Plastics are incredibly common across the products we use every day and 70-80% of plastic parts are manufactured by the injection molding process. Injection molding is the most important processing method for plastics due to its ability to mass-produce parts with tight tolerances and little to no finishing operations required. Injection molding, however, consumes a lot of energy with 82% [1] of the global warming potential (GWP) of injection molding coming from the energy consumption of the machine. An estimated 186 TWh of electricity is consumed by injection molding machines per year [2], equivalent to 4.5% of the total yearly electricity output of the United States. The energy consumption of global injection molding operations leads to emissions of 81.1 million metric tonnes of CO2e per year. Due to the ubiquity of injection molded plastic parts in combination with rising energy costs and increasing demand for more sustainable products, businesses need to understand how to calculate and reduce energy consumption and emissions from this critical manufacturing process.

Traditional Approach to Emissions Calculation

Most often when conducting a Life Cycle Assessment (LCA) of an injection molded part, a commercial Life Cycle Inventory (LCI) database is consulted to determine the energy consumption and emissions of the process (e.g. Ecoinvent). Unfortunately, available LCI databases provide a generic parameter to estimate specific energy consumption (~3 kWh/kg of injected material [1]) without considering factors like material grade, part design, mold design, or even type of machine. The process is shown in the image below:

Although time efficient this process leads to imprecise estimations of the energy consumed to produce a part. In a study conducted by Thiriez and Gutowski, they found specific energy consumption could range from 1.85 kWh/kg to 7.37 kWh/kg for all-electric machines and from 2.46 kWh/kg to 22.51 kWh/kg for hydraulic machines [3]. Depending on your machine relying on the LCI database average could lead to an error of up to 86.7%.

Additionally, the coarseness of this approach prevents modifications in the design of the part and the mold from affecting the environmental impact, limiting both the product designer and tooling engineers’ ability to make sustainable design choices. Having a more accurate estimation of energy consumption during injection molding could drastically improve the accuracy of emissions estimates for plastic products and give engineers tools to make environmentally conscious decisions during the part and mold design phase.

The Impact of Machine Type

The type of machine, more specifically, the type of power unit has a substantial impact on energy consumption during a working cycle. A study funded by the National Natural Science Foundation of China analyzed 5 different electro-hydraulic power units and found that energy consumption could vary by up to 87% in one working cycle depending on the type of motor and pump used in the power unit [2]. The study found that the most inefficient power unit was a fixed displacement pump powered by an asynchronous motor. Power units with a speed variable motor reduced power consumption by 47% and power units using a servo motor reduced power consumption by 87%. Power consumption was also reduced by power units using a variable displacement pump instead of a fixed displacement pump.

In addition to the staggering differences in energy consumption between different electro-hydraulic power units, all-electric machines are becoming more common. All-electric machines save energy by eliminating the losses inherent to the hydraulic system and drastically reducing idle energy consumption. An all-electric machine can reduce energy consumption by an average of 44% when compared to electro-hydraulic machines. Thiriez and Gutowski showed specific energy consumption of all-electric machines averages 3.49 kWh/kg compared to electro-hydraulic machines which average 5.27 kWh/kg [3].

In addition to the changes in energy consumption based on power units, the size of the machine also impacts the thermal energy required to maintain polymer temperature and move injection and clamping mechanisms. The type of machine is an important factor that will impact the amount of energy used to produce plastic parts and drastically change the overall emissions associated with production.

The Impact of Mold Design

In their study, Matarrese et al. analyzed the specific energy consumption of an 8-cavity mold versus a 24-cavity mold producing identical parts [1]. They observed that increasing the number of cavities increased the energy per part during the filling phase but decreased the energy per part during the cooling phase. Overall, specific energy consumption for the 24-cavity mold was significantly lower versus the 8-cavity mold. The specific energy consumptions were as follows:

• Baseline LCI Database (Ecoinvent): 3 kWh/kg
• 8 Cavity Mold: 5.07 kWh/kg (measured)
• 24 Cavity Mold: 1.98 kWh/kg (measured)

The 24-cavity mold had a specific energy consumption of 88% less than that of the 8-cavity mold, leading to substantially lower emissions per part for the higher cavity mold. In both cases, the LCI database average for specific energy consumption is noticeably different from the measurements. The 8-cavity mold had a specific energy consumption 51% higher than the database average and the 24-cavity mold had a specific energy consumption 41% lower than the database average.

Other Factors to Consider

Although not as impactful as mold design and machine type, there are other factors to consider that impact the energy consumption and emissions associated with injection molding plastic parts.

Cycle Time: Machines with higher throughput typically consume less energy per part. Since the baseline idling energy of a machine is relatively constant a shorter cycle time attributes less idling energy to each part [4].

