Plastic Molding Workshop

Andy Clarke: Welcome to today’s presentation for expert users on aPriori’s plastic injection molding manufacturing process model. My name is Andy Clarke and I am a product manager here at aPriori. I am responsible for the plastics, machining, and assembly manufacturing process models. In today’s expert session I will be exploring some of the fundamental calculations of our plastic injection molding model. I will be going into detail of recent enhancements and then tying it all together with a section on increasing throughput and using analytics to identify opportunities and ultimately realize cost savings.

Injection Molded Parts

AC: So firstly, let us have a look at the calculations behind our numbers. As experts in plastic injection molding, we understand that cooling time is typically dominant in the cycle time of an injection molded part. aPriori uses the industry and academically accepted Ballman and Shusman cooling equation. First published in the Modern Plastics Journal back in 1959, it uses the production parameters of melt temperature, mold temperature, and eject temperature, as well as thickness dimensions of the part and material thermal properties to calculate cooling time.

Uniform Wall Thickness

AC: If you look at the top line of the equation, we can see that the wall thickness variable is squared. This means that any increase in the wall thickness leads to an exponential increase in cooling time. This is the reason that the first rule of plastic molding design is usually to keep our uniform wall thickness. It is also worth noting that the thermal diffusivity of the material is a very sensitive variable, so if you have the thermal diffusivity data for your specific resin, then you’re recommended to use it.

AC: So the calculation gives us a temperature time curve like this, and the part cooling time is how long after injection the part reaches ejection deflection temperature. This all seems very straightforward for parts with a constant thickness. If we have parts with significantly variable thickness across its geometry then calculating cooling time becomes more of a challenge. On that note, I would like to bring you through some of our recent enhancements to overcome challenges like this one.

AC: To account for variances in wall thickness, aPriori passes in a variable known as nominal wall thickness for the thickness in the equation. Nominal wall thickness is a representation of the parts general thickness. Inversions previous to 21.1, aPriori used a weighted average using average wall thickness and maximum wall thickness. It was found to be overly conservative for parts with very thick sections especially when they are significantly localized. To address this issue, we saw the need to classify parts with respect to the thickness distribution and aspect ratio.

Thickness Standard Deviation

AC: To enable this classification, we now extract additional wall thickness data in the form of the thickness standard deviation across the part, and also the maximum thickness of 80% majority of the part. We now know what the thickness is across the lion’s share of the part and by how much it varies. We then classify the parts into four camps. Different nominal wall thickness equations are then applied depending on the categorization.

AC: The result of this work is significantly improved nominal wall thickness calculations and thus cycle time for parts with irregular wall thickness distribution. This enhancement was validated with the support of data from six different customers from different industries, including several of whom who were able to provide us shop for data of actual production cycle times. We released this functionality in June 2021 in our 21.1 software release.

Significantly Higher Clamp Forces

AC: The next calculation I would like to review is somewhat simpler, and that calculation is for clamp force. This is an area we have made significant enhancements in recently. We had feedback from multiple customers, particularly from ones that made larger parts, that they were seeing aPriori calculate significantly higher clamp forces than expected, leading to the selection of higher tonnage machines and more expensive overhead rates. We know that clamp forces equal to pressure times cross-sectional area, and since we can do little about cross-sectional area, we investigated if the calculated pressure may be too high and tried to understand what the cause may be.

Types Of Plastic Resins

AC: While investigating this issue, we reviewed the way in which we calculate pressure. Firstly, we understand that different types of plastic resins have different radiological properties which dictate flow characteristics. Our clamp force calculation uses a material’s minimum and maximum pressure from the material library and we will select a point in this range based on a number of parameters, the most significant being flow ratio. We assume a single gate on the short side of the part, conservatively giving the longest flow length. The flow ratio calculation is the part length divided by the nominal wall thickness. Depending on the results of this calculation, we may see a lesser value in the range or a higher value in the range. For a smaller part like this, the resultant data aligns well with reported production parameters.

