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This paper introduces a simulation-based approach to enhance quality defect detection in a prominent French textile company's manufacturing process. The production workflow is initially simulated and analyzed to identify key parameters influencing defect creation and detection. After assessing the existing control process using performance metrics, a novel Inspection Quality Plan (IQP) is formulated. To validate the effectiveness of the proposed IQP, a simulation model is developed and calibrated to mirror real-world conditions. This model, initially validated against the current solution, acts as a benchmark for evaluating predefined scenarios and determining optimal configurations. The implemented scenario, tested in the workshop under authentic conditions, results in a remarkable 42% improvement in defect detection within the knitting process. The positive outcomes are meticulously examined and discussed. This study, employing a holistic approach that integrates simulation modeling, performance evaluation, and real-world deployment, establishes an optimized strategy for enhancing defect detection in the textile industry. It underscores the effectiveness of the proposed IQP, showcasing significant improvements observed in practical settings based on a simulation model. [ABSTRACT FROM AUTHOR]

Freeform surfaces find potential applications in optics but pose new challenges for manufacturing and functional testing. A basic description of such surfaces is observed in terms of surface spatial frequencies which have been altered as a result of evolution in the fabrication of optics. While mid-spatial-frequency (MSF) errors arising from manufacturing and metrology processes are well documented, our investigation reveals that such errors originate not only during these stages but also during the surface construction at the design phase, particularly for high-order XY polynomial optical surfaces. The presence of MSF errors can significantly affect the performance of high-performance optical systems across various applications. The study discussed in this paper focuses on the relationship between the resolution of the design dataset of freeform surfaces with high-order polynomials and the subsequent surface imperfections in the production of precision optics. The main contributions of this investigation are the identification of MSF errors at surface construction of the design phase and the control over the MSF errors in design dataset using the 2nd-order Gaussian filtration which will bridge the gap between freeform optics design and ultra-precision manufacturing. [ABSTRACT FROM AUTHOR]

This work presents a measurement for the identification of translational quasi-static stiffness of machine tool using Stiffness Workspace System (SWS). The novelty of this work is a significant modification of the SWS. The changes in measurement procedure and data analysis as well as new technical solutions are described in detail. A methodology for generalised translational static stiffness determination is presented. The major purpose of this paper is to provide the information about quasi-static stiffness values that characterise a typical machine tool. The measurement procedure is implemented in a case study on a 5-axis machining centre. Measurement results were used to determine the generalised translational stiffness indicators of points in the workspace. Then, the static stiffness distribution on the XY plane over machining space was estimated. The obtained results confirm the importance of analyses of static stiffness distribution as they point out areas where some possible changes occur. In the conclusion of the paper the utility value of the information of translational static stiffness is emphasised. [ABSTRACT FROM AUTHOR]

Today, additive manufacturing (AM) is being utilized in a plethora of fields and applications. This study investigates two of the most commonly used AM technologies, fused filament fabrication (FFF) and stereolithography (SLA), under different part complexities and their effects to finished part quality. The process parameters investigated are infill density (ID), infill pattern (IP), and layer height (LH). The output variables observed are power consumption (PC), surface roughness (SR), and mass. Statistical analysis of experimental data indicates that LH is the most influential parameter for all output variables except mass. A validation study is later performed using three specimens with more diversely complex geometries. The model complexity is then evaluated and analyzed using three different ratios. Normalizing PC with respect to volume indicates a lower PC per unit volume for FFF. Experimental data also indicates SLA has a better surface finish and uses less power. This paper reports the state-of-the-art FFF and SLA process knowledge blocks created through this research study. [ABSTRACT FROM AUTHOR]

Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence (AI) models, but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameters configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field. [ABSTRACT FROM AUTHOR]

This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required. [ABSTRACT FROM AUTHOR]

