important issue for the rotational molding industry, addressing a significant disadvantage of the process. Previous attempts to reduce cycle times have addressed surface enhanced molds, internal pressure, internal cooling, water spray cooling, and higher oven air flow rates within the existing process. This article explores the potential benefits of these cycle time reduction techniques, and combinations of them. Recommendations on a best practice combination are made, based on experimental observations and resulting product quality. Applying the proposed molding conditions (i.e., a combination of surface-enhanced molds, higher oven flow rates, internal mold pressure, and water spray cooling), cycle time reductions of up to 70% were achieved. Such savings are very significant, inviting the rotomolding community to incorporate these techniques efficiently in an industrial setting. POLYM. ENG. SCI., 49:1846-1854, 2009. (C) 2009 Society of Plastics Engineers

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

Laser-driven x-ray and neutron source development for industrial applications of plasma accelerators

Pulsed beams of energetic x-rays and neutrons from intense laser interactions with solid foils are promising for applications where bright, small emission area sources, capable of multi-modal delivery are ideal. Possible end users of laser-driven multi-modal sources are those requiring advanced non-destructive inspection techniques in industry sectors of high value commerce such as aerospace, nuclear and advanced manufacturing. We report on experimental work that demonstrates multi-modal operation of high power laser-solid interactions for neutron and x-ray beam generation. Measurements and Monte Carlo radiation transport simulations show that neutron yield is increased by a factor ∼2 when a 1 mm copper foil is placed behind a 2 mm lithium foil, compared to using a 2 cm block of lithium only. We explore x-ray generation with a 10 picosecond drive pulse in order to tailor the spectral content for radiography with medium density alloy metals. The impact of using >1 ps pulse duration on laser-accelerated electron beam generation and transport is discussed alongside the optimisation of subsequent bremsstrahlung emission in thin, high atomic number target foils. X-ray spectra are deconvolved from spectrometer measurements and simulation data generated using the GEANT4 Monte Carlo code. We also demonstrate the unique capability of laser-driven x-rays in being able to deliver single pulse high spatial resolution projection imaging of thick metallic objects. Active detector radiographic imaging of industrially relevant sample objects with a 10 ps drive pulse is presented for the first time, demonstrating that features of 200 μm size are resolved when projected at high magnification.