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Injection molding is one of the most common and effective manufacturing processes used to produce plastic products and impacts industries around the world. However, injection molding is a complex process that requires careful consideration of several key control variables. These variables and how they are utilized greatly affect the resulting polymer parts of any molding operation. The bounds of the acceptable values of each Control Process Variable (CPV) must be analyzed and delimited to ensure manufacturing success and produce injected molded parts efficiently and effectively. One such method by which the key CPVs of an injection molding operation can be delimited is through the development of a process window. Once developed, operating CPVs at values inside the boundaries of the window or region will allow for the consistent production of parts that comply with the desired Performance Measures (PM), promoting a stable manufacturing process. This work proposes a novel approach to experimentally developing process windows and illustrates the methodology with a specific molding operation. A semicrystalline material was selected as it is more sensitive to process conditions than amorphous materials.

While design groups have utilized the abilities of injection molding simulation software, its use is often underutilized by process engineers. To expand the application of simulation software to manufacturing groups, this work focuses on developing a methodology to construct injection molding process windows through the predictions of simulation software. The methodology was developed by testing combinations of controllable process variables in the filling and packing stage of the injection molding process with high-impact polystyrene. Using this method, the process window can be tailored to a manufacturer’s desired product performance measures as well as target a specific defect they are facing. The process windows developed were experimentally validated displaying a successful combination of controllable process variables in the filling and packing stages which resulted in an acceptable part. Additionally, the process window was able to predict the dimensional shrinkage of a part within 1% of the experimentally produced part. This analysis establishes confidence in the software’s ability to aid manufacturing groups to successfully run their operations.

Quality control seeks to collect and analyze large amounts of data to take appropriate corrective actions and ensure that products or services meet quality requirements. This study proposed a methodological framework to analyze the quality control process employing Monte Carlo simulation. The methodology consists of four steps: (i) Establishment of probability distributions, (ii) Construction of the mathematical model, (iii) Running the simulation, and (iv) Analysis of the results. The application of the methodological framework in a carbonated beverage production made it possible to ensure with 99% confidence that one of the most important quality characteristics of the product, the degrees Brix, varies in a range of ± 0.02. The results show the methodology allows to broadly map the process variables behavior and to make decisions on optimal levels for quality monitoring and control.

The trend in bioplastic application has increased over the years where polyhydroxyalkanoates (PHAs) have emerged as a potential candidate with the advantage of being bio-origin, biodegradable, and biocompatible. The present study aims to understand the effect of acetic acid concentration (in combination with sucrose) as a mixture variable and its time of addition (process variable) on PHA production by Cupriavidus necator . The addition of acetic acid at a concentration of 1 g l −1 showed a positive influence on biomass and PHA yield; however, the further increase had a reversal effect. The addition of acetic acid at the time of incubation showed a higher PHA yield, whereas maximum biomass was achieved when acetic acid was added after 48 h. Genetic algorithm (GA) optimized artificial neural network (ANN) was used to model PHA concentration from mixture-process design data. Fitness of the GA-ANN model (R 2 : 0.935) was superior when compared to the polynomial model (R 2 : 0.301) from mixture design. Optimization of the ANN model projected 2.691 g l −1 PHA from 7.245 g l −1 acetic acid, 12.756 g l −1 sucrose, and the addition of acetic acid at the time of incubation. Sensitivity analysis indicates the inhibitory effect of all the predictors at higher levels. ANN model can be further used to optimize the variables while extending the bioprocess to fed-batch operation.

Rising sea levels and the increasing intensity of storm surges and tropical cyclones due to climate change and the resulting dynamic shifts in shoreline positions have dramatically increased the exposure risk and vulnerability of local communities inhabiting the ecologically sensitive deltaic tracts of the Sunderbans in India. The impacts arising from such hazard events on this fragile ecosystem need to be gauged to ameliorate the lives and livelihoods of these residents. This article examines the spatial distribution of vulnerability to coastal hazards within the Sundarban Biosphere Reserve (SBR) in India. For this, we have utilized several structural and process variables, which were integrated to construct a coastal vulnerability index (CVI), using the square root equation. The coastlines of the islands located within the SBR were overlain by 543 grids, each of 2 × 2 km dimension, to assign the risk rank for each considered variable. This revealed that of the total shoreline length (754 km), nearly one-fourth was very highly vulnerable, followed by highly vulnerable (27.8%), moderately vulnerable (27.9%) and low vulnerability (18.8%). Of the total islands located in these grids (27), the coastline of eleven islands was found to have very high vulnerability, five experienced high vulnerability, eight recorded moderate vulnerability while only three had low vulnerability status. The ambient geomorphological characteristics, coastal area slope, the rate of shoreline change and sea level rise were significant variables that accorded high and very high vulnerability to the islands. The CVI helped in identifying islands that require immediate attention for lessening the impact of climate change induced hazards in the SBR and also aided the assessment of the physical and coastal vulnerability conditions of these islands. This approach can be effectively utilized for assessing coastal vulnerability and for creating a holistic approach towards coastal conservation and management.

