Manufacturing variation models in multi-station machining systems

In product design and quality improvement fields, the development of reliable 3D machining variation models for multi-station machining processes is a key issue to estimate the resulting geometrical and dimensional quality of manufactured parts, generate robust process plans, eliminate downstream manufacturing problems, and reduce ramp-up times. In the literature, two main 3D machining variation models have been studied: the stream of variation model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part, oriented to product and manufacturing design activities (manufacturing and product tolerance analysis and synthesis). This paper reviews the fundamentals of each model and describes step by step how to derive them using a simple case study. The paper analyzes both models and compares their main characteristics and applications. A discussion about the drawbacks and limitations of each model and some potential research lines in this field are also presented

A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes

Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure to detect the process fault based on a sequential testing algorithm and a minimum monitoring system is proposed. After the monitoring system detects that the process is out of statistical control, the features to be inspected (end of line or in process measurements) are defined sequentially according to the expected information gain of each potential inspection measurement. A case study is analyzed to prove the benefits of this approach with respect to a predefined inspection scheme and a randomized sequential inspection considering both the use and non-use of fault probabilities from historical maintenance data

Inspection by exception: a new machine learning-based approach for multistage manufacturing

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively

Variation propagation modelling for multi-station machining processes with fixtures based on locating surfaces

Modelling the dimensional variation propagation in multi-station machining processes (MMPs) has been studied intensively in the past decade to understand and reduce the variation of product quality characteristics. Among others, the Stream-of-Variation (SoV) model has been successfully applied in a variety of applications, such as fault diagnosis, process planning and process-oriented tolerancing. However, the current SoV model is limited to the MMPs where only fixtures with punctual locators are applied. Other types of fixtures, such as those based on locating surfaces, have not been investigated. In this paper, the derivation of the SoV model is extended to model the effect of fixture- and datum-induced variations when fixtures with locating surfaces are applied. Due to the hyperstatic nature of these fixtures, different workholding configurations can be adopted. This will increase the dimension of the SoV model exponentially and thus may make the model-based part quality prediction extremely complex. This paper presents a method of reducing the complexity of the SoV model when fixtures based on locating surfaces are applied and evaluates the worst-case approach of the resulting part quality

Analysis to Support Design for Additive Manufacturing with Desktop 3D Printing

[ES] En los últimos años, la fabricación aditiva a través de la extrusión de materiales ha experimentado un desarrollo y adopción acelerados gracias a la amplia disponibilidad de máquinas y materiales de bajo costo. El tamaño de estas máquinas se ha reducido del tamaño del taller al tamaño del escritorio, lo que permite su uso en configuraciones de oficina o en el hogar. Este cambio ha permitido la adopción de la tecnología por la gama más amplia de usuarios que nunca, con o sin experiencia en diseño de ingeniería. Este nuevo paradigma ha creado el desafío de cómo habilitar que estos nuevos usuarios aprovechen las capacidades proporcionadas por esta tecnología. Esta tecnología permite la creación de geometrías complejas y productos personalizados con un coste inferior a los procesos de fabricación convencionales. Además, la gran cantidad de usuarios dispuestos a compartir sus diseños permite encontrar soluciones de diseño desde otros diseñadores. Sin embargo, la amplia gama de configuraciones de máquina, parámetros y materiales requiere brindar soporte para obtener resultados exitosos para cualquier combinación. Esta tesis aborda este desafío identificando las características de diseño y fabricación a considerar e investigando las consideraciones mecánicas y de pos procesamiento. Se propone y evalúa un nuevo marco de diseño que permite a los nuevos usuarios aprovechar las capacidades y considerar las limitaciones. Esta investigación encuentra que es posible crear un conjunto de herramientas de diseño que permita a los usuarios no capacitados diseñar productos utilizando la complejidad habilitada por la tecnología al tiempo que garantiza la funcionalidad y la capacidad de fabricación del producto.[CA] En els últims anys, la fabricació additiva a través de l'extrusió de materials ha experimentat un desenvolupament i adopció accelerats gràcies a l'àmplia disponibilitat de màquines i materials de baix cost. La grandària d'aquestes màquines s'ha reduït de la grandària del taller a la grandària de l'escriptori, la qual cosa permet el seu ús en configuracions d'oficina o en a casa. Aquest canvi ha permés l'adopció de la tecnologia per la gamma més àmplia d'usuaris que mai, amb o sense experiència en disseny o enginyeria. Aquest nou paradigma ha creat el desafiament de com habilitar que aquests nous usuaris aprofiten les capacitats proporcionades per aquesta tecnologia. Aquesta tecnologia permet la creació de geometries complexes i productes personalitzats amb un cost inferior als processos de fabricació convencionals. A més, la gran quantitat d'usuaris disposats a compartir els seus dissenys permet trobar solucions de disseny des d'altres dissenyadors. No obstant això, l'àmplia gamma de configuracions de màquina, paràmetres i materials requereix brindar suport per a obtindre resultats reeixits per a qualsevol combinació. Aquesta tesi aborda aquest desafiament identificant les característiques de disseny i fabricació a considerar i investigant les consideracions mecàniques i de post processament. Es proposa i avalua un nou marc de disseny que permet als nous usuaris aprofitar les capacitats i considerar les limitacions. Aquesta investigació troba que és possible crear un conjunt d'eines de disseny que permeta als usuaris no capacitats dissenyar productes utilitzant la complexitat habilitada per la tecnologia al mateix temps que garanteix la funcionalitat i la capacitat de fabricació del producte.[EN] In recent years, additive manufacturing through material extrusion has experienced accelerated development and adoption thanks to the wide availability of low-cost machines and materials. The size of these machines has been reduced from shop floor to desktop size, enabling their usage in office setups or at home. This change has allowed the adoption of the technology by the broadest range of users than ever, with or without an engineering design background. This new paradigm has created the challenge of how to enable these novel users to leverage the capabilities provided by this technology. This technology allows the creation of complex geometry and customised products with a cost lower than conventional manufacturing processes. Furthermore, the large number of users willing to share their designs allows finding design solutions from other designers. However, the wide range of machine configurations, parameters and materials requires providing support to obtain successful results under any combination. This thesis addresses this challenge by identifying the design and manufacturing characteristics to be considered and investigating the mechanical and post-processing considerations. A new design framework that enables new users to leverage the capabilities and consider the limitations is proposed and evaluated. This research finds that it is possible to create a design toolkit that enables untrained users to design products using the complexity enabled by the technology whilst ensuring the product's functionality and manufacturability.Fernández Vicente, M. (2022). Analysis to Support Design for Additive Manufacturing with Desktop 3D Printing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185344TESI