Part Design: In addition to the part's mass, the thickness of wall sections also plays an important role in determining the cooling rate of the part and thus the cycle time. Two parts of identical mass could have substantially different cooling times depending on the wall thickness; cooling time is proportional to the square of the maximum wall thickness [5]. During the cooling phase, the machine continues to idle and consume energy. For very thick parts, active cooling may be required further increasing energy demand.

Material Grade: The grade of material used will impact process parameters such as injection pressure, injection temperature, mold temperature, and ejection temperature.

Production Schedule: Before each production run the machine needs to be set up and calibrated. During this time the injection molding machine consumes significant energy during warmup and then continues to consume energy while idling during mold installation [4]. Before production starts the machine needs to be calibrated and often the first dozen parts are rejected and scraped.

An Improved Approach to Calculating Emissions

At Tool Zero, our goal is to give designers and engineers the tools they need to create more environmentally sustainable products. Remember 82% of the GWP of injection molding comes from the energy consumption of the machine [1]. Therefore the more accurately we can predict energy consumption the more accurately we will be able to predict and reduce emissions. The benefits of more accurate energy predictions don’t stop at emissions reductions; a large portion of the operating costs of an injection molding machine are from energy consumption and optimizing for lower energy consumption can also reduce cost per part.

The below image outlines an improved process that considers additional factors like part design, cavitation, and machine type to more accurately predict energy consumption. The goal of such an approach is to augment the LCI and LCIA (Life Cycle Impact Assessment) information from an LCI database with more accurate specific energy consumption for more accurate emissions calculations.

The input factors for this method were chosen for the following reasons:

Material Type and Grade: With the specific material selected we can find the relevant molding parameters from a materials database which will be used to select the machine parameters.

Number of Cavities: The number of mold cavities is required to determine energy consumption per part as well as determine the mass of injected material for one shot and estimate machine parameters. To limit required input detail, assumptions are made about the runner layout. An improvement could be made here to analyze the actual runner layout.

Part Model: The part model is used to calculate the mass of injected material as well as estimate machine parameters based on volume, part depth, maximum wall thickness, and surface area. It is a significant challenge to precisely predict machine parameters without conducting a complete mold flow analysis (MFA) on the intended mold design. With a complete MFA machine parameters and cycle times can be directly input into the model for a more precise result.

Machine Type: A database of machine types allows us to determine the power profile of the machine in combination with the machine parameters.

Production Schedule: Information about average lot size, total production volume, setup times, and number of calibration parts allows us to accurately attribute setup energy to the parts.

The outline approach is still being developed and refined internally at Tool Zero. If you are interested in learning more or partnering with us on reducing energy consumption for your injection molding operation contact us at contact@toolzero.com

Conclusion

Plastics is one of the most common materials in the products we use every day and 70 - 80% of all plastic parts are produced using the injection molding process. Alongside rising energy costs and increased consumer demand for sustainable products businesses are looking to understand and reduce the energy use and emissions associated with making their products.

82% of the global warming potential of the injection molding process comes from energy consumption however the traditional approach for calculating emissions assumes an average specific energy consumption of 3 kWh/kg which leads to not precise emissions predictions. This also limits the tools available to product designers and tooling engineers to proactively reduce emissions during the design phase.

Machine type, mold design, part design, material selection, and production schedule all play a role in the energy consumption of an injection molding machine on a per-part basis. The improved approach proposed herein is designed to account for these additional factors. Using the improved approach to augment the LCIA data from an LCI database could enable businesses to more accurately calculate their emissions and allow designers to make more sustainable design decisions.

At Tool Zero we aim to empower businesses to make more sustainable design decisions. Reducing the energy consumption of your injection molding operations helps save money and reduce the global warming potential of your plastic products.

References

[1] P. Matarrese, A. Fontana, M. Sorlini, L. Diviani, I. Specht, A. Maggi, “Estimating energy consumption of injection moulding for environmental-driven mould design”, Journal of Cleaner Production, Volume 168, 2017

[2] He Liu, Xiaogang Zhang, Long Quan, Hongjuan Zhang, “Research on energy consumption of injection molding machine driven by five different types of electro-hydraulic power units”, Journal of Cleaner Production, Volume 242, 2020

[3] A. Thiriez and T. Gutowski, "An Environmental Analysis of Injection Molding," Proceedings of the 2006 IEEE International Symposium on Electronics and the Environment, 2006., 2006

[4] A. Weissman, A. Ananthanarayanan, S. Gupta, R. Sriram, “A Systemic Methodology For Accurate Design-Stage Estimation of Energy Consumption for Injection Molded Parts”, Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2010

[5] Boothroyd, G., P. Dewhurst, and W. Knight, Product Design for Manufacture and Assembly. 2 ed. 2002