Industry and research have long desired the establishment of standards for weldability testing in regards to hydrogen-assisted cold cracking formation. This would have the obvious advantage of allowing data to be reliably compared between different research labs. But making decisions regarding standards requires some careful thought and agreement on i) how test parameters affect test results, ii) what exactly needs to be measured, and iii) how test results should be interpreted and reported. Our depth of understanding on these points has matured significantly over time and, while there is not always universal agreement, it is at least possible to start highlighting factors important to standards. This paper examines these factors, including the welding parameters, restraint, hydrogen, and cracking index. When comparing different alloys having different thermal characteristics, the use of constant welding parameters (common practice) will result in variable weld penetration and weld pool shape, which can influence grain shape and microstructural features, which can result in inequitable weldability comparisons. Welding on test coupons having different dimensions can affect restraint, which will influence the residual stresses around the weldment. High restraint usually results in higher crack susceptibility. Also, hydrogen content present in a weldment depends on the thermal history, welding parameters, and surrounding atmosphere humidity, with high hydrogen contents associated to great cracking susceptibility. Finally, the selection of an appropriate cracking index is required for data analysis. Quantifications of crack length and minimum preheat temperature are common indexes used for comparison. Critical stress and hydrogen content are other indexes. But how well these indexes actually represent weldability are contentious issues. This paper will examine and quantify these issues in detail, thus providing the reader with an appreciation of all things that must be considered when preparing a standardized procedure for weldability testing. [ABSTRACT FROM AUTHOR]

In assembly activities, performing assembly planning is a crucial issue for the human-centered manufacturing. The challenges involve in retrieving and utilizing the real-time data about human-based work activities in a shop floor. Instead of the simulation-based assembly planning, the marker-based motion capture system can acquire realistic motion data of workers in assembling sites, but this method is inclined to corrupt due to the occlusion and is troublesome to be installed within the shop floor. Therefore, based on the complement of the optical and inertial sensor, this paper presents a self-contained motion capture method for assembly planning in real shop floor. It can provide a real-time and portable motion capturing for workers, avoiding the failure of traditional outside-in motion capture system due to occlusions or incorrect installations. What is more, the portable motion capture method can run on consumer mobile devices, providing a convenient and low-cost way to perceive the workers' motion in shop floor, which is significant for extensive applications in assembly verification and planning for digital factories. Finally, experiments are carried out to demonstrate the accuracy and feasibility of the proposed motion capture method for assembly activities. [ABSTRACT FROM AUTHOR]

Feedrate planning is crucial for CNC systems to generate a smooth and fast movement, which is closely related to the machining quality and machining efficiency. In this paper, a new method for adaptive feedrate planning is proposed for parametric tool path with respecting geometric and kinematic constraints for CNC machining. The mathematic relationships between the prescribed constraints and tool tip feedrate are first formulated. Then the feedrate upper bound at any curve parameter is derived, and the tool path is adaptively discretized into discrete sampling positions and corresponding maximum feedrates are calculated. A linear mathematic model for linear programming is proposed to obtain the optimal square values of feedrates at each sampling position. After solving the linear model by a well-developed linear programming algorithm, the optimal feedrates at each sampling position can be calculated. Finally, the optimal discrete feedrate data are fitted to a smooth spline curve as the ultimate feedrate profile, which respects all prescribed constraints. Experiments are conducted on two parametric tool paths to show the merits of the proposed method. Results show that the proposed method are feasible and applicable, the generated feedrate profiles of the two tool paths respect all the prescribed constraints. [ABSTRACT FROM AUTHOR]

Conducting experiments to understand and model a complex process or system is usually costly and time-consuming due to multistages, multivariables, and multidisciplinary issues involved in the complex process. To reduce the complexity, for a single experiment, experimenters often fix some variables and investigate the effects of a smaller subset of variables. If then, it is possible to build individual models for each subset of variables, but this only allows partial understanding of the whole process. In this paper, we propose a method for building a holistic model of a complex process using multiple partial models that are learned from multiple sub-experiments that focus on different variables or the same variables but with different variable ranges. Using the proposed holistic model, it should be possible to provide an initial understanding of the complex process involving all variables. The effectiveness of the proposed method is demonstrated using a real example from a buckypaper process. [ABSTRACT FROM AUTHOR]

Nowadays, machining process information from CAPP cannot yet be directly interpreted and utilized in CAM systems. It is the NC programmers who decipher the information and link up CAPP and CAM, which is inefficient. In order to reduce the tool path editing and programming time, this paper describes the implementation of an automatic tool path generation from the structuralized machining process (SMP). An intelligent agent named KMAIMOD was developed to automate SMP analysis and tool path generation based on operation controlled template (OCT) and feature machining volume semantics (FMVS). The framework of CAD/CAPP/CAM integration was introduced for automatic tool path generation. Then the working principle and machining process data analysis of KMAIMOD was proposed. And three categories of FMVS construction with machining elements were discussed to automatic determine machining position and range. In the experiments, some part models are utilized to verify the approach for automatic tool path generation, and the experimental results are analyzed at length to show the application effectiveness of the method. [ABSTRACT FROM AUTHOR]

The main aim of this research is to analyze the differences between cellular manufacturing and cellular disassembly systems. While doing so, this paper makes a relevant contribution. It proposes a mathematical programming formulation for robust manufacturing and disassembly cell formation problems. A solution method based on the Tabu Search schema is adapted for both problems and will be used for the comparison analysis. The fact that the Tabu Search algorithm has been effective in different cell formation approaches gives us confidence in its reliability, and although alternative metaheuristic approaches may be developed in the future, we opted for adapting this algorithm to the problem at hand, since our first aim is to obtain data to analyze the difference in the design of both types of production environments and not to develop new approaches to the cell formation problem. A full factorial design study is proposed in order to appraise the significance of controllable factors in cellular designs. Four dependent variables were considered in the experiments: total cost, size of the cells, number of intercellular moves, and machine utilization. The issue here is to see what happens with the solutions for each production environment (manufacturing and disassembly) when varying factors, in order to analyze the differences and similarities between both. [ABSTRACT FROM AUTHOR]

The cutting of high hardness composite materials such as sintered carbides based on the cobalt is a challenge from a technological point of view. The paper presents the influence of cutting data ( v, f, a) and cobalt content in a work piece (10, 15, and 25 wt%) on the turning process of WC-Co with polycrystalline diamond (PCD) tool. This study focuses on the main component of cutting force F and surface roughness, described by the roughness parameter Ra. The research plan, based on the Taguchi method, and variance analysis ANOVA were applied. Two types of experimental model, which describes turning process of sintered carbides based on the power function for three variables and polynomial functions as a modified method of the response surface methodology (RSM), were also presented. [ABSTRACT FROM AUTHOR]

Over the last few decades, sheet metal parts have had a number of applications in the industry. The most important and most used type of the dies to production these parts is progressive dies. Computer-aided design of these dies consists of a considerable number of sections. The two most important types of these sections are nesting and piloting. Finding the best nesting plays an important role in the final cost of production and for an accurate strip positioning, the appropriate piloting system is required. In this paper, a new method for curve recognition by dividing part's element into component points is presented for finding the best nesting and piloting state. Curve recognition data and the algorithms for finding the best nesting and piloting system were obtained just by using algebraic equations. This method leads to a decrease in the time of design. The best nesting for all parts that includes line and circular arc will be obtained with the high precision. The semi-direct and indirect pilot hole centers and its diameter will be obtained by defining four matrixes. This system is written in visual basic programming language in the SOLIDWORKS 2010 software. Finally, to compare the system's results to similar system, the nesting and piloting systems for the number of samples are analyzed. [ABSTRACT FROM AUTHOR]

Feature selection from imbalance data plays an important role in building efficient support decision systems, improving the machine learning process performance and enhancing the classification accuracy. The problem of feature selection becomes even more difficult with imbalance data, which occurs in real-world domains when the classes representing the data set are not equally distributed. Using the traditional classifiers to seek an accurate performance over a full range of instances is not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into one class. In this paper, the Mahalanobis genetic algorithm (MGA) classifier is proposed to address the problem of feature selection for imbalance welding data. The MGA classifier was benchmarked with the Mahalanobis-Taguchi system (MTS) classifier, in terms of the following metrics: the total misclassification errors, the area under the curve (AUC) for receiver operating characteristic (ROC) curves, and the signal-to-noise (S/N) ratio. A real-life data set from the spot welding process was used as a pilot study. The results in terms of the total misclassification error and the AUC metrics showed that the MGA had better classification performance than MTS. Very close results were obtained when the training data set was balanced by using the Synthetic Minority Oversampling Technique (SMOTE) which indicates the suitability of the MGA and MTS classifiers to be used for the imbalance data set without using any preprocessor approach. Regarding the S/N ratio, the results were inconsistent with the other classification metrics, which raises the question about its credibility. [ABSTRACT FROM AUTHOR]

