Manufacturing Sensitive Products in the USA - mold manufacturing company

Moldex3D Cloud-Connect helps users deploying the latest software version on AWS, Azure, and Google within only 40 minutes, and can adjust the calculation scale according to demand, the company said. Cloud-Connect also supports automatic on/off functions, allowing enterprises to save up to 72% of costs during the period of timed plans.

The new software also aims to meet customer demands for virtual and real integration. The Moldex3D iSLM allows users to establish a “big-data” database, effectively manage work assignments and project schedules, and create their own quality indicators to determine product quality. Users can search models by uploading CAD files, saving time and effort on searching and comparing files throughout the database.

Arburg uses AI “to develop master models using experience and data collected over the years on process, material, and machinery,” Faulhaber continued. “The customer could then sharpen the provided master model ‘on edge’ and optimize their processes. The in-house development Gestica control system, the Arburg host computer system, and the arburgXworld customer portal give an advantage here.

By harnessing the reams of data produced in modern plastics processing facilities, artificial intelligence can improve machine performance.

Moldex3D 2023 introduces its latest cloud platform called Moldiverse. This allows users to access services such as MHC, iMolding, and University, to improve molding analysis.

Comparing AI and machine learning, Glueck said, “AI actually requires a much higher time investment and, correspondingly, a higher financial investment. A large number of parameters must be recorded from a running process and the relevant parameters are determined on the basis of the deviations. These are compared with measurement data of the product.”

The new software is designed to improve venting analysis, including compressibility and air-temperature calculation. It does this accurately, the company said, by simulating the temperature and pressure changes of air in the mold cavity during the filling process. This allows users to plan and compare various venting designs to avoid defects such as short shots and burn marks.

The latest version of the molding analysis software features robust simulation, user-friendly interfaces, and access to cloud services.

Moldex3D 2023 offers new features to overcome barriers in complex manufacturing processes. In terms of optics, the latest MCM dual-shot optical simulation function accurately calculates residual stresses and thermal residual stresses for each shot, providing more detailed optical molding parameters for design optimization, the company said. It also provides two viewing output options — Isochromatics and Isoclinics — allowing users to intuitively identify simulation results.

Wittmann Battenfeld’s HiQ software can generally be retrofitted even to older injection molding machines equipped with a B8 machine control.

Based on factors like changes in material, ambient temperature, machine wear, tool wear, and other influences, “AI can determine which machine parameters need to be changed so that the product can be produced within its quality tolerances. This can take months, as errors first must occur in order to learn from them.”

The Industry 4.0 era of manufacturing depends so heavily on data-driven precision that artificial intelligence (AI) is playing an increasing role in harnessing that data to enhance the performance of machines — including injection molders.

In an era of increasingly popular multi-core computers, using parallel computing to reduce the calculation time of molding analysis can offer advantages to enterprises. The new software supports parallel computing on multi-core processors, multiple processors, and clusters, which can increase the calculation speed by up to 80%, the company said. In molding applications that contain a semiconductor device, it can improve the calculation efficiency of wire sweep analysis by nearly 20%.

Geoff Giordano is a tech journalist with more than 30 years’ experience in all facets of publishing. He has reported extensively on the gamut of plastics manufacturing technologies and issues, including 3D printing materials and methods; injection, blow, micro and rotomolding; additives, colorants and nanomodifiers; blown and cast films; packaging; thermoforming; tooling; ancillary equipment; and the circular economy. Contact him at [email protected].

“AI is becoming increasingly important in mechanical engineering, not least because of the need to automate injection molding processes efficiently and flexibly despite ever smaller batch sizes and shorter product life cycles,” said Werner Faulhaber, Director of Research and Development at Arburg. “Application examples of AI include automatic programming of robotic systems, targeted malfunction remedying, and a spare parts system with ‘intelligent’ image processing. Arburg is working on making injection molding more intelligent, step by step — ensuring that the machine continuously learns, keeps itself stable, and can even optimize itself in the future.”

Wittmann co-funded such an assessment program with Austria’s Montanuniversität Leoben university, “but we found that the time needed to make it workable for production had to be questioned because in addition to the long-term investigation of the process, you also need the manpower necessary to handle it.”

CoreTech has released Moldex3D 2023, the latest version of its molding analysis software, which focuses on reliability, efficiency, augmentation, and liberation, said the company. It strengthens simulation performance and provides user-friendly interfaces and access to various cloud services, the company added.

Users can set parameters to automatically build baffles and cooling channel mesh, supporting the establishment and simulation of manifolds, which can add cooling tubes outside the mold to consider the actual influence of the mold temperature controller in simulation. This is designed to accelerate the efficiency and calculation accuracy of the overall molding analysis.

