Plastics Part Design: 10 “Holy” Design Rules For Injection Moulded Products I Design Guidelines For Injection Molded Parts I DFM

In today’s post I would like to give you 10 design rules which can be used as a checklist or little helper when finishing up your part design for injection moulded products and considering Design for Manufacturing (DFM).

Let’s start by listing all the 10 at once and then present detail information for each rule [1, 2, 3]:

1. Wall thickness as thin as possible
2. Continuous wall thickness to prevent accumulation of mass
3. Corners and edges with radius
4. Ribs designed for moulding: 40-60% of wall thickness for ribs
5. Avoid plane and even surfaces
6. Use draft angels http://upmold.com/draft-angle-chart/
7. Avoid undercut sections
8. No more accurate machining as necessary
9. Check for possibilities of function integration
10. Past performance of design can be guarantee of future results

1. Wall thickness as thin as possible: wall thickness has influence on the following factors:

• The allover part mass and therefore the material costs
• Cycle time
• Surface quality, warpage and shrinkage cavities
• Flow length
• Tolerances
• Stiffness build-up (use ribs)
• Orientation of molecules and additives such as glass fibers
   

Furthermore, designers seek for thin wall thicknesses since the latter enlarges the cooling time to the square (cooling time = wall thickness^2/ thermal conductivity). It has also the side benefit of saving material costs. Tolerances play an important role in terms of shrinkage and warpage. They are defined for plastic parts in several standards. One standard used in central Europe is DIN 16901. Here, you can find tolerances <1% for normal injection moulding, <0.6% for technical moulding and <0.3 for precision moulding. For achieving your production tolerances, the absolute value of your shrinkage is not as important as the shrinkage spread/deviation resulting through the process over time. Increasing the tolerances will not lead to a better part, it will most probably lead to a lower overall yield.

2. Continuous wall thickness to prevent accumulation of mass:

Different wall thickness influences mainly:

• Creation of porosity
• Sink marks
• Internal stresses (crystallization, shrinkage, polymerization) and warpage
  

When designing parts, it is important to consider that thick areas will take longer to cool down, especially when these are located far from the injection location and connected over thin wall areas. As a result, full freezing of the part does not happen in the packing phase and porosity and sink marks will be present in the final part. In the figure below, an example of how to connect the so called “eyes” to the main part.



3. Corners and edges with radius

In case you have a wall thicknesses change (thicker to thinner; thinner to thicker) or have corners, it is necessary to apply a radius for lowering the stresses.

The graph below shows the optimal radii as a function of wall thickness.

4. Ribs designed for moulding: 40-60% of wall thickness for ribs

There are several ways to increase the overall stiffness of your part:

• Increase the wall thickness: the cross sec­tional moment of inertia I = b*h^3/ 12 shows that increasing the wall thickness will increase the stiffness of your part to the power of three. It is more effective than changing the material. However, cooling time will increase significantly too. Therefore, the optimum needs to be found.
• Material selection: changing to a material with high modulus.
• Beads: usually with a factor of 1.8 times more effective than a rib in the same dimensions.
• Ribs: they are just getting effective when they are 5-10 times higher than the wall thickness and should be made with a thickness of 40-60% of the original wall. Injection moulding location and filling direction (molecular orientation) affects how ribs will perform in a later stage.

5. Avoid plane and even surfaces

The moulding of plane and even surfaces is one of the most challenging processes since the part tends to have sink marks and buckles. The reason for that can be a heterogeneous cooling process after moulding, or shrinkage due to variable wall thickness. Ineffective deployment of the holding pressure may also lead to buckling. It is difficult to replicate buckling since it is an unstable phenomenon.

6. Use draft angels

The cooled down polymer melt shrinks onto the mould cores which will be the inside of the final part. Draft angels are needed for the removal of the frozen part form the mould cores. A guidance on draft angels can be found below.