Three-roll cross rolling (TRCR) is a new advanced plastic forming technology to manufacture rings with small hole and deep groove (RSD), such as duplicate gear. Force parameters are the important basis for plastic forming technology design and equipment selection. So, it is necessary to establish a rational method of calculating force parameters. As the deformation process of TRCR is controlled by multifactors coupling interactive effects, to establish a universal design approach for comprehensive consideration of various parameters which affect the force and energy parameters, considering various kinds of factors with finite element analysis (FEA) method, a great quantity model and data analysis must be developed, which is more time-consuming and difficult to summarize the effective rule. While based on related supposition and simplified calculation, a common analytical method can be adopted quickly with the traditional plastic mechanics theory. Therefore, analytical models of rolling power, torque, and force are built in the paper, by means of the classic plastic mechanics theory of upper bound method combined with static analysis and calculation of kinematical admissible velocity field. Then, comparing to the results in FEA models of different sizes of workpieces and kinematic parameters of rolls under ABAQUS/Explicit environment, analytical models are corrected. As a consequence, the effect rules of workpiece sizes and roll kinematic parameters on force parameters display the same, and corrected analytical model has a great agreement with FEA, which provides reliable calculation basis for the design of force parameters in TRCR of RSD. [ABSTRACT FROM AUTHOR]

This paper presents the development and realization of the ' Integrated system for analyzing Maintenance records in Product improvement' (IMaPro). IMaPro has been designed based on research and need analysis through literature survey and consultation with industry partners in Germany. The primary goal is to analyze structured feedback data, such as condition monitoring, service, and customer data, especially to discover improvement potentials in maintenance management and ultimately product improvement. In this context, Bayesian network utilizes the knowledge-based analysis of feedback data. The Bayesian network is used in combination of a mathematical cost model. The model supports cost-based monitoring and controlling of maintenance activities, and consequently leads to the identification of the lack and applying the lessons learned for improving maintenance costing. The concept has been validated and pilot-tested through the use of a sample product, namely block heater system. Furthermore, IMaPro incorporates an in-house development of mobile application for acquisition of service data. [ABSTRACT FROM AUTHOR]

Most of industrial applications of statistical process control involve more than one quality characteristics to be monitored. These characteristics are usually correlated, causing challenges for the monitoring methods. These challenges are resolved using multivariate quality control charts that have been widely developed in recent years. Nonetheless, multivariate process monitoring methods encounter a problem when the quality characteristics are of the attribute type and follow nonnormal distributions such as multivariate binomial or multivariate Poisson. Since the data analysis in the latter case is not as easy as the normal case, more complexities are involved to monitor multiattribute processes. In this paper, a hybrid procedure is developed to monitor multiattribute correlated processes, in which number of defects in each characteristic is important. Two phases are proposed to design the monitoring scheme. In the first phase, the inherent skewness of multiattribute Poisson data is almost removed using a root transformation technique. In the second phase, a method based on the decision on belief concept is employed. The transformed data obtained from the first phase are implemented on the decision on belief (DOB) method. Finally, some simulation experiments are performed to compare the performances of the proposed methodology with the ones obtained using the Hotelling T and the MEWMA charts in terms of in-control and out-of-control average run length criteria. The simulation results show that the proposed methodology outperforms the other two methods. [ABSTRACT FROM AUTHOR]

A major role in the process of quality assurance of cast parts is the analysis of porosities. In this paper, a method providing an evaluation technique of the available 3D data offered by the new computed tomography (CT) technology is developed, describing how the material's strength at the location of the porosity gets attenuated. The steps in achieving this goal are as follows: First, the multitude of tensile testing results of porosity-afflicted samples at hand is to be studied giving rise for quantitative descriptions of the main influential factors of the material's degradation, like the porosity shape, the position, the minimum radius, the distance to surface, etc. Then, the second step is to build up a numerical testing setup, facilitating to modify these parameters independently of each other utilizing characteristic porosity geometries. After validation of this model in regard of the known experimental results, at third, a design of experiments (DOE) has to be performed on it revealing the main dependencies of the shape's properties on the stress concentration factors. Eventually, the applied approach is evaluated for prediction of fatigue life and fracture plane position of porous material. [ABSTRACT FROM AUTHOR]