By enabling manufacturers to incorporate sustainability best practices into the early upstream product development stages, engineers can advance innovation while helping to mitigate the industry’s environmental footprint.

ccording to the United States Census Bureau, data from the International Database puts the global population at about 8 billion and rising. With this growth comes an increased demand for products. Unfortunately, as manufacturing output intensifies, so does its effect on the environment.

Common significant environmental footprints in discrete manufacturing include:

• Material Consumption: The current demand alone exceeds 1.75 of Earth’s capacity.
• Energy Consumption: 54% of global energy consumption is by manufacturing and production sectors.
• Waste (Hazardous and Non-Hazardous): Only 18% of e-waste and 6% of discarded plastic is recycled. Although aluminum can be infinitely recycled, 7 million tons are not recycled each year.

In response, forward-thinking manufacturers are emphasizing a shift towards more sustainable practices. In addition to social awareness, such initiatives are generally driven by a confluence of business factors, including demand, regulatory requirements, economic benefits, and the broader shift towards a circular economy.

Read the entire article here in Digital Engineering 24/7.

The manufacturing industry’s movement towards zero physical prototype testing and the consequential shift towards increased digital testing represent a historic turning point in the simulation world. This transition highlights the critical need to embrace and implement new classes of tools to support the management and sharing of simulation results data. Foremost among these tools are Simulation Process and Data Management (SPDM) and Rapid Results Review™ (RRR).

Simulation Process and Data Management

Simulation Process and Data Management, refers to a comprehensive approach to managing simulation processes and their associated data throughout their lifecycle. It involves organizing, storing, and retrieving simulation data, ensuring version control, traceability, and data integrity.

SPDM facilitates collaboration among team members, streamlines workflows, and enhances productivity in simulation-based projects. By centralizing simulation data and automating processes, SPDM optimizes decision-making, reduces errors, and enhances the efficiency of engineering and research endeavors.

SPDM is playing an increasingly important role in modern product development and engineering. Through SPDM, many companies have realized measurable and sustainable gains in product development time, quality, and cost. As industries strive for innovation, agility, and cost-effectiveness, the significance of SPDM cannot be overstated.

Rapid Results Review

While SPDM handles a wide range of simulation data and processes, the integration of Rapid Results Review technology emerges as a vital component of every successful SPDM installation and its adoption. RRR contributes to shortening product development cycles, improving product performance, and minimizing time-to-market. All within the SPDM framework.

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Because the bottom line is critical to any “for profit” company, business owners and management are constantly looking for ways to improve. The key is to advance profitability without relying solely on sales.  How? Look around — profitability boosters can be found throughout the average plastics processor’s operation. Technology in the form of automation, for example, provides the foundation to become more efficient, cost conscious, and less wasteful. And there are plenty more opportunities. In fact, here are seven often overlooked areas where you can enhance profitability.

Read the entire article here in Plastics Today magazine.

Does today’s product development process match current market demands or is it just a replica of the way we’ve “always done it”? Looking in from the outside, product development can often appear to be a sequential process on a one-way street running from design to production with a side trip for analysis. However, there are multiple touchpoints and feedback loops within and between these stages.

One of the most critical, yet often overlooked and underdeveloped, areas of collaboration is between the CAE analyst and design teams. More than an inconvenience, effects of this gap can ripple throughout the organization, causing delays, excessive costs and bad decisions.

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One of the biggest mistakes a small to mid-size fabricator makes has nothing to do with equipment, personnel or partnerships. Rather, the error lies in dismissing automation as beyond what they need or can afford. Automation positions manufacturers for long-term success in an increasingly digital world. Reducing manual or repetitive efforts frees up skilled workers to focus on higher value tasks, minimizes errors, improves product quality and grows profitability through increased production capacity.

So, with these advantages and more, why do some fabricators shy away from automation?

Read more here.

Driven by increased demands for precision, versatility and cost-effective production, a growing number of fabricators are considering the addition of fiber lasers to their cutting arsenal. These machines offer several advantages, including unparalleled accuracy, intricate cuts and minimal material waste. Their versatility supports the processing of plate and sheet in both ferrous and non-ferrous materials, such as carbon steel, stainless steel, aluminum, brass and copper. And, as fabricators prioritize automation and smart manufacturing, fiber lasers streamline production while enhancing overall operational efficiency.