Thanks to the integrated arburgXworld Control FillAssist, Arburg’s Allrounder "knows" the molded part it is supposed to produce. One issue for the future is to ensure that the machine not only keeps itself stable, but also self-optimizes.

The application programming interface (API) function can streamline workflow, enhance software and hardware capabilities, support digital twins, and seamlessly connect virtual and actual processes.

“The technology draws new conclusions from current parameters and, thus, becomes increasingly intelligent as it monitors performance,” said Product Manager Christian Glueck. “We limit it to a methodical determination of parameters. Therefore, the time required to use the technology is minimal, as is the price.”

Wittmann Battenfeld, which has fully embraced Industry 4.0 connectivity across its portfolio of injection molding and auxiliary machines over the past several years, employs AI with its robots to monitor cycle times and control robots’ speeds outside the molding machine.

Another Wittmann feature, Eco-Vac conserves energy by setting a few parameters on the robot and allowing the robot to turn its vacuum circuits off and on. “The robot monitors the vacuum level of the circuit used for picking the part out of the mold. If the robot senses the vacuum has reduced to a level that it could drop the part before it is told to, the robot will turn the vacuum on until it reaches the safe level again, then shuts back off.” This feature cuts the amount of compressed air each robot uses “and could save customers hundreds of dollars a year per robot.”

AI in manufacturing encompasses an array of technologies that allow machines to perform with intelligence that emulates that of humans. Machine learning and natural language processing help machines approximate the human capacity to learn, make judgments, and solve problems. Data-enhanced efficiency keeps processes moving faster and more cost-effectively.

For the increasing number of continuous fiber-reinforced layup applications, Moldex3D 2023 supports file formats from LS-DYNA or Abaqus, helping with the calculation of composite material layups and providing more accurate simulation results.

As AI and machine learning are further leveraged to improve injection molding operations, simply gathering data is not enough to optimize processes, Faulhaber cautioned. “You also need the process expertise and domain knowledge. In the future, the evaluation of many data directly in the control unit will offer further added value.”

The company’s Eco-Mode saves wear and tear on the robot by ensuring it does not run faster than necessary — ultimately saving maintenance and energy costs. Offered standard on many Wittmann robots, Eco-Mode “requires no special programming or interface with the IMM or operator/programmer,” said Jason Long, National Sales Manager for robots and automation for Wittmann USA. “All the end user has to do is tell the robot how many seconds it should get back over the IMM before the mold opens.”

Moldex3D integrates with injection molding machines from Fanuc and Sumitomo. Users can import molding conditions and response curves from these injection molding machines into Moldex3D, allowing the molding software to perform molding analysis using more accurate machine data. It also can export the optimized injection parameters back to the machine for use in trial molding. This connects simulation software with real-world information.

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One application Arburg is working on is the automatic programming of its Multilift linear robotic systems. “The idea is that the operator simply enters the destination, as with a car navigation device, and the system automatically calculates the optimal route. For robotic systems, this means that the operator simply enters the desired start and end positions, and the control system takes care of the rest.”

The Moldex3D Forming Technology R&D Center said it conducted actual injection experiments using nearly 400 different materials to enhance reliability and make the results of molding analysis closer to reality.

“One of Arburg's medium-term goals is to develop a system for digital twins of customized injection molding machines. This will open up completely new possibilities for simulating the cycle and making energy predictions. In addition, 3D views and installation plans of the machine — stored in the arburgXworld customer portal and in the control system — support the operator,” said Faulhaber.

Assisting customers in converting simulation data into product insights more quickly and easily is a continuing goal of Moldex3D. The new software allows users to customize simulation projects and generate reports, comparing all simulation results with the same perspective and conditions, clearly identifying the most suitable molding conditions, the company said. Users also can adjust the timeline to understand changes in temperature and pressure of the molten resin in the mold cavity at each time point.

The company’s machine-learning capabilities — HiQ Flow and CMS technology — will be on display at this year’s K show on Oct. 19 to 26 in Düsseldorf, Germany. The speed of ROI can be as short as a few cycles with HiQ Flow, and the software can often be retrofitted to older injection molding machines equipped with a B8 machine control. A CMS Pro version will be available at a later date.

Arburg forms flexible — and controllable — production systems by combining machines, automation, and proprietary IT solutions. The company’s Gestica control system, with its intelligent assistant functions, is integral to those systems. “All Kuka six-axis robots, for example, have been equipped with the new Gestica user interface as standard,” Faulhaber noted. “This simplifies programming, as well as the monitoring, storage, and evaluation of process data.”