7. Avoid undercut sections

In nowadays injection tools is difficult to avoid undercut sections due to increased parts complexity. Polymers can withstand a forced ejection as long as the resulting stresses are within the yield stress of the polymers. As such the part can recover the deformation. Other ways of solving undercuts associated issues are:

• Movable sliders or collapsible cores
• Melting cores
• Utilization of split clamping design and subsequent laser weld of the resulting parts after moulding

For avoiding undercuts, creative design solutions are available. One of them is to use snap-fits.

8. No more accurate machining as necessary

When tolerances are set to high, quality will not automatically increase. Most of the times the opposite is the case: total yield of parts decreases. Therefore, parts need to have only the necessary tolerance to still fulfill their function.

9. Check for possibilities of function integration

Function integration for having off-tool parts are challenging for part designers and tool makers, since overall tool costs will increase. However, it allows keeping a competitive edge in high wage countries. Well known examples for function integration are:

• Metallic inserts like screw sockets and plugs
• Hinge joints: off-tool monkey
• Detachable screw joints
• Multi-component moulding
• Film hinges

10. Past performance of design can be guarantee of future results

On the stock market you get always the advice that past performance is not an indication for future performance. In case of plastics design this is not the case. Looking back at successful launched plastic products you can learn from them. Incorporation of proven design helps launch your part solution in a faster way.

Thanks for reading/watching and #findoutaboutplastics

Greetings

Herwig Juster

Interested in my monthly blog posts – then subscribe here and receive my high performance polymers knowledge matrix.

New to my Find Out About Plastics Blog - check out the start here section

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My dedicated site for Polymer Material Selection

Literature:

[1] Keuerleber and Eyerer: Konstruieren und Gestalten mit Kuntstoffen, 2007

[2] Eric Larson – Thermoplastic Material Selection

[3] http://upmold.com/draft-angle-chart/

Polymeric Material Selection: A Critical Factor In Making Successful Plastics Parts

Holding a high quality plastic part in your hands is a result of several product development steps. Having your product development and production process strategy properly aligned is half the success. The other half comes by considering five factors which influence your outcome in having best in class injection moulded parts (Figure 1) [1]:

1. Part design: there are design rules for plastics part, especially for injection moulded parts which need to be followed. Polymers have anisotropic behavior compared to isotropic metal parts.
2. Material selection: once application requirements are established and the base design of the part is done, selecting the material can start.
3. Mould design and construction: designed in a way that the mould can withstand the moulding process and the polymer.
4. Moulding machine selection: when the mould design is completed, the injection moulding machine selection should be done or it can be done in parallel to the mould design, depending on the data available.
5. Moulding process: optimization of the process is the last step and often done incorrectly or not at all.

Figure 1: “From Art to Part”: Material Selection as one critical factor in successful plastic part production [1].

In this post, I keep the focus on the “material selection” factor, since it is a vital one. After the basic part design is done, it is time to review the part performance requirements. In general, a separation of material selection based on performance, processing, and costs can be done. Following questions you need to answer for your part:

- Which areas of performance do I need to consider for this application?

- Are mechanical performance criteria (strength, stiffness, toughness) dominating?

- Are electrical performance criteria (insulating polymers vs. conductive polymers) dominating?

- Are environmental effects (temperature, chemicals, radiation, time) dominating?

Furthermore, tolerance criteria on the part itself need to be taken into account. In case you have a tight tolerance part, low shrinkage materials are the preferred choice. Having thick sections in your part, filled polymers can help obtaining a good filled part.

After gathering all the data which is needed to answer the questions from above, you can start your material selection procedure and make your material shortlist for decision making. Usually, a typical material selection procedure covers three steps [3]:

1. Application screening
2. Generic family and specific grade identification
3. Process selection and cost analysis

These three steps reconcile with the five critical factors for the making successful plastics parts.

Here are 9 more tips what can be considered in the phase of material selection [2]:

1. Stress/strain curve: for plastics the stress/strain behavior is usually not linear up to yield. There are cases where the yield may be very slight or does not exist at all.
2. Modulus of elasticity in tension vs. compression: the E-modulus in tension is not necessarily the same as that in compression.
3. Young’s modulus (E-modulus): the plastic modulus of elasticity is low compared to that of metals.
4. Plastics show anisotropic behavior: injection moulded parts made out of fiber reinforced plastics demonstrate anisotropic behavior.
5. Mechanical behavior: in plastics parts mechanical behavior is influenced by the rate of straining of the material. It is a function of temperature and time as well.
6. Creeping: in comparison to metals, plastic parts creep under load with time.
7. Reduction in strength: plastic parts show a decrease in the strength with time. This is the case with static loads too.
8. Environmental conditions: material properties of polymer-based products may change in certain environmental conditions.
9. Additive package: most plastics have an additive package consisting out of heat stabilizers, fillers and glass reinforcements and this must be considered when specifying the material.
   

Once the material is chosen, the mould design (factor 3), injection moulding machine selection (factor 4) and processing (factor 5) can kick off.

Since there is not always a full engineering of the material properties needed, time saving material selection tips can help. Here are some rules of thumb for making an educated guess on plastics material selection [4]:

• Trying out acrylonitrile butadiene styrene (ABS): it works for many applications and is in a reasonable price range. It is strong and relatively though, combined with a low melting point and good processing properties.
• For a cheap solution and when surface aesthetics are not critical, polypropylene (PP) will do the job.
• For having increased temperature resistance as well as higher impact resistance, polycarbonate (PC) is the next best candidate going from ABS.
• For having a good overall aesthetics and transparency, polymethylmethacrylate (PMMA) is your material of choice. The downside is that it can be too brittle for certain applications. Considering a transparent PC, it will be tougher than PMMA, however the surface aesthetics might not fulfill your set of needs.
• For higher engineering demands, aliphatic nylons are the best way to go. Particularly, the polyamide 6.6-GF30 is well established in lots of engineering applications, especially in Automotive. When higher temperatures are needed (120-140°C), aromatic polyamides (e.g. polyphthalamide (PPA)) will do the job.

Apart of the aforementioned guides, I developed a systematic way of selecting polymers which uses a funnel method. Here you can read an introduction and my book on this topic is available here . 

Success with your next material selection!

Thank you for reading!

Herwig Juster

Interested to talk with me about your polymer material selection, sustainability, and part design needs - here you can contact me 

Interested in my monthly blog posts – then subscribe here and receive my high performance polymers knowledge matrix.

New to my Find Out About Plastics Blog – check out the start here section

My YouTube channel

My personal website

Literature:

[1] Distinctive Plastics Inc.:  The 5 critical factors to produce a succesfull injection moulded product, 2011

[2] B. S. Benjamin, "Structural Design with Plastics," Van Nostrand-Reinhold, 1961.

[3] Paul F. Kusy: Plastics Material Selection Guide, 1976

[4] Proto Labs: Materials Matter – The Material Selection Process

“Data is the new plastic” - Data Algorithms in….Plastics Injection Moulding

Hello and welcome back to my “data is the new plastic” blog post series.

In this post I will scrutinize how plastics processing companies, in particular injection moulding operations increase efficiency using algorithms. The main question evolves around which algorithms can we use to improve our processing operations and which data we have to provide to make these operations successful?

“The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.

Since decades, quality departments collected data from injection moulding machines for a production run and ensure the defined set of quality levels. Once quality is fulfilled, data will be stored and is available in case of complains. There is much more potential in the data which is stored over time. Algorithms (Wikipedia definition: “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer”) enable the analysis of large data sets. They allow a descriptive or predictive (future) look at your production facility. Moulders are under constant cost pressure, especially in the Automotive industry. Therefore, applying data analytics might bring you the 2-3% improvement overall needed to keep efficiency innovation alive and competition away. Looking from a broader angle, every task in business operations has a potential digital component. Average business tasks will disappear into the cloud.

Based on the DataIkuBlog post , I made a table to give you an overview on algorithms used in data mining:

Following a list of scientific publication topics on how algorithms can be applied in plastics processing is shown:

1) Taguchi method: optimization of injection moulding machine parameters and quality characteristics [1-3].

2) Artificial Neural Networks: process modelling, parameter optimization and quality production [4-8].

3) Fuzzy logic: predict flash of mixed materials [9].

4) Generic Algorithms in injection moulding: optimize the parameters of the process [10].

5) Response Surface Methodology: create a non-linear model of the process which is used to control process [11-12].

6) Rule based expert systems and case based reason techniques: design plastic injection processes [13-14].

7) Linear Regression Models: predict production [15].

8) Support Vector Machines: quality monitoring of the process [16]

Now a quick reality check: how does it look like in industrial practice?

Most of the algorithms listed before are not yet implemented completely in real industrial environment, however current innovation speed together with the Open Platform Communication (OPC) standard (Euromap 77) is changing this. Data will be more accessible and turned into valuable information used to improve efficiency, predictions and decisions.

A positive outlook for processors, or putting it all together:

Analyzing your business data will lead to a massive productivity advantage and as such more revenue for your business. Extra capital can be used then to expand other fields of the Industrial Internet of Things. Over this route, new digital business models are generated over which you can attract more customers. Platform business models, especially B-2-B platforms (“Platform as a Service”) show here an enormous potential in plastics industry. They need to be open and independent. Currently more and more B-2-C platforms are sweeping over and interest in B-2-B platforms is increasing. At the end, new business models should be explored together by using deep technology (artificial intelligence to just name one). Plastics companies should at least in a theoretical experiment think how a platform could look like. It could look like this:

Example of platform economy in plastics industry

There will be main customers such as OEMs and Tier-1s on one hand and all kind of plastics business such as tooling, moulding and service providers on the other. The latter can function in a complementary manner. For example, when a part from a moulding company is ordered, a simulation and a suggestion for optimal material selection can be provided by the first when possible or by a complementary external party connected to the main platform (comprehensive global scale). In this way, customer service will be stronger than ever. Once again, OPC will be the enabler of such services.  

Data analytics as interface between IT and plastics engineering will enable companies and customers to strive their ventures. On the other hand, neglecting data analytics can be fatal for business. First changes need to be done now.

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” – Jim Barksdale, former Netscape CEO

“Without data you're just another person with an opinion.” - W. Edwards Deming

Thanks for reading and till next time!

Herwig Juster

New to my Findoutaboutplastics Blog – check out the start here section.

Literature:

[1] Chung-Feng J. K., Te-Li S, “Optimization of multiple qualitycharacteristics for polyether ether ketone injection molding process”,Fibers and Polymers, Dec 2006, Volume 7, Issue 4, pp 404-413.

[2] Chung-Feng J. K., Te-Li S., “Optimization of Injection Molding Processing Parameters for LCD Light-Guide Plates”, Journal of Materials Engineering and Performance, Oct 2007, Volume 16, Issue 5, pp 539-548.

[3] Oktem, Tuncay Erzurumlu, Ibrahim Uzman, “Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part”, Materials & Design, Volume 28, Issue 4, 2007, Pages 1271–1278.

[4] Jie-Ren S., “Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network”, The International Journal of Advanced Manufacturing Technology, April 2008, Volume 36, Issue 11-12, pp 1091-1103.

[5] Sadeghi B.H.M., “A BP-neural network predictor model for plastic injection molding process” Journal of Materials Processing Technology, Volume 103, Issue 3, 17 July 2000, Pages 411–416.

[6] Ozcelik B., Erzurumlu T.,” Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm”, Journal of Materials Processing Technology, Volume 171, Issue 3, 1 February 2006, Pages 437–445.

[7] Shi F., Lou Z.L., Zhang Y.Q., F. Shi, Lu J.G., “Optimisation of Plastic Injection Moulding Process with Soft Computing”, The International Journal of Advanced Manufacturing Technology, June 2003, Volume 21, Issue 9, pp 656-661.

[8] Chen W.C, Tai P.H., Wang M.W, Deng W.J., Chen C.T. “A neural network-based approach for dynamic quality prediction in a plastic injection molding process”. Expert Systems with Applications, Volume 35, Issue 3, October 2008, Pages 843–849.