While the thought of such technology may be tempting, there are issues that must be considered when adding any piece of equipment – not the least of these are cost and logistics. Investing in fiber laser cutting equipment often represents a substantial financial commitment. As such, it’s critical that the laser and its capabilities are fully maximized.

Similarly, fabrication equipment typically demands a substantial footprint on the shop floor. The need for adequate spacing is often driven by operational functionality, safety concerns, operational workflow and the potential for future scaling of production. Efficient space utilization becomes a delicate balancing act as every square foot of space becomes a valuable commodity.

Read the article here.

Comparatively speaking, the century following the Industrial Revolution saw the process to develop and manufacture products remain relatively unchanged, with long stretches between incremental advancements. Without the tools to obtain, interpret and apply meaningful feedback, product development was largely guided by observations, trial-and-error, and experience. Consequently, progress was severely limited.  

This all began to change with the introduction of digital applications in the latter half of the 20th Century. Throughout the decades that followed, increasingly maturing software systems would allow organizations to capture and leverage previously untapped data embedded in products. Software was becoming the catalyst to ignite innovation, drive quality, enhance equipment effectiveness, provide insight and inspire next-generation products.

Today sophisticated applications continue to advance rapidly to keep pace with complex and evolving products and processes. The examples below show how software and systems translate raw metrics into insights and actions throughout the product lifecycle. 

Read the entire Machine Design Magazine article here.

Production planning and scheduling sits at the heart of all operations for plastics processors and poses unique challenges. To optimize overall production runs and improve lead times, it is crucial to identify the best-performing machines and tools for each product upfront. Additionally, processors must consider the availability of limited quantities of secondary equipment such as conveyors, grinders, calibrators, robots and coextruders, along with any scheduled maintenance or ongoing production runs.

Moreover, dynamic material requirements planning (MRP) calculations are essential to ensure the availability of materials and equipment when rescheduling work orders.

To effectively address these complexities, plastic processors require an enterprise resource planning (ERP) system with an interactive production scheduling module. Let’s explore the significance of interactive master production scheduling for plastic processors and the benefits it offers in improving lead times.

Read the article here.

The integration of artificial intelligence (AI) into engineering simulation has been rapidly advancing, transforming the landscape of design and analysis. AI technologies are playing a pivotal role in enhancing the efficiency, accuracy, and speed of engineering simulations across various industries.

One notable area where AI has made significant strides is in the optimization of simulation processes. Machine learning algorithms are being employed to automate parameter tuning, allowing for quicker and more precise simulations. This not only reduces the time and resources required for traditional trial-and-error methods but also enables engineers to explore a broader design space.

Moreover, AI is facilitating the development of predictive models that can simulate complex behaviors and interactions. This has proven valuable in predicting the performance of structures, materials, and systems under different conditions, leading to more informed decision-making in the design phase. Neural networks and deep learning algorithms have shown promise in recognizing patterns and extracting meaningful insights from large datasets generated by simulations.

In the realm of fluid dynamics and computational fluid dynamics (CFD), AI is being employed to optimize fluid flow simulations. Machine learning algorithms can analyze vast datasets to identify flow patterns, turbulence, and heat transfer phenomena, providing engineers with valuable insights for designing more efficient systems.

However, challenges persist, including the need for large and diverse datasets for training AI models and ensuring their generalizability to real-world scenarios. Additionally, there are ongoing efforts to enhance the interpretability of AI-driven simulations, allowing engineers to understand and trust the results produced by these intelligent systems.

As technology continues to evolve, the integration of AI in engineering simulation is likely to become even more sophisticated, revolutionizing the way engineers approach design challenges and ultimately leading to more innovative and optimized solutions across various industries.

Overall Equipment Effectiveness (OEE) monitoring plays a crucial role in the realm of lean manufacturing, serving as an essential tool to assess and enhance the efficiency and productivity of manufacturing processes.

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