Real-time detection of grip length deviation for fastening operations: a Mahalanobis-Taguchi system (MTS) based approach

Hand-held fastening tools are extensively used in manufacturing, especially in aerospace industry. Typically, the process is monitored by the operator and joints are visually inspected after the process is completed. When complex products, such as an aircraft, are considered, fastening process and its inspection can be very time consuming. In addition, no inspection data is typically collected during the process unless a major problem is encountered. Real-time monitoring and verification of the fastening process of joint quality are two important advancements to reduce the manufacturing lead time while ensuring safety and quality --Abstract, page iv

A variability taxonomy to support automation decision-making for manufacturing processes

Although many manual operations have been replaced by automation in the manufacturing domain, in various industries skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realise the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realise the potential. This article describes a taxonomy of variability when considering automation of manufacturing processes. Three industrial case studies were analysed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making

A variability taxonomy to support automation decision-making for manufacturing processes

Although many manual operations have been replaced by automation in the manufacturing domain, in various industries skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realise the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realise the potential. This article describes a taxonomy of variability when considering automation of manufacturing processes. Three industrial case studies were analysed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making

Design for quality manufacturability analysis for common assembly process

The globalization of market economy has precipitated a dramatic increase in competition necessitating the need for higher quality products at lower cost in shorter time periods. Shorter life cycles and proliferation of products has made companies integrate all the phases of manufacturing to bring about a superior design. Design for Quality Manufacturability (DFQM) provides a technique to invoke manufacturing and assembly considerations while designing a product. The DFQM architecture identifies factors consisting of several variables that are influenced by certain error catalysts to cause one or more specific defects. A methodology is suggested to identify and quantify these error catalysts to be able to estimate the quality of the design. Some of the assembly processes that are widely used are insertion, riveting, welding, fastening, press-fit, and snap-fit. A detailed study of each of these processes is done to analyze the techniques, capabilities, and limitations. Using the DFQM architecture defect classes and specific defects are identified and analyzed. A correlation matrix is formed to identify the processes that are associated with each specific defect. Cause-Effect analysis using Ishikawa diagrams provide a means of analyzing the characteristics of the relevant processes attributing to each specific defect. These characteristics are grouped to identify the error catalysts that influence the occurrence of the specific defect

A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly

The objective of this research is to test the hypothesis that manufacturing complexity can reliably predict product quality in mixed-model automotive assembly. Originally, assembly lines were developed for cost efficient mass-production of standardized products. Today, in order to respond to diversified customer needs, companies have to allow for an individualization of their products, leading to the development of the Flexible Manufacturing Systems (FMS). Assembly line balancing problems (ALBP) consist of assigning the total workload for manufacturing a product to stations of an assembly line as typically applied in the automotive industry. Precedence relationships among tasks are required to conduct partly or fully automated Assembly Line Balancing. Efforts associated with manual precedence graph generation at a major automotive manufacturer have highlighted a potential relationship between manufacturing complexity (driven by product design, assembly process, and human factors) and product quality, a potential link that is usually ignored during Assembly Line Balancing and one that has received very little research focus so far. The methodology used in this research will potentially help develop a new set of constraints for an optimization model that can be used to minimize manufacturing complexity and maximize product quality, while satisfying the precedence constraints. This research aims to validate the hypothesis that the contribution of design variables, process variables, and human-factors can be represented by a complexity metric that can be used to predict their contribution on product quality. The research will also identify how classes of defect prevention methods can be incorporated in the predictive model to prevent defects in applications that exhibit high level of complexity. The manufacturing complexity model is applied to mechanical fastening processes which are accountable for the top 28% of defects found in automotive assembly, according to statistical analysis of historical data collected over the course of one year of vehicle production at a major automotive assembly plant. The predictive model is validated using mechanical fastening processes at an independent automotive assembly plant. This complexity-based predictive model will be the first of its kind that will take into account design, process, and human factors to define complexity and validate it using a real-world automotive manufacturing process. The model will have the potential to be utilized by design and process engineers to evaluate the effect of manufacturing complexity on product quality before implementing the process in a real-world assembly environment

Force and effort analysis of unfastening actions in disassembly processes

Fastening is the process of connecting one or more parts together with the aid of fastening elements. Unfastening, the reverse of fastening, is the process of separating components from each other by removing or detaching fastening elements. So far, the unfastening process is not well understood, and the analysis about it is not very extensive. However, the need for disassembly is currently increasing. First, parts have to be taken apart for service and repair, and secondly, for the recycling process. Therefore, there is a need to consider unfastening during the design process in order to enable efficient disassemblies. The purpose of this dissertation is to develop an analytical model, which enables unfastening analysis during the design of new products. Specifically, (i) a standard nomenclature for defining unfastening related parameters and variables is introduced, (ii) the U-Effort model for deriving the unfastening effort for a variety of commonly used fasteners is developed, (iii) the U-Effort model to model unfastening motion and hence estimate disassembly complexity is extended, and (iv) the U-Force model for estimating the required unfastening force in the case of cantilever and cylindrical snap fits is developed. The U-Effort model is a detailed study about the unfastening effort and the design attributes of commonly used fasteners. There is a difference between unfastening effort and unfastening force. Unfastening effort depends on several influencing factors, whereas the unfastening force is a more direct calculated value. The influencing attributes for the unfastening effort include the geometry and shape of the fastener and the condition at the end-of-life of the product. In the U-Force model, unfastening considerations are included in the design phase, mainly through the calculation of unfastening forces. The U-Force model is applied to the cantilever and cylindrical snap fit integral attachments. The U-Effort and the U-Force models can be used by designers to evaluate the unfastening suitability of new and existing product designs. Fastening elements can be selected based on functionality and the least unfastening effort. The developed models can assist industrial companies engaged in demanufacturing plan their recycling and reuse activities

Isolation forests and deep autoencoders for industrial screw tightening anomaly detection

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n 39479; Funding Reference: POCI-01-0247-FEDER-39479]. The work of Diogo Ribeiro is supported the grant FCT PD/BDE/135105/2017

Extracting, managing, and exploiting the semantics of mechanical CAD models in assembly tasks

The manufacturing of mechanical products is increasingly assisted by technologies that exploit the CAD model of the final assembly to address complex tasks in an automated and simplified way, to reduce development time and costs. However, it is proven that industrial CAD models are heterogeneous objects, involving different design conventions, providing geometric data on parts but often lacking explicit semantic information on their functionalities. As a consequence, existing approaches are mainly mathematics-based or need expert intervention to interpret assembly components, and this is limiting. The work presented in the thesis is placed in this context and aims at automatically extracting and leveraging in industrial applications high-level semantic information from B-rep models of mechanical products in standard format (e.g. STEP). This makes possible the development of promising knowledge intensive processes that take into account the engineering meaning of the parts and their relationships. The guiding idea is to define a rule-based approach that matches the shape features, the dimensional relations, and the mounting schemes strictly governing real mechanical assemblies with the geometric and topological properties that can be retrieved in CAD models of assemblies. More in practice, a standalone system is implemented which carries out two distinct operations, namely the data extraction and the data exploitation. The first involves all the steps necessary to process and analyze the geometric objects representing the parts of the assembly to infer their engineering meaning. It returns an enriched product model representation based on a new data structure, denoted as liaison, containing all the extracted information. The new product model representation, then, stands at the basis of the data exploitation phase, where assembly tasks, such as subassembly identification, assembly planning, and design for assembly, are addressed in a more effective way