AC: For large flat parts or long narrow parts, this would typically result in an overestimation of the required pressure and thus clamp force. And in reality, even at maximum recommended material pressure, parts of this size will likely not fill and would in fact result in a short shot. To ensure the mold fills properly, the injection pressure is reasonable, a tool maker would in fact utilize multiple gates reducing the distance that the melt front has to flow.

AC: This animation is from a leading standalone melt flow analysis software. You can see where the simulation engineer has placed the gates before simulating and evaluating performance, manually iterating the analysis as needed until they are satisfied with the results. Wouldn’t it be great if we had this kind of flow simulation functionality natively within aPriori? Well, we do, and we call it flow appraisal. However, in contrast to other packages where there may need to place gates manually, aPriori will iterate through multiple gate locations and multiple numbers of gates until it finds a configuration in which the mold will fill within the material parameters and tooling configuration. Like in this example here, we have a PC-ABS bumper that requires seven hot runner gates to fill the mold.

Multiple Configurations

AC: Now so far, the examples I have given have been hot runner configurations. We also support multiple configurations of cold runner tools, all controlled by the user via the process setup options. Users can even override the number of gates should they want to. You will remember when I was explaining our previous pressure calculation, that for this part, for a single gate, the cavity would likely produce a short shot. Well, now we actually alert the user of the risk of that happening. The part will fail to cost and a warning is generated that informs the engineer that either the tooling configuration, part material, or part geometry need to be modified in order to feasibly manufacture this part.

AC: At this point, I really hope you are impressed by this new functionality. However, I can understand any initial hesitance in accepting the values without validation. You will be pleased to hear we extensively validate our results against the industry leading standalone melt flow analysis package across multiple parts and across our entire range of baseline supported injection molded resins with the average difference being only 7% between our results and the leading standalone package.

AC: As well as internal validation against an industry leading flow analysis package, we have also had great feedback from one of our early adopter customers, Scania. One of their senior development and cost engineers said they were following. He said, “You know it’s valuable because the tonnage is exactly according to whatever the supplier told me.” He followed on to say, “You cannot imagine how it simplifies my life to have a tool like this one implemented. I do not need to be answering to every colleague here, other users do not require much monitoring. What takes longer is the mold flow advisor setup actually, referencing the time that aPriori saves by not having to iterate manually through those simulations.” And lastly, he said, “I must recognize that this module could be one of the best improvements ever that I’ve seen in this software.” Glowing praise indeed.

Process Specific Results

AC: Now that we are confident in the quality of the calculations, I also want to share some other features that offer total control, especially for expert users. I recommend any expert user set Part Details, Default Table to All Data. This gives you the entire suite of outputs from aPriori and allows you to interrogate them thoroughly. The custom outputs give the user a snapshot of the most important process specific results. We can interrogate the numbers and calculations that aPriori uses via the formula dependencies functionality. In the example here, we are looking at cycle time.

AC: Not only can we see what goes into cycle time calculation, but we can override those inputs even in real time to see the impact of any input change to the final results. This is invaluable functionality for fact-based negotiations, where we may want to validate a supplier’s justification for the delta between aPriori’s numbers and theirs. We have a tooling report that provides insight into the cost breakdown of the tool, the configuration, and parameters of the mold base, as well as details of the tooling actions.

AC: This provides a mechanism to share data with colleagues who are not yet aPriori users and for them to utilize this information during negotiations. Nikola Motors have used these very reports in negotiating on average 20 to 30% savings on purchasing hard tooling. In addition to the major enhancements we have made to cooling time via wall thickness recognition and clamp force calculation via flow analysis, we have also been making other enhancements too, including enhancements to our tool finishes. We have updated our tool finishes to align with SPI specifications as well as using MultiTech standards for our textures.

Material Injection Pressures

AC: We have made enhancements to how we calculate and consider regrind. We now dynamically adjust the process regrind with respect to what is available given the specific production parameters up to the limit in which you set for your re-growing percentage. We have made enhancements to our awareness of plasticization time, we calculate plasticization and we will set it as the long pull for cycle time if we detect that it is longer than the injection, cooling, ejection and reset cycle. We have made enhancements to our material injection pressures and how the user can control them. While doing the flow appraisal development, we identified the need to update some of our material injection pressures. We also updated the process setup option to give the user more insight into the pressure range for their select material. We have also made updates to how we set draw direction.