A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing

Manufacturing is usually performed as a sequence of operations such as forming, machining, inspection, and assembly. A new challenge in manufacturing is to move towards Industry 4.0 (the fourth Industrial revolution) concerning the full integration of machines and production systems with machine learning methods to enable for intelligent multistage manufacturing. This paper discusses Multistage Manufacturing Processes (MMPs) and develops a probabilistic model based on Bayesian linear regression to estimate the results of final inspection associated with comparative coordinate measurement given in-process measured coordinates. The results of two case studies for flatness tolerance evaluation demonstrate the effectiveness of the probabilistic model which aims at being part of a larger metrology informatics system to be developed for predictive analytics and agent-based advanced control in multistage manufacturing. This solution relying on accurate models can minimise post-process inspection in mass production with independent measurements

A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes

The emergence of highly instrumented manufacturing systems has enabled the paradigm of smart manufacturing that provides high levels of prognostics functionality. Of particular interest is to precisely determine geometric conformance or non-conformance of workpieces during manufacturing. This paper presents a new dimensional product health monitoring system that learns from in-process sensor data and updates the prediction of the product quality as the product is manufactured. The system uses data from multiple manufacturing stages, unlike from a single stage at a time, to predict the dimensional quality of the finished product that is updated with subsequent measurements such as On-Machine Measurements (OMMs), in on-line incremental learning fashion. It is based on self-supervised neural networks for dimensionality reduction, Gaussian Process Regression (GPR) models for probabilistic prediction about the end product condition and the associated uncertainty, and Bayesian information fusion for updating the conditional probability distribution of the end product quality in the light of new information. The monitoring approach was tested on the prediction of diameter deviations with validation results showing its ability to achieve an average accuracy better than 5 μm in terms of the Root Mean Squared Error (RMSE). Having obtained a Probability Density Function (PDF) for the measurand of interest, the conformance and non-conformance probabilities given the tolerance specifications are computed to support the principle of inspection by exception. This ability to construct a conformance probability-based product quality monitoring system using probabilistic machine learning methods constitute a step change to manufacturing prognostics

System-level Quality Planning and Diagnosis for Complex Multistage Manufacturing Processes.

The performance of a multistage manufacturing process (MMP) can be measured by quality, productivity and cost. All these measures are influenced by the variation of the key product characteristics (KPC’s). To remain competitiveness, variation of KPC’s should be reduced to ensure efficient delivery of quality products. However, the unprecedentedly high requirements on quality make variation reduction a very challenging problem. To reduce KPC variation, massive data are generated and collected from different phases of product realization, including quantitative data and qualitative data. The heterogeneous data poses great challenges to traditional quality assurance methodologies, which emphasize monitoring of manufacturing processes but provide limited diagnostic information. Taking advantage of readily available data, this research focuses on system-level methodology for effective quality assurance of MMP’s in the following aspects: (i) A mathematical variation propagation model is developed to describe the process induced variation and its propagation along multiple manufacturing stages. The generic formulation makes it capable to model a wide variety of processes where 3-D dimensional variation is of interest. The modeling concept and techniques can be extended and applied in early phases of product realization to effectively evaluate product and process design alternatives. (ii) A quality assured setup planning methodology is developed to address the quality assurance in the process design phase of product realization. Setup planning is formulized as an optimal sequential decision making problem and is solved based on analytical evaluation. This research creates the potential for future works on concurrent development of system-level setup and fixture planning. The setup planning results can be further utilized for process diagnosis in the manufacturing phase of product realization. (iii) An engineering-driven factor analysis methodology is developed to diagnose an MMP based on qualitative rather than quantitative representation of product/process interactions. By using the qualitative indicator vectors to direct the estimation of true spatial patterns from multivariate measurement data, the variation sources are identified. The diagnostic results are robust to unknown process changes. The proposed methodologies represent the initial research efforts in a general framework of unified methodology for quality assurance of MMP’s. Based on them, future research directions are identified and discussed.Ph.D.Mechanical Engineering and Industrial and Operations EnginUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60659/1/jliuzz_1.pd

An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP