Quality Assurance is in Demand Everywhere

Plastic recycling sounds quite simple: collect plastic wastes, reprocess them and then manufacture new products – and you’re done! Unfortunately, it’s a lot more complicated than that in practice.

Besides difficulties in collecting and sorting wastes, and with plastic mixtures and composite materials that are difficult to separate, recycling is also made more difficult by the wear and tear on the plastics.

Plastics are subject to various environmental influences during use, which can lead to thermo-oxidative degradation and shortening of the polymer chains. This has effects on their mechanical properties and subsequent processing. Both of these can be compensated to a certain extent by the use of additives. However, this requires checking the specific condition of the plastic wastes. These investigations must be carried out continuously for each waste batch in order to ascertain the wear and tear on the material in each case.

In this regard, by the way, recycling is no different from plastic processing. The continuous process monitoring also plays an important role in injection molding, extrusion and compounding. For this, inline processes are being used ever more frequently. If this takes place in a production environment – the keyword here is Industry 4.0 – and if machine learning is used, problems can be identified and often directly remedied while the products are being manufactured. And even, increasingly, without human intervention. You can find current examples of successful quality assurance in the Special in this edition, from page 6.

Florian Streifinger [florian.streifinger@hanser.de]

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Streifinger, Florian

Rubber Injection Molding with  Quality Recognition

Automatic Process Monitoring with iQ Clamp Control and Machine Learning

With the increasing digitalization of injection molding, new process parameters are coming under the spotlight. Engel, together with the Polymer Competence Center Leoben and Montan University Leoben, Austria, is using machine-learning methods to explore the potential of mold breathing for quality assurance in rubber injection molding. This research project was supported by the partner company SKF Sealing Solutions Austria.

Technical rubber products, such as gaskets, dampers or connectors, are critical in many applications for the proper and reliable operation of systems, equipment or vehicles. Nevertheless, economic and environmental factors are forcing manufacturers to integrate ever more functions into their components with ever lower resource consumption [1, 2]. Here, manufacturers are supported in continuing to ensure a constantly high quality by the collection and interpretation of process data [3–5].

The iQ clamp control assistance system of Engel Austria GmbH with mold breathing, provides a useful signal that enables automatic clamping force optimization. It also facilitates monitoring of the production process and manual optimization of quality-relevant parameters, such as changeover point and holding pressure time. It was developed for processing thermoplastics, where it is already widely used [6, 7].

As part of a scientific study, the system was evaluated for elastomers. Chemical crosslinking poses a challenge for this application. The goal of trials by the project partners, Engel, Polymer Competence Center Leoben (PCCL) and Montan University Leoben (MUL), was to detect critical process deviations in the manufacture of rubber parts in real time using the mold breathing signal to detect parts out of tolerance already during the manufacturing process without an additional manual quality control step. To simulate process deviations, the mold temperature was deliberately increased during injection molding without adjusting the crosslinking time accordingly. This causes an impermissible change in the dynamic-mechanical properties of the parts, equivalent to producing rejects as a result of process disturbances.

The process monitoring is performed with the aid of PCA-based methods (principal component analysis). These are machine-learning methods and are capable of utilizing clear correlations between different process signals – e. g. mold breathing (Fig. 1). This allows the error identification rate to be significantly improved compared to standard methods, and temperature-related process disturbances to be visualized.

To allow the PCA, and therefore the correlation between the process and quality data in the production process, to be performed, a training dataset must be generated from the process data for acceptable parts. This training set, which is used as a basis for the subsequent inline error identification, is determined experimentally.

Trainings Set as a Basis for Subsequent Inline Error Identification

The tests were performed in Engel’s technical center on an e-victory 740/220 injection molding machine with a maximum clamping force of 2200 kN. For the mold temperature control, heating platens with electrical heating elements were used, of which the setpoint values could be changed by controlling the injection molding machine. A carbon black-filled nitrile-butadiene rubber (NBR, manufacturer: SKF Sealing Solutions Austria) that is typical for industrial applications was processed into sample parts. In preliminary tests, settings were found for all process parameters, which ensure a stable part production process (Table 1). The heating time was chosen so that, at the point of demolding at a mold temperature of 160 °C, the parts have a degree of crosslinking of 90 %.

The settings were kept constant over 20 cycles, in order to generate training data in cycles 5 to 15 after a short transient oscillation. A constant temperature of 163 °C was established, which was measured with the mold temperature sensor (Fig. 2). From cycle 21, the setpoint value of the heating elements was increased to 180 °C, causing the measured maximum mold temperature also to rise. From cycle 52, the control temperature of the heating elements is reduced to 160 °C again.

With the increase of the setpoint temperature, unexpected temperature deviations resulting from possible malfunctions of the heating or thermal elements were simulated. On this basis, the threshold value for impermissible changes of part quality, as well as the response behavior of the process monitoring methods, were investigated.

The dynamic-mechanical properties of the parts are investigated with an electrodynamic test system, type Instron ElectroPuls E3000 (manufacturer: Illinois Tool Works). The test method used can determine the characteristic material behavior in the pressure oscillating load range. The material’s response to the introduced load is determined as a characteristic value of the tangent of the loss angle tan(δ), which describes the ratio between plastic and elastic material behavior. It thereby provides useful information about the degree of crosslinking.

The tan(δ) values determined in the dynamic tests, as expected, correlate during the test with the measured mold temperature from Figure 2. An increase of tan(δ) means an increase of the plastic components, e. g. because of a lower degree of crosslinking  under the changed crosslinking conditions (Fig. 3a).

In the training phase (cycles 5 to 15), tan(δ) has a mean value of 0.209, with a standard deviation (σ) of 0.002. Starting from this, a tolerance range of ±3 σ is defined. If a part has a value outside this range, it is to be considered an unacceptable part. In the time sequence of the test cycles, this range is left as from cycle 23 and only consistently reached again with cycle 58. The object of statistical process monitoring systems – in this case PCA – is now, while the process is running, to use the process parameters to identify whether the part quality lies within the permissible process window. For assessing the investigated monitoring methods, the individual cycles were marked as acceptable or unacceptable parts corresponding to the marked control limits.

Correlation between Mold Breathing and Cavity Pressure

To confirm that mold breathing is suitable for error recognition, the correlation between the breathing signal and the cavity pressure was first investigated in practical tests (Fig. 4). It is conspicuous that the mold breathing signal only shows significant deflections when the mold is almost full, while the cavity pressure rises as soon as the flow front reaches the sensor (Fig . 4a).

This behavior can be expected since, besides the pressure, mold breathing also takes into account the projected, already filled  surface area. At the switchover point, on the other hand, the peaks of both signals are equally pronounced and can therefore be exactly evaluated. If the peaks of the two signals for all cycles are plotted one on top of the other (Fig. 4b), a linear relationship can be clearly seen.

Process Monitoring with Principal Component Analysis

Processes with multiple variables can be suitably monitored using multivariate statistical methods [8, 9]. For the tests, a PCA-based approach (Info Box) was chosen to reliably identify process fluctuations, and therefore quality fluctuations. The recorded process variables, with the exception of the mold temperature, are then processed by principal component analysis, and the SPE (squared predictive error) statistics for each test cycle are computed (Fig. 3b). Cycles 5 to 15 were used as training data. A common, unilateral intervention limit is calculated from these using the PCA method. If the SPE statistics of a cycle lie beyond this limit, the part is identified as probably unacceptable. Here, the common evaluation of the process variables is found to make the change in mold temperature visible in a statistically significant way, without the need to measure it directly. The change of mold temperature is rapidly identified; only from cycle 55 does the system return to normal operation.

The correct classification rate, i. e. the proportion of cycles that are in each case correctly assigned to acceptable or unacceptable parts, is 85 % in these experiments. The basis for decision-making here lies in the violation of the tan(δ)) intervention limits. The 15 % incorrect classifications are all cycles that ought to be unacceptable parts according to PCA, but are acceptable parts according to tan(δ). The monitoring system would thus be oversensitive. However, the incorrectly classified unacceptable parts are underlain by critical process deviations that are correctly recognized by the system and do not have an impact on the quality feature tan(δ) that is specifically investigated, but can bring other quality features out of tolerance.

All the incorrectly classified data are identified with hollow markers (Fig. 3). The process monitoring system thus classifies cycles 3 and 4 as errors (Fig. 3a), which can be attributed to process conditions at the start of the test that have clearly not yet settled to the steady state. Despite the return to normal process conditions from cycle 51, the dynamic properties of the parts only lie within the control limits again after a delay. It is conspicuous that cycles 60 and 62 are qualified as unacceptable parts, although the measured mold temperature is already in the range of normal conditions again at 160 °C. The system thus also responds to other process disturbances. A comparison with Figure 3b shows that exactly these two cycles have higher tan(δ) values than adjacent cycles.

Something similar can be seen with cycle 23, the tan(δ) value of which is lower than that of the adjacent cycles, and also the SPE statistics lie below the intervention limit. Also in the case of acceptable parts, there seems to be a certain relationship between the SPE statistics and part quality according to tan(δ).

In general, the examples show that despite a strong correlation, none of the evaluated process signals is able to represent the crosslinking process absolutely. All the variables recorded in the experiments describe changes of physical properties of the rubber, especially the flow behavior. However, if the properