Tool wear classification using time series imaging and deep learning

Abstract: Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%

XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications

Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and sped-up process changes. In particular, the use of the digital twin in a manufacturing environment makes it possible to test such approaches in a timely manner using a realistic 3D environment that limits incurring safety issues and danger of damage to resources. To obtain superior performance in an industry 4.0 setup, a modified version of a binary gravitational search algorithm is introduced which benefits from an exclusive or (XOR) operator and a repository to improve the exploration property of the algorithm. Mathematical analysis of the proposed optimization approach is performed which resulted in two theorems which show that the proposed modification to the velocity vector can direct particles to the best particles. The use of repository in this algorithm provides a guideline to direct the particles to the best solutions more rapidly. The proposed algorithm is evaluated on some benchmark optimization problems covering a diverse range of functions including unimodal and multimodal as well as those which suffer from multiple local minima. The proposed algorithm is compared against several existing binary optimization algorithms including existing versions of a binary gravitational search algorithm, improved binary optimization, binary particle swarm optimization, binary grey wolf optimization and binary dragonfly optimization. To show that the proposed approach is an effective method to deal with real world binary optimization problems raised in an industry 4.0 environment, it is then applied to optimize the assembly task of an industrial robot assembling an industrial calculator. The optimal movements obtained are then implemented on a real robot. Furthermore, the digital twin of a universal robot is developed, and its path planning is done in the presence of obstacles using the proposed optimization algorithm. The obtained path is then inspected by human expert and validated. It is shown that the proposed approach can effectively solve such optimization problems which arises in industry 4.0 environment

Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%

Semantic models and knowledge graphs as manufacturing system reconfiguration enablers

Reconfigurable Manufacturing System (RMS) provides a cost-effective approach for manufacturers to adapt to fluctuating market demands by reconfiguring assets through automated analysis of asset utilization and resource allocation. Achieving this automation necessitates a clear understanding, formalization, and documentation of asset capabilities and capacity utilization. This paper introduces a unified model employing semantic modeling to delineate the manufacturing sector's capabilities, capacity, and reconfiguration potential. The model illustrates the integration of these three components to facilitate efficient system reconfiguration. Additionally, semantic modeling allows for the capture of historical experiences, thus enhancing long-term system reconfiguration through a knowledge graph. Two use cases are presented: capability matching and reconfiguration solution recommendation based on the proposed model. A thorough explication of the methodology and outcomes is provided, underscoring the advantages of this approach in terms of heightened efficiency, diminished costs, and augmented productivity

Towards Modular and Plug-and-Produce Manufacturing Apps

Industry 4.0 redefines manufacturing systems as smart and connected systems where software solutions provide additional capabilities to the manufacturing equipment. However, the connection of manufacturing equipment with software solutions is challenging due to poor interoperability between different original equipment manufacturers (OEMs), making it difficult to integrate into the manufacturing system. Hence, there is a need for a methodology to develop modular "plug-and-produce" applications in the manufacturing domain to meet the requirements of Industry 4.0. This work investigates the "appification" of manufacturing processes where the goal is to subdivide the process into independent, re-configurable digital manufacturing applications. In this context, "appification" means separating the digital implementation from the physical implementation of the system by making the former modular and independent so that digital implementations can be re-used without depending on the physical parts of the system. In this paper a framework for the development of such manufacturing "apps" is presented. This framework consists of four main elements: a modular plug-and-produce architecture, a manufacturing apps development kit, a communication protocol, and a construction methodology. The modular plug-and-produce architecture is developed using the recent advances in microservices, containerization, and communication technologies. The manufacturing apps development kit (MAPPDK) has been developed to facilitate the implementation of manufacturing apps using high-level programming languages. MAPPDK allows to control manufacturing equipment from external computational devices. The methodology for developing different modules for different types of manufacturing processes is also provided. The proof of concept is shown experimentally by the "appification" of a sorting process using an industrial robot arm, a gripping end-effector, a third-party vision camera, and an intelligent vision module

Orientaciones específicas para la incorporación de tecnología en procesos de formación de profesores de Ciencias Naturales, Lenguaje y Comunicación, y Matemáticas en contextos de diversidad para el diseño de secuencias de enseñanza aprendizaje

Las Tecnologías de la Información y de la Comunicación (TIC) se han transformado en un elemento de innovación dentro del conocimiento humano. Aplicadas directamente a la disciplina académica, las TIC se han ganado un espacio debido al creciente interés de este tipo de herramientas por parte de los docentes. No obstante, al día de hoy, en Latinoamérica el uso de TIC no es masivo. Su implementación aún está en una etapa inicial, y esto es debido a diversos factores. Algunos tienen que ver con el poco seguimiento que se realiza al implementar este tipo de tecnologías en las instituciones educativas; la limitada formación es una debilidad. En una escuela, el profesor que no ha sido preparado adecuadamente en el uso de TIC difícilmente logrará utilizarlas adecuadamente, y sus metodologías de aula serán, probablemente, idénticas a las que existían previas a su implementación. Existe una gran diferencia entre conocer una herramienta y dominarla; el dominio se logra, por ejemplo, al conocer las ventajas y desventajas que posee una herramienta determinada en conexión directa con los contenidos y las necesidades de los estudiantes. El dominio de las TIC otorga al docente de aula la posibilidad de tomar decisiones que tiendan a la efectividad del proceso educativo, puesto que las TIC no son solo un conjunto de herramientas, sino que, además, son orientables hacia un área u otra dependiendo de factores variables propios del proceso. Teniendo en cuenta el contexto anterior, este documento contiene algunas orientaciones para el uso del material desarrollado en el área de Ciencias Naturales, Lenguaje y Comunicación, y Matemáticas del Proyecto ALTER-NATIVA, el cual va dirigido a la formación de profesores que atienden poblaciones en contexto de diversidad con incorporación de las TIC. Es importante anotar que las actividades que se proponen son una posibilidad distinta de asumir la enseñanza de las ciencias naturales, lo cual a su vez implica reflexión y evaluación constante de la práctica de los docentes de esta área; que los compromete también con los desarrollos tecnológicos actuales, lo cual conlleva analizar y canalizar sus esfuerzos para diseñar y gestionar mecanismos que permitan la incorporación de las TIC en sus prácticas, pues se considera que estas posibilitan colaborar en la construcción de mundos posibles cercanos a los ideales de participación, igualdad y equidad (Alter -nativa , 2012). Estas orientaciones específicas tienen un doble propósito: primero, cobijan algunas recomendaciones, las cuales son una propuesta de uso para aprovechar al máximo el material que se propone en el estudio de las Ciencias Naturales, Lenguaje y Comunicación, y Matemáticas, para las poblaciones objeto de estudio. Para el desarrollo de las actividades planteadas es necesario que los estudiantes para profesor y profesores de escuelas, colegios y universidades de las áreas de Ciencias Naturales, Lenguaje y Comunicación, y Matemáticas, como posibles usuarios de este material, las perciban como un recurso que tiene un potencial y que, para su desarrollo, se hace necesario tener presente el diseño y estructura didáctica, los objetivos temáticos, la infraestructura, los recursos tecnológicos, las necesidades y características de las poblaciones con quien se vaya a utilizar, entre otros aspectos. Y segundo, estas orientaciones tienen como propósito ofrecer elementos para la formación de profesores de Ciencias Naturales, Lenguaje y Comunicación, y Matemáticas en su acción pedagógica y didáctica; se involucran las prácticas profesionales de diseño y elaboración de objetos didácticos y de los objetos virtuales de aprendizaje (OVA) como casos específicos de estos. Tomando como referencia lo anterior, se plantea como objetivos central de este texto el presentar algunas orientaciones que es necesario tener en cuenta a la hora incorporar TIC en la formación de profesores de Ciencias Naturales, Lenguaje y Comunicación, y Matemáticas. De igual forma, unos objetivos específicos serían los siguientes: a) Establecer unas orientaciones que sean consideradas a la hora de usar los recursos virtuales como los Objetos Virtuales de aprendizaje (OVA) que fueron diseñados como parte del proyecto ALTER-NATIVA en el área de ciencias naturales, lenguaje y matemáticas. b) Favorecer el aprendizaje de las ciencias naturales, lenguaje y matemáticas en las poblaciones objeto de estudio a través del uso de las TIC. c) Resaltar la importancia que tiene el uso de las TIC para concebir el aprendizaje como un sistema de interacción y no como transmisión de información solamente. d) Aportar a la reflexión y formación de docentes con una cultura tecnológica que les permita afrontar su labor pedagógica y didáctica en ámbitos de diversidad. e) Proporcionar unos elementos teóricos y de uso de las TIC que les admita la planeación, el diseño, desarrollo, uso y evaluación de objetos virtuales u OVA dentro de un ambiente virtual de aprendizaje

A data analytics model for improving process control in flexible manufacturing cells

With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology based on data analytics that simplifies the monitoring process for the operator, allowing the practitioner to concentrate on the relevant issues, anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine’s settings are needed (tool replacement, repositioning the axis, etc.). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.Engineering and Physical Sciences Research Council (EPSRC): EP/R511730/1 and EP/R032777/

Online and Modular Energy Consumption Optimization of Industrial Robots

Industrial robots contribute to a considerable amount of energy consumption in manufacturing. However, modeling the energy consumption of industrial robots is a complex problem as it requires considering components such as the robot controller, fans for cooling, the motor, the friction of the joints, and confidential parameters, and it is difficult to consider them all in modeling. Many authors investigated the effect of operating parameters on the energy consumption of industrial robots. However, there is no prescriptive methodology to determine those parameter values because of the challenges in the modeling of industrial robots. This work investigates an industrial robot and the manufacturing process together and proposes a black-box model-based energy consumption optimization approach. Our contribution to the research is the new online and data-efficient methodology, prescriptive algorithm, and the analysis of operating parameters' effects on industrial robots' energy consumption. The proposed methodology was tested using two real FANUC industrial robots in three industrial settings