Virtual In-line Inspection for Function Verification in Serial Production by means of Artificial Intelligence

In high-tech production, companies often deal with the manufacture of assemblies with quality requirements close to the technological limits of manufacturing processes. The article shows an approach of a virtual in-line inspection, predicting the products functionality. An artificial neural network (ANN) fed with product characteristics and process data as well as the resulting functional fulfillment of the product is trained for virtual function prognosis. Through the preventive identification of defective products before the final assembly step, components can be recovered and returned to serial production. By optimizing the parameters of the ANN, incorrect classifications are reduced and the practical applicability is ensured. The approach is demonstrated in an industrial application in the automotive industry

Fluid Automation - A Definition and an Application in Remanufacturing Production Systems

Production systems must be able to quickly adapt to changing requirements. Especially in the field of remanufacturing, the uncertainty in the state of the incoming products is very high. Several adaptation mechanisms can be applied leading to agile and changeable production systems. Among these, adapting the degree of automation with respect to changeover times and high investment costs is one of the most challenging mechanisms. However, not only long-term changes, but also short-term adaptations can lead to enormous potentials, e.g. when night shifts can be supported by robots and thus higher labor costs and unfavorable working conditions at night can be avoided. These changes in the degree of automation on an operational level are referred to as fluid automation, which will be defined in this paper. The mechanisms of fluid automation are presented together with a case study showing its application on a disassembly station for electrical drives

Trust-UBA: Detecting trust from the trustor's voice

The protocol of the dataset consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the agent's skills, they are first informed that it was previously rated by other users as being either good or bad; subsequently, the agent answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and agents communicating verbally, which allows the recording of speech produced under different trust conditions.Fil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Gravano, Agustin. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Riera, Pablo Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Pepino, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Gauder, María Lara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentin

Towards detecting the level of trust in the skills of a virtual assistant from the user's speech

Research has shown that trust is an essential aspect of human–computer interaction directly determining the degree to which the person is willing to use a system. An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions. With this goal in mind, in this work we aim to explore the feasibility of automatically detecting the technical competence or ability of a virtual assistant (VA) from the user's speech, a simple proxy for the task we truly care about: detecting whether the user trusts the ability of the VA. Since, to our knowledge, no public databases were available to perform such study, we developed a novel protocol for collecting speech data from subjects interacting with VAs with different skill levels. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a VA. At the beginning of each session, subjects are informed that the VA they are going to use has been previously rated by other users as being either competent or incompetent. During the session, the VA answers the subjects’ questions consistently to its alleged ability. All interactions are speech-based, with subjects and VAs communicating verbally, which allows the recording of speech produced under different conditions. The goal of the protocol was to induce subjects to either trust or distrust the VA's skills, assuming that this would, in turn, affect their speech patterns in ways that could be automatically detected. Using this protocol, we collected a speech corpus in Argentine Spanish which is publicly available for research use. We show clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills. Using the collected data, we developed a system to detect the ability of the VA with which a subject interacted during a session, based on the subject's speech patterns. We found that it was possible to detect whether the VA was competent or incompetent with an accuracy up to 76%, compared to a random baseline of 50%. Our analysis suggests that these results are possible because the subjects change the way they speak to the VA depending on whether they perceive it as more or less competent; that is, depending on whether they trust its ability or not.Fil: Gauder, María Lara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Pepino, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Riera, Pablo Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Brussino, Silvina Alejandra. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentina. Universidad Nacional de Córdoba. Facultad de Psicología; ArgentinaFil: Vidal Dominguez, Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Gravano, Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella; ArgentinaFil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentin