Manufacturing-Oriented Discrete Process Modeling Approach Using the Predicate Logic

: Manufacturing process models are developed for a data-driven, reconfiguarable manufacturing facility. A human assembly process model is developed to characterize assembly processes. Machine and tool resources are characterized for manufacturing processes to provide a broad characterization of manufacturing process coverage in a hypothetical final assembly factory for infantry fighting vehicles. A sample population of manufacturing process parameters and resources is collected and incorporated into a manufacturing capability and process model library. Requirements for a virtual manufacturing environment are developed, and an initial, prototype version is implemented to facilitate process model validation, configuration, and experimentation.

This paper describes an on going research by the authors who are seeking methods to integrate an earlier developed manufacturing process modeling method in the virtual manufacturing (VM) technology. The primary aim is to attach special manufacturability evaluation to modeling of mechanical products. A more or less generic process model is generated for a cluster of manufacturing tasks then manufacturing process entities are created by evaluation of the manufacturing process model using part, form feature, production environment, production planning and cost information. Petri net manufacturing process model representation is used. It has proved to be a versatile virtual manufacturing tool in manufacturing process planning, evaluation of manufacturability and in manufacturing environment design. A process model evaluation procedure is emphasized in the course of which feasibility and resource requirements of the manufacturing are revealed. Firstly, techniques of evaluation of manufacturability, the applied approach to integration of shape and manufacturing process modeling and the related virtual manufacturing concepts are introduced. The process model is outlined and earlier relevant publications of the authors are cited. Then problems and solutions at evaluating of process model in close connection with design and environmental changes are detailed. Following this, integration of the method into a comprehensive virtual manufacturing concept is explained. Finally, main issues and future research plans are concluded.

In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

Sustainability indicators for discrete manufacturing processes applied to grinding technology

Digital twins for electro-physical, chemical, and photonic processes

This paper proposes fuzzy Colored Petri Nets with stochastic time delay (FCPN-std) as one of methodologies of incorporating qualitative uncertainties and stochastic information in a manufacturing system. As a combined model with fuzzy colored Petri Nets (FCPNs) and stochastic Petri Nets (SPNs), it captures and models statistical and qualitative uncertainties in manufacturing processes. A parametric modular design methodology is proposed for effective design of complex processes. A mold producing process in digital camera making industry is modeled as an example application. The model incorporating uncertain and stochastic information is represented using extended Petri Net Markup Language (xPNML) architecture. xPNML formatted fuzzy colored Petri Net with stochastic time delay is shown as an effective design methodology and representation tool for describing manufacturing processes and capturing uncertainties.

Industrial use of natural resources are increasing at an alarming rate. Engineering and decision support tools are needed for analyzing and curbing industrial consumption of resources. Further, assessment methods to measure and indicate continuous improvement are also needed. Modeling individual manufacturing processes facilitates the generation of quantifiable evidence that improvements are being made. Such a modeling approach is developed and demonstrated in this paper to characterize sustainability performance of two metals inspection processes: magnetic particle inspection for ferromagnetic alloys and penetrant inspection for non-ferromagnetic alloys. Individual unit manufacturing process (UMP) models were developed by observing the inspection practices at an aircraft component manufacturer, and a mathematical basis for comparison with other inspection processes was identified. The paper further demonstrates the aggregation of performance metrics from all UMPs across a manufacturing process flow thus providing a basis for generating detailed sustainability performance assessments of manufactured products. By developing and documenting a comprehensive set of UMP models, more complete knowledge of manufacturing processes can be gained by industry practitioners, leading to continuous improvement of sustainability performance.

Over the past decade, several efforts have characterized manufacturing processes from a sustainability perspective. In addition, frameworks, methodologies, and standards development for characterizing and linking unit manufacturing process (UMP) models to construct manufacturing system models for supporting sustainability assessment have been pursued. In this paper these research efforts are first briefly reviewed, and then, ASTM standards derived from this work are described and built upon. The contribution of this research is to demonstrate how more formalization of these prior efforts will facilitate systematic reuse of developed models by encapsulating different aspects of complex processes into reusable building blocks. The research proposes a methodology to define template UMP information models, which can further be abstracted and customized to represent an application-specific, upgraded manufacturing process. The methodology developed is based on the ASTM standards of characterizing manufacturing process for sustainability characterization. The approach is demonstrated for analyzing manual and computer numerically controlled (CNC) machining processes.

A deep autoencoder feature learning method for process pattern recognition

As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs’ relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence.

Aimed at the problem of dynamic visualization monitoring in discrete manufacturing process, the visualization monitoring architecture based on Internet of Things is designed. Then the information fusion architecture is given, and some key technologies are discussed and carried out by using theory and technology of data fusion. RFID technology, wireless camera, eddy current sensor, kanban and andon are adopted and their functions are described with work flow in this system. Next the structure of system functions are analyzed in order to lay the foundation for system development. Ultimately, a prototype system is developed to simulate the discrete manufacturing process. The framework of the system and these relevant technologies provide a new proposal plan or suitable pattern for dynamic visualization monitoring, it can provide an important reference in discrete manufacturing system.

It has been shown that a manufacturing process can be modeled (learned) using Multi-Layer Perceptron (MLP) neural network and then optimized directly using the learned network. This paper extends the previous work by examining several different MLP training algorithms for manufacturing process modeling and three methods for process optimization. The transformation method is used to convert a constrained objective function into an unconstrained one, which is then used as the error function in the process optimization stage. The simulation results indicate that: (i) the conjugate gradient algorithms with backtracking line search outperform the standard BP algorithm in convergence speed; (ii) the neural network approaches could yield more accurate process models than the regression method; (iii) the BP with simulated annealing method is the most reliable optimization method to generate the best optimal solution, and (iv) process optimization directly performed on the neural network is possible but cannot be especially automated totally, especially when the process concerned is a mixed integer problem.

Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.

Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out.

Proton exchange membrane fuel cell and electrolyzer manufacturing are in the early stages of rapid expansion. Many companies have announced plans for significantly increase their manufacturing capacity through new facilities or expansion of existing facilities. This increase in scale is necessary to achieve system-cost and emission-reduction targets. However, manufacturing speed is not a knob that can simply be turned up to produce high performance, durable systems. Manufacturing processes must be understood and developed to produce fuel cells and electrolyzers that meet the requirements for both throughput and performance.The U.S. Department of Energy (DOE) Hydrogen and Fuel Cell Technologies Office (HFTO) has launched a new consortium, the Roll-to-Roll (R2R) Consortium, to conduct process science and engineering research for fuel cell and electrolyzer manufacturing. R2R’s mission is to advance efficient, high-throughput, and high-quality manufacturing methods and processes to accelerate domestic manufacturing and reduce the capital cost of durable and high-performing systems. Three major research thrusts in catalyst synthesis process science, MEA fabrication process science, and quality control are set to address challenges associated with high-volume manufacturing, which is supported by additional activities including the development of characterization tools suited for the needs of the manufacturing environment, process modeling, artificial intelligence and machine learning, and technoeconomic analysis (TEA). The objective of these activities is to enable increased production speed, yield, and efficiency to achieve fuel cell and electrolyzer manufacturing rate and cost targets. This talk will provide an overview of the R2R Consortium’s research activities.