[9] Zhu J., Chen J.C., “Fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) in injection molding operations” The International Journal of Advanced Manufacturing Technology, June 2006, Volume 29, Issue 3-4, pp 308-316.

[10] Shi F., Lou Z.L., Zhang Y.Q, Lu J.G, “Optimisation of Plastic Injection Moulding Process with Soft Computing” The International Journal of Advanced Manufacturing Technology. June 2003, Volume 21, Issue 9, pp 656-661.

[11] Mathivanan D., Parthasarathy N.S. “Prediction of sink depths using nonlinear modeling of injection molding variables“, The International Journal of Advanced Manufacturing Technology, August 2009, Volume 43, Issue 7-8, pp 654-663.

[12] Mathivanan D., Parthasarathy N.S., “Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm” The International Journal of Advanced Manufacturing Technology, December 2009, Volume 45, Issue 9-10, pp 867-874.

[13] Kwong C.K., Smith G.F., “A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach” The International Journal of Advanced Manufacturing Technology, 1998, Volume 14, Issue 5, pp 350-357.

[14] Shelesh-Nezhad K., Siores E. “An intelligent system for plastic injection molding process design” Journal of Materials Processing Technology, Volume 63, Issues 1–3, January 1997, Pages 458–462.

[15] DasNeogi P., Cudney E., Adekpedjou A., “Comparing the Predictive Ability of T-Method and Cobb-Douglas Production Function for Warranty Data”, ASME 2009 International Mechanical Engineering Congress and Exposition (IMECE2009) , November 13–19, 2009 , Lake Buena Vista, Florida, USA.

[16] Ribeiro B., “Support vector machines for quality monitoring in a plastic injection molding process”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Aug 2005, Volume 35, Issue 3, Pages 401 – 410.

[17] https://www.featuredcustomers.com/vendor/ge-digital/customers/toray-plastics-inc

“Data is the new plastic” - Data algorithms in ….. Plastic Materials Selection

Hello and welcome to this blog post entitled “data is the new plastic”.

“In God we trust, all others must bring data.” W. Edwards Deming

As data science evolves throughout the plastics industry, materials, moulds and machines information come closer together by means of digital services. Following, are the three main business interfaces where data science is playing or will play a crucial role:

1. Resin suppliers and their customers: Better decision making together with the customer with regards to material proposal and selection (optimal material at the right time for the right price).
2. Plastics processing: Increased efficiency and streamline processes. For example, Toray Plastics optimized their manufacturing operational performance by using GE Digital’s Big Data Analytics [9].
3. Product designers and tool makers: Better prediction of which applications will evolve due to touch points with customer (e.g. industrial marketing offers by GadflyZone [10]) and better product design using data and simulation like CFD and FEA

As data analytics and data mining operations find their way through the plastics business value chain, business operations such as marketing, sales, social selling and customer relationship management (CRM) evolve into new disciplines - Plastics Information Technology (PIT). Data-driven marketing actions ensure a better customer journey.



Figure 1: Data generated from polymeric materials, machines and moulds is the foundation for Plastics Information Technology.

In this post, I focus on point 1) from above and thus on data driven decision making on the optimal material for a certain application. I will not cover the conventional selection methods, since most of us are familiar with them. Especially for automotive industry, I would like to refer to Paul F. Kusy’s material selection guide [11]. Basically, we always have a material screening phase and a material comparing phase. In the first one, elimination of the obvious unsuitable materials takes place. The second phase involves a multi-level decision process which finishes with a material decision step.

Al-Oqla et. al. [8] explains that the design of an engineering part has three elements: specifying a shape, selecting a material, and choosing a proper manufacturing process. The “selecting the material” element will be supported more and more by algorithms.

Figure 2: Overview of the data science approach for polymeric materials selection.