Europe is currently not competitive in battery cell production. In order to increase competitiveness, battery cell production must be made more efficient. A major factor in improving efficiency is the reduction of waste. This requires understanding the many dependencies between the production process variables within battery cell manufacturing. For clarifying these dependencies, knowledge of the variables is essential. The challenge here is to completely determine them. For this purpose, different tools and methods are applied. But, in the case of cross-process chain production processes like battery cell production, they quickly reach their limits because these tools are not suitable for the structure and properties of the respective processes. The aim of the approach presented here is to support a complete identification of all process variables of battery cell production in the best possible way.

Methanol-to-olefins, as a promising non-oil pathway for the synthesis of light olefins, has been successfully industrialized. The accurate prediction of process variables can yield significant benefits for advanced process control and optimization. The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes, such as high nonlinearities, dynamics, and data distribution shift caused by diverse operating conditions. In this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues. Firstly, a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions. Subsequently, convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns. Meanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions. Afterward, the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction. Finally, the outputs are denormalized to obtain the ultimate results. The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices, making the model more interpretable. Lastly, this model is deployed onto an end-to-end Industrial Internet Platform, which achieves effective practical results.

Abstract Meeting quality characteristics of products and processes is an important issue for customer satisfaction and business competitiveness. It is necessary to integrate new techniques and tools that improve and complement traditional process variables analysis. This paper proposes a new methodological approach to analyze process quality control variables using Fuzzy Cognitive Maps. Application of the methodology in the production process of carbonated beverages allowed identifying process variables with the greatest influence on finished product quality. The process variables with the greatest impact on carbon dioxide content in the beverage were the beverage temperature in the filler, the carbo-cooler pressure, and the filler pressure.

A mixture process variable (MPV) design consists of mixture design and process variable(s). The problem in MPV experiment is the number of experiment runs will be larger if the process variable increases. An optimal design can be a solution to produce a good design with a certain criterion and a limited number of runs. In practice, the compositions of the mixture design are running on each level of the process variable(s). It has a consequence that the randomization is restricted. A split-plot design can be an alternative to overcome the problem. In this research, the whole plots of the split-plot design were the levels of the process variable(s) and the subplots were the compositions of the mixture experiments. In addition, two optimality criteria were used: D-optimality and I-optimality criterion. The D-optimal design is searching a design by minimizing covariance of model parameters meanwhile the I-optimal design is seeking a design by minimizes average of the prediction variance. The study case was a baking experiment in which consisted of three flours and a process variable. It is surprised that the D-optimal design out performed compared to the I-optimal design in terms of the variance prediction in this case.

With an increased demand for comfortable and aesthetically pleasing automobile interiors, fabric seat covers are being used more widely. Previously, covers were manufactured using adhesives attached to a molding and covered with a skin layer. However, this process releases Volatile organic compounds (VOC), pollutes the air inside the automobile, and leads to peel-strength-related problems. This study examines a multi-component injection molding process that uses residual heat during injection molding to glue the skin layer to the molding, employed in seat-backboard manufacturing. Hence, the VOC emission problem is overcome as adhesives are not employed. To obtain enhanced peel strength the optimal skin material is selected using surface-adhesion length and material peel-strength measurements. The response surface design method is utilized with a design-of-experiments method to determine the process variables that maximize the peel strength for the selected materials. The process variable selection is then confirmed via additional experiments. It is expected that the problems related to VOC emissions and peel strength, which limit current seat-backboard manufacturing techniques, can be resolved through application of the optimal conditions identified in this study to a multi-component injection molding process.