Operational experience with the GEM detector assembly lines for the CMS forward muon upgrade

The CMS Collaboration has been developing large-area triple-gas electron multiplier (GEM) detectors to be installed in the muon Endcap regions of the CMS experiment in 2019 to maintain forward muon trigger and tracking performance at the High-Luminosity upgrade of the Large Hadron Collider (LHC); 10 preproduction detectors were built at CERN to commission the first assembly line and the quality controls (QCs). These were installed in the CMS detector in early 2017 and participated in the 2017 LHC run. The collaboration has prepared several additional assembly and QC lines for distributed mass production of 160 GEM detectors at various sites worldwide. In 2017, these additional production sites have optimized construction techniques and QC procedures and validated them against common specifications by constructing additional preproduction detectors. Using the specific experience from one production site as an example, we discuss how the QCs make use of independent hardware and trained personnel to ensure fast and reliable production. Preliminary results on the construction status of CMS GEM detectors are presented with details of the assembly sites involvement

Towards a decision-support framework for reducing ramp-up effort in plug-and-produce systems

Nowadays, shorter and more flexible production cycles are vital to meet the increasing customized product demand. As any delays and downtimes in the production towards time-to-market means a substantial financial loss, manufacturers take an interest in getting the production system to full utilization as quickly as possible. The concept of plug-and-produce manufacturing systems facilitates an easy integration process through embedded intelligence in the devices. However, a human still needs to validate the functionality of the system and more importantly must ensure that the required quality and performance is delivered. This is done during the ramp-up phase, where the system is assembled and tested first-time. System adaptations and a lack of standard procedures make the ramp-up process still largely dependent on the operator’s experience level. A major problem that currently occurs during ramp-up, is a loss of knowledge and information due to a lack of means to capture the human’s experience. Capturing this information can be used to facilitate future ramp-up cases as additional insights about change actions and their effect on the system could be revealed. Hence, this paper proposes a decision-support framework for plugand-produce assembly systems that will help to reduce the ramp-up effort and ultimately shorten ramp-up time. As an illustrative example, a gluing station developed for the European project openMOS is considered

Learning and reuse of engineering ramp-up strategies for modular assembly systems

YesWe present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.Funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project

Deep Underground Science and Engineering Laboratory - Preliminary Design Report

The DUSEL Project has produced the Preliminary Design of the Deep Underground Science and Engineering Laboratory (DUSEL) at the rehabilitated former Homestake mine in South Dakota. The Facility design calls for, on the surface, two new buildings - one a visitor and education center, the other an experiment assembly hall - and multiple repurposed existing buildings. To support underground research activities, the design includes two laboratory modules and additional spaces at a level 4,850 feet underground for physics, biology, engineering, and Earth science experiments. On the same level, the design includes a Department of Energy-shepherded Large Cavity supporting the Long Baseline Neutrino Experiment. At the 7,400-feet level, the design incorporates one laboratory module and additional spaces for physics and Earth science efforts. With input from some 25 science and engineering collaborations, the Project has designed critical experimental space and infrastructure needs, including space for a suite of multidisciplinary experiments in a laboratory whose projected life span is at least 30 years. From these experiments, a critical suite of experiments is outlined, whose construction will be funded along with the facility. The Facility design permits expansion and evolution, as may be driven by future science requirements, and enables participation by other agencies. The design leverages South Dakota's substantial investment in facility infrastructure, risk retirement, and operation of its Sanford Laboratory at Homestake. The Project is planning education and outreach programs, and has initiated efforts to establish regional partnerships with underserved populations - regional American Indian and rural populations

A Framework for Pilot Line Scale-up using Digital Manufacturing

Pilot lines are essential test-beds for process and product validation before the establishment of production lines. However, there is a lack of well-defined methodology for pilot line scale-up. To better support this transition, Virtual Models can be integrated with Discrete-Event Simulation (DES) models for potential production-line configurations. However, the validation of the developed models is hardly possible due to the absence of a physical counterpart. Therefore, this paper proposes a framework to increase the accuracy of the DES scale-up models with Virtual Modelling tools and Ontology. Subsequently, a test-case is used to explain the concept

The human performance impact on oee in the adoption of new production technologies

Featured Application This research work proposes a framework for the evaluation of the initial adoption phase of new production technologies and its application to the introduction of a semi-automatic packaging machine in a relevant logistics company. The case study allowed the assessment of the machine ramp-up phase and provided valuable insights for reducing the length of this period by achieving a stable target production output. Moreover, it shows how the framework can be adopted, applied and interpreted for obtaining useful insights. Manufacturing systems in digital and interconnected industrial settings where human worker activity is required represents further potential applications of this paper. The initial adoption phase of new production technologies is the period between the first production run or technology reconfiguration and the achievement of a stable target output. This time frame is generally characterized by productivity unsteadiness, quality performance variability, and unexpected machine failures together with increasing production volumes due to the process setup and instability, which inevitably affects production output. In this context, human performance represents an additional source of variability and process instability that is dependent on the workers' productivity, learning curve and related training activities. Hence, to effectively assess the ramp-up phase of new production technologies, an appropriate evaluation of human performance is required. This paper proposes a comprehensive framework and criteria to perform a consistent assessment of the initial adoption phase of new production technologies by introducing two OEE measurement methodologies that distinguish between human performance, process configuration and technical features of the production technology. The proposed framework is then applied to and validated by a case study concerning the introduction of a semi-automatic packaging machine in a primary multinational company in the logistics industry. This case study shows the difference between the two OEE measures, along with the values interpretation and useful insights for achieving a stable production output

Technical Design Report for the PANDA Solenoid and Dipole Spectrometer Magnets

This document is the Technical Design Report covering the two large spectrometer magnets of the PANDA detector set-up. It shows the conceptual design of the magnets and their anticipated performance. It precedes the tender and procurement of the magnets and, hence, is subject to possible modifications arising during this process.Comment: 10 pages, 14MB, accepted by FAIR STI in May 2009, editors: Inti Lehmann (chair), Andrea Bersani, Yuri Lobanov, Jost Luehning, Jerzy Smyrski, Technical Coordiantor: Lars Schmitt, Bernd Lewandowski (deputy), Spokespersons: Ulrich Wiedner, Paola Gianotti (deputy

Technical Design Report for the PANDA Solenoid and Dipole Spectrometer Magnets

This document is the Technical Design Report covering the two large spectrometer magnets of the PANDA detector set-up. It shows the conceptual design of the magnets and their anticipated performance. It precedes the tender and procurement of the magnets and, hence, is subject to possible modifications arising during this process

Understanding human decision-making during production ramp-up using natural language processing

Ramping up a manufacturing system from being just assembled to full-volume production capacity is a time consuming and error-prone task. The full behaviour of a system is difficult to predict in advance and disruptions that need to be resolved until the required performance targets are reached occur often. Information about the experienced faults and issues might be recorded, but usually, no record of decisions concerning necessary physical and process adjustments are kept. Having these data could help to uncover significant insights into the ramp-up process that could reduce the effort needed to bring the system to its mandatory state. This paper proposes Natural Language Processing (NLP) to interpret human operator comments collected during ramp-up. Recurring patterns in their feedback could be used to gain a deeper understanding of the cause and effect relationship between the system state and the corrective action that an operator applied. A manual dispensing experiment was conducted where human assessments in form of unstructured free-form text were gathered. These data have been used as an input for initial NLP analysis and preliminary results using the NLTK library are presented. Outcomes show first insights into the topics participants considered and lead to valuable knowledge to learn from this experience for the future