AC: Historically, aPriori has provided a powerful proprietary algorithm for analyzing the geometry of molded parts and determining the draw direction. We use this algorithm in our various plastic molding, casting, and forging process groups. In some cases, the draw direction selected by aPriori is different than the intended as designed draw direction. APriori does provide the ability for an end user to specify the desired draw direction interactively, however, this requires a user to inspect and edit the draw direction if needed.

aPriori Bulk Costing & Analysis

AC: This new functionality, aPriori can now be configured to read and use designated data references in the CAD file as the specified draw direction rather than using the proprietary algorithm. This eliminates the need to manually review and occasionally update the chosen draw direction of your parts. This functionality is particularly useful for supporting high volume and light site workflows which leverage aP Generate or aPriori bulk costing and analysis. Which brings me nicely into our next section on automation, reporting and value realization.

AC: We have multiple options for increasing throughput, but in this section, I am going to concentrate on bulk costing. Bulk costing allows the analysis of large batches of different parts or different scenarios of the same part driven by a spreadsheet. This particular example is of the same part but made out of different materials. I have given the scenario names to reflect this, I have defined the material here, and lastly, we can see I have the ability to set the major process set of options two. This functionality allows me to quickly generate vast quantities of data that ultimately allow me to identify opportunities and find cost savings.

AC: We can use this data in different use cases. For example, in design engineering, we can identify design issues for the parts we expect to have the largest spend on, dig into what those issues may be. AP Generate lends itself particularly well to this workflow as the analysis happens autonomously without the design engineer having to interact with aPriori at all until it comes time to review potentially impactful design issues. For sourcing, we can use bulk costing to perform a regional analysis to help inform decisions on the next NPI project or indeed potentially move existing production. We can interrogate this data piece per level or aggregate level, so we can see what our specific part impact is or what an overall view is of the different regions.

AC: Additionally, why do not we try doing a razors and blades analysis of your plastic molded part spend, help identify if any suppliers are hooking you with loss leaders and making their money back elsewhere. AP Analytics allows you to aggregate data to inform yourselves on the reality of your business transactions with your supply base and negotiate where applicable. In this example, we can see that even though we have great parity on piece part costs, we are potentially being overcharged on tooling and ultimately overcharged overall by an additional 15%. I want to leave you with my favorite plastic molding sourcing strategy. It is my favorite because of its pure simplicity. It is called the cost per cycle time analysis. The only bit of data you need from your supplier is something you have undoubtedly already been given, and that is quoted cost.

Manufacturing Rate For Parts

AC: Firstly, we cost a range of parts from the supplier and export the aPriori data. We then filter the data for only parts made on a certain tonnage of machine. From there, we can build a chart like this. If we back out the material cost, which we should have an idea of, given the commodity pricing and any associated SG&A margin for that material, we are left with the manufacturing cost. If we then divide this manufacturing cost by the cycle time, we get our cost per cycle time, or you can think of it as the manufacturing rate for these parts.

AC: Given we filtered on parts that are manufactured on the same size machine, we would expect them to have the same manufacturing rate. From a plot like this, we can identify our trend and also our outliers. Once outliers are identified, we can approach our supplier and validate the in-trend parts as having the correct production parameters. Once this validation is done, we then introduce our outlier parts and request an explanation for the price difference against our benchmark part.

AC: If the supplier is unable to justify the difference in cost per cycle time priority, then you find yourself in a strong position to negotiate on price. As always though, the aim is not to squeeze your supplier’s margin but to ensure you are paying a consistent fair market price. This strategy was pioneered by our applied services team. It has been executed by them many times and yielded very significant savings from multiple customers. Here is an example from last year where a new customer was able to realize $170,000 of annualized savings on only four plastic molded parts.

AC: I encourage you to try it for yourself, you should already have all the data you need. Thanks for listening, and if you have any questions feel free to contact me, your customer success manager, or anyone from our marketing team.