Plastics and Rubber
Industries in the Plastics and Rubber Products Manufacturing subsector make goods by processing plastics materials and raw rubber. The core technology employed by establishments in this subsector is that of plastics or rubber product production. Plastics and rubber are combined in the same subsector because plastics are increasingly being used as a substitute for rubber; however the subsector is generally restricted to the production of products made of just one material, either solely plastics or rubber.
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Manufacturing process optimization for real-time quality control in multi-regime conditions: Tire tread production use case
The high-stake nature of most manufacturing processes empowers the importance of real-time quality control and assurance. In the event of a failure in production, a decision-making process can be time-consuming for the human and prevent timely actions. The agility can be boosted with a decision-support system based on artificial intelligence. Particularly, multi-objective process optimization can be employed to select the optimal control settings in real-time, and thus enhance relevant key performance indicators, concurrently.
Based on process and quality parameters being streamed from the production plant in real-time, the optimizer can act in timely critical and quality-threatening situations and generate immediate corrective actions. The multi-regime operation of the plant and design space dimensionality can impact the convergence rate and add to execution time. Therefore, production regimes recognition and greedy search of suffix tree-based models of the process have been engaged, aiding in a better-focused and faster space search at an early phase of the algorithm run.
Beyond simply reviewing the outputs, the user can leave feedback, which is utilized by the optimizer’s reinforcement learning mechanisms. Validated in this real-world scenario, the solution produced a rise from 81.83% to 90.91% in the tread quality.
Apollo Tyres Moves to AWS to Build Smart, Connected Factories
Apollo Tyres needed to upgrade its infrastructure to develop new ways of engaging with fleet operators, tyre dealers, and consumers, while delivering tyres and services efficiently at competitive prices. The company’s first step was to create a data lake on AWS, which centrally stores Apollo Tyres’ structured and unstructured data at scale. This data lake provides the foundation for an integrated data platform, which enables Apollo Tyres’ engineers around the world to collaborate in developing cloud-native applications and improve enterprise-wide decision making. The integrated data platform enables Apollo Tyres to innovate new products and services, including energy-efficient tires and remote warranty fulfillment.
Transfer learning with artificial neural networks between injection molding processes and different polymer materials
Finding appropriate machine setting parameters in injection molding remains a difficult task due to the highly nonlinear process behavior. Artificial neural networks are a well-suited machine learning method for modelling injection molding processes, however, it is costly and therefore industrially unattractive to generate a sufficient amount of process samples for model training. Therefore, transfer learning is proposed as an approach to reuse already collected data from different processes to supplement a small training data set. Process simulations for the same part and 60 different materials of 6 different polymer classes are generated by design of experiments. After feature selection and hyperparameter optimization, finetuning as transfer learning technique is proposed to adapt from one or more polymer classes to an unknown one. The results illustrate a higher model quality for small datasets and selective higher asymptotes for the transfer learning approach in comparison with the base approach.
2021 IW Best Plants Winner: IPG Tremonton Wraps Up a Repeat IW Best Plants Win
“If you wrapped it and just wound it straight, it would look like a record, with peaks and valleys,” says Richardson. So instead, the machines rotate horizontally, like two cans of pop on turntables. Initially, IPG used a gauge that indicated whether the film was too thick or too thin. “That was OK,” says Richardson, “but it didn’t get us the information we needed.”
Working with an outside company, IPG Tremonton upgraded the gauge to one that could quantify the thickness of the cut plastic in real time as the machine operates.
The benefits of the tinkering were twofold. First, the upgrade gave operators the ability to correct deviations on the fly. Second, “we found that we had some variations between a couple of our machines,” Richardson says. Using the new gauge on both machines revealed that one of them was producing film “a few percentage points thicker” than its twin. “We [were] basically giving away free product,” Richardson recalled. The new sensor gave IPG the information it needed to label film more accurately.
SEM-EDS Failure Analysis in Tire Manufacturing
Tires are often considered as low-tech commodities. However, contrary to popular perception, tires are actually highly engineered structural composites. Tires contain many rubber compounds (up to 20, with several types of microstructures) that provide different levels of grip and traction. Fillers are added to the main polymer matrix to facilitate rubber reinforcement.
Tire failures often occur due to a lower or decreased material quality, and an optimal and homogenous dispersion of all the different fillers is a key factor for a higher material quality. Analytical techniques like SEM-EDS are required to understand the root cause of a failure but the material contrast obtained from a backscattered electron image is not enough to distinguish between the large variety of materials employed.
This application note demonstrates that the live quantitative elemental analysis of Axia ChemiSEM provides an efficient and easy way to characterize the different fillers, despite their similar compositional contrast.
Trash to Cash: Recyclers Tap Startup with World’s Largest Recycling Network to Freshen Up Business Prospects
People worldwide produce 2 billion tons of waste a year, with 37 percent going to landfill, according to the World Bank.
“Sorting by hand on conveyor belts is dirty and dangerous, and the whole place smells like rotting food. People in the recycling industry told me that robots were absolutely needed,” said Horowitz, the company’s CEO.
His startup, AMP Robotics, can double sorting output and increase purity for bales of materials. It can also sort municipal waste, electronic waste, and construction and demolition materials.
Five companies make a quarter of world’s single use plastics
The top 5 companies created roughly 26 million metric tones of plastic waste fueled by demand of the United States and China.
Strategic Analytics Help Intertape Polymer Shrink Inefficiencies
For Intertape Polymer Group (IPG), a global manufacturer of packaging and protective solutions for industrial and e-commerce applications, the digital transformation process has always been about embracing technology with a keen eye on extracting the overall business value. As such, IPG is currently at different levels of maturity across the portfolio of digital technology deployments, including additive manufacturing, AR/VR training, IoT-based predictive downtime and robotic process automation.
IPG has taken advantage of the unique data modeling capabilities of the Sight Machine platform, which continuously transforms all data types generated by factory equipment and manufacturing software into a robust data foundation for analyzing and modeling a plant’s machines, production processes and finished products.