There are three main groups of algorithms which can be applied to the screening phase of materials selection and comparison phase (Figure 2):

1) Artificial intelligence (AI) methods: AI is used for solving complex problems by implementing intelligence for processing unstructured knowledge. Looking back, an engineer could inform himself in engineering handbooks, scientific journals and own experience. As the amount of polymeric materials and their compounds are increasing, an efficient managing of engineering data is necessary. Here, AI methods can play a key role. AI can access over the network (and stored in the cloud) all kind of information and does not need to make mistakes to learn. Following are some methods used in AI for materials selection:

• Computer-aided materials selection systems [1]
• Knowledge-based systems [6]
• Case-based reasoning [2]
• Neural networks [3]

2) Genetic algorithms combined with neural network: Connecting the desired mechanical properties with the polymer properties is enabled by genetic algorithms and neural network. It was found that neural network reduces the time and costs for selecting the optimum polymeric material. Al-Oqla et al. [8] has shown this by applying genetic algorithms and neural network for selecting the optimum composite material for different applications. In this case, fuzzy oriented models were used to select the relationship between performance requirements and material properties.

3) Optimization: three optimization techniques are shown for enabling better materials selection using an optimization algorithm.

• Mathematical programming [5]
• Computer simulation [7]: Simulation tools like for example Moldflow and Sigmasoft have an extensive database on materials which allow you to test materials of different costs in a virtual domain.  It is possible to couple the results to a finite element analysis to simulate the mechanical performance too.
• Genetic algorithm [2]

The result of your screening process will be two to three materials which can do the job. Following, is the second phase of the material selection. This itself is a multi-level decision making process to come to a definite outcome. There are the following methods available which are supported by algorithms:

• Multiple attribute decision making methods (MADM) [12]: Focus on making the decision based on the attributes of the material which can be expressed as exact numeric values, Boolean values or just as ranking (best-good-bad).
• Multiple objective decision-making methods (MODMM) [12]: Focus on making a material decision considering different desirable objectives. The design engineer has the objective to develop the lightest and/or cheapest solution considering the boundary conditions.
• Polymer processing decision solver [4]: The main element of this program is a decision matrix which has as input criteria and options. As a result, the user will get a weighting of the options with points. The options are the different polymers to be decided upon. 

Let’s start harvesting insights from your data generated by our plastics products and get a competitive edge by proposing the optimal material!

Thanks for reading and till next time!

Greetings, Herwig Juster

New to my Find Out About Plastics Blog – check out the start here section.

Literature:

[1] Jahan, A., Ismail, M. Y., Mustapha, F., & Sapuan, S. M. (2010a). Material selection based on ordinal data. Materials & Design, 31, 3180–3187.

[2] Amen, R., & Vomacka, P. (2001). Case-based reasoning as a tool for materials selection. Materials & Design, 22, 353–358.

[3] Goel, V., & Chen, J. (1996). Application of expert network for material selection in engineering design. Computers in Industry, 30, 87–101.

[4] H. Juster, Strategic decision-making in the product development of polymer components, Master thesis, University Leoben, Austria, 2015 

[5] A. Jahan, M.Y. Ismail, S.M. Sapuan, F. Mustapha, Material screening and choosing methods – A review, Materials & Design, Volume 31, Issue 2, 2010, Pages 696-705, ISSN 0261-3069, https://doi.org/10.1016/j.matdes.2009.08.013.(http://www.sciencedirect.com/science/article/pii/S0261306909004361)

[6] Sapuan, S. M. (2001). A knowledge-based system for materials selection in mechanical engineering design. Materials & Design, 22, 687–695.

[7] SIGMASOFT® Virtual Molding: The best Ally in Resin Selection

[8] AL-Oqla, F. M., & Sapuan, S. M. (2015a). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. Journal of the Minerals Metals and Materials Society, 67(10), 2450–2463.

[9] https://www.featuredcustomers.com/vendor/ge-digital/customers/toray-plastics-inc

[10] http://gadflyzone.com/

[11] Kusy, P.F., Plastic Materials Selection Guide, 1976

[12] AL-Oqla, F. M., & Omar, A. A. (2015). An expert-based model for selecting the most suitable substrate material type for antenna circuits. International Journal of Electronics, 102, 1044–1055.