“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.

5 Software Tools To Estimate The Production Economics In Injection Moulding

Hello and welcome to this blog post on 5 tools to estimate production economics in injection moulding.  A series production of an injection moulding business may comprise thousands of produced parts monthly. Therefore, optimizing costs per piece can substantially impact the business financial outcome. Based upon literature research [1] and my own experience, I have compiled 5 injection moulding software tools to improve your production economics.

1. Injection Molding Cycle Time Estimator:

It is mainly used to estimate the time of an injection moulding cycle, which in turn will support a better production planning.

Features: The estimation takes into consideration the resin type. Furthermore, it allows a machine to set override for temperatures. For cooling, two types of estimation methods are available: centerline and average cooling of the wall. It has an error checking included and an easy Windows Forms interface. The software is freeware.

Source

2. ProMax-One™ Plastic Part Cost Estimator by InjectNet:

This program calculates the total cost per part offering a cost breakdown where individual cost positions are detailed.

Features: The following boundary conditions are used: material, labour, mould cost, machinery, maintenance, cavity information and general project information (timeframe, etc.). This software is in its freeware version more complete than the Injection Molding Cycle Time Estimator from 1).

Source

3. CostMate® by UL Prospector

CostMate® is part of UL Prospector plastics search engine.

Features: It can accurately estimate the cost of producing an injection moulded part and it considers costs associated to shipping as well as packing.  Additionally, it can generate a report of material price, machine, secondary costs, profit and total quote. Its basic version is free.

Source

4. DFM Concurrent Costing® by Boothroyd Dewhurst Inc.

This software is mainly intended for the design of the part stage.  

Features: You can import your part geometry and you can customize the cost estimate inputs. Furthermore, you can import variables from your own injection moulding machine. Geometry calculations can be done within the program too. You can compare alternative production processes and materials for manufacturing your desired part. This is a commercial paid software.

Source

5. CalcMaster® by Schoenberg & Partners

This commercial software is a cost estimator and a good design assistant too.

Features: CalcMaster® estimates the most economical number of cavities for your mould. Furthermore, it is able to take the all over project hours cost (design and manufacturing) into account.

Source

Bonus: Injection molding cost estimator by Custompart

The injection molding cost estimator by Custompart is a handy tool which is free and web-based allowing you to quickly assess your production metrics. 

Try it out here

Overarching, the commercial software solutions, DFM Concurrent Costing and CalcMaster, are most comprehensive regarding decision making on the final part costs. Nevertheless, ProMax-One™ can be a good choice to start with before investing into a commercial software package. It offers already the calculation of several parameters such as cavity data, project timeframe and material data.

Enjoy trying out some of the programs and thanks for reading!

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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Literature:

[1] M.A. Selles, Analysis and review of different tools to calculate the production economics in injection molding, The 7th International Conference Interdisciplinarity in Engineering (INTER-ENG 2013)

High performance polymers used in children buggies

Weight reduction to save energy and costs has been and will continue to be a constant topic of interest in the aircraft and automotive industries. In this regard, much has been done toward replacement of metals especially the heaviest ones by lighter materials still able to fulfill application demands. Nevertheless, there are other industries where metal replacement and consequent weight reduction can also make life easier especially when you have children. I am talking about children buggies. Most buggies have between 8.5 kg and 10 kg. Heaviest ones may range from 12 kg to 17 kg in total weight (frame plus seat). The weight is a result of using aluminum, which is already among the lightest metal materials (2700 kg/m³). Furthermore, it is naturally resistant to corrosion. Is it still possible to make buggies lighter while keeping their current application suitability? Yes, by using high performance polymers.

Following, I will present you a commercial example of the company Quinny that won the 2014 Red Dot ‘Best of the Best’ Award for their 5 kg Yezz buggy [1,2].

5 kg Yezz buggy using PARA frames [1,2]

Why is it only 5 kg heavy and still a high-end performer?

The frame of the buggy is made of several frame parts using the high performance polymer, polyarylamide, commonly known as PARA (or MXD6), reinforced with glass fibers (50 wt%) [3]. The weight reduction is achieved by the reduced density of PARA, 1600 kg/m³. The high-end performance comparable to metal is achieved by the aromatic amide macromolecular structure of PARA, which provides this polymer with inherent high stiffness and strength. For instances, the tensile modulus of PARA can reach values up to 23 GPa at 20°C.

In terms of processing, a major added value of PARA is that it can be loaded up to 60%wt with glass fibers for reinforcement purposes [4] without this to be noticed in the moulded part. The latter will still exhibit a smooth surface with no evidence of contained glass fibers. Such surface allows excellent painting and/or metallization. Additionally, PARA has good flow properties at melting temperatures, which allow the moulding of parts as thin as 0.5 mm. For these reasons, PARA can be a suitable material when complex parts requiring high stiffness in combination with superior surface quality are needed. Accordingly, premium automotive interiors could be of interest.

Next time when you’re are looking for a buggy keep in mind that there are lightweight solutions to make travelling activities with your children easier.

Thanks for reading!
Greetings,
Herwig Juster


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


Literature:
[1] http://www.juniormagazine.co.uk/interiors-and-lifestyle-awards/quinny-yezz-best-lightweight-buggy-design-junior-design-awards-2014-highly-commended/18838.html
[2] http://www.quinny.com/stroller-buggy/reviews/quinny-yezz-red-dot-award/
[3] http://www.quinny.de/de-de/kinderwagen-buggy/yezz-air/
[4] https://www.solvay.com/en/markets-and-products/featured-products/ixef.html