Analytical evaluation of the output variability in production systems with general Markovian structure

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general methodology to analyze the variability of the output of unreliable single machines and small-scale multi-stage production systems modeled as General Markovian structure. The generality of the approach allows modeling and studying performance measures such as the variance of the cumulated output and the variance of the inter-departure time under many system configurations within a unique framework. The proposed method is based on the characterization of the autocorrelation structure of the system output. The impact of different system parameters on the output variability is investigated and characterized. Moreover, managerial actions that allow reducing the output variability are identified. The computational complexity of the method is studied on an extensive set of computer experiments. Finally, the limits of this approach while studying long multi-stage production lines are highlighted. © 2013 Springer-Verlag Berlin Heidelberg

Characterization of fine metal particles using hyperspectral imaging in automatic WEEE recycling systems

Waste from electric and electronic equipment (WEEE) represents the fastest growing waste stream in EU. The large amount and the high variability of electric and electronic products introduced every year in the market make the WEEE recycling process a complex task, especially considering that mechanical processes currently used by recycling companies are not flexible enough. In this context, hyperspectral imaging systems (HSI) can represent an enabling technology able to improve the recycling rates and the quality of the output products. This study shows the preliminary results achieved using a HSI technology in a WEEE recycling pilot plant, for the characterization of fine metal particles derived from WEEE shredding

Characterization of fine metal particles derived from shredded WEEE using a hyperspectral image system: Preliminary results

Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metalsâ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%

Modularization in material flow simulation for managing production releases in remanufacturing

Remanufacturing is recognized as a major circular economy option to recover and upgrade functions from post-use products. However, the inefficiencies associated with operations, mainly due to the uncertainty and variability of material flows and product conditions, undermine the growth of remanufacturing. With the objective of supporting the design and management of more proficient and robust remanufacturing processes, this paper proposes a generic and reconfigurable simulation model of remanufacturing systems. The developed model relies upon a modular framework that enables the user to handle multiple process settings and production control policies, among which token-based policies. Customizable to the characteristics of the process under analysis, this model can support logistics performance evaluation of different production control policies, thus enabling the selection of the optimal policy in specific business contexts. The proposed model is applied to a real remanufacturing environment in order to validate and demonstrate its applicability and benefits in the industrial settings

Particle simulation of granular flows in electrostatic separation processes

In waste processing technology, the recent Corona Electrostatic Separation (CES) method is used to separate conductive from non-conductive particles in recycling streams. This paper proposes an innovative simulation approach based on non-smooth dynamics. In this context, a differential-variational formulation is used to implement a scalable and efficient time integrator that allows the large-scale simulation of trajectories of particles with different properties under the effect of particle-particle interactions and frictional contacts. Issues related to performance optimization, fast collision detection and parallelization of the code are discussed

Automotive manufacturing technologies – an international viewpoint

The automotive industry can be described as a backbone in many developed countries such as Japan, Korea, USA, and Germany, while being an enabler for economic prosperity in developing countries like China, Brazil, Eastern Europe, and Russia at the same time. However, the dynamics and uncertainty are increasing heavily by market changes, regulations, customer behavior, and new product technologies. Manufacturing research has to find answers to increase quality of products, flexibility of plants, and supply chain networks, to manage complexity in technologies and variants and overall to stay competitive even in high wage countries. In this paper, major technological challenges are discussed and the current state of manufacturing technology and research is presented. Moreover, for each technological and organizational area, future industrial, and research challenges are highlighted

Beam Test of Silicon Strip Sensors for the ZEUS Micro Vertex Detector

For the HERA upgrade, the ZEUS experiment has designed and installed a high precision Micro Vertex Detector (MVD) using single sided micro-strip sensors with capacitive charge division. The sensors have a readout pitch of 120 microns, with five intermediate strips (20 micron strip pitch). An extensive test program has been carried out at the DESY-II testbeam facility. In this paper we describe the setup developed to test the ZEUS MVD sensors and the results obtained on both irradiated and non-irradiated single sided micro-strip detectors with rectangular and trapezoidal geometries. The performances of the sensors coupled to the readout electronics (HELIX chip, version 2.2) have been studied in detail, achieving a good description by a Monte Carlo simulation. Measurements of the position resolution as a function of the angle of incidence are presented, focusing in particular on the comparison between standard and newly developed reconstruction algorithms.Comment: 41 pages, 21 figures, 2 tables, accepted for publication in NIM

Radiation Tolerance of CMOS Monolithic Active Pixel Sensors with Self-Biased Pixels

CMOS Monolithic Active Pixel Sensors (MAPS) are proposed as a technology for various vertex detectors in nuclear and particle physics. We discuss the mechanisms of ionizing radiation damage on MAPS hosting the the dead time free, so-called self bias pixel. Moreover, we discuss radiation hardened sensor designs which allow operating detectors after exposing them to irradiation doses above 1 Mra

Optimization of Tracking Performance of CMOS Monolithic Active Pixel Sensors

CMOS Monolithic Active Pixel Sensors (MAPS) provide an attractive solution for high precision tracking of minimum ionizing particles. In these devices, a thin, moderately doped, undepleted silicon layer is used as the active detector volume with the readout electronics implemented on top of it. Recently, a new MAPS prototype was fabricated using the AMS 0.35 $mu$m OPTO process, featuring a thick epitaxial layer. A systematic study of tracking performance of that prototype using high-energy particle beam is presented in this work. Noise performance, signal amplitude from minimum ionizing particles, detection efficiency, spurious hit suppression and spatial resolution are shown as a function of the readout pitch and the charge collecting diode size. A test array with a novel readout circuitry was also fabricated and tested. Each pixel circuit consists of a front-end voltage amplifier, capacitively coupled to the charge collecting diode, followed by two analog memory cells. This architecture implements an on-pixel correlated double sampling method, allowing for optimization of integration independently of full frame readout time and strongly reduces the pixel-to-pixel output signal dispersion. First measurements using this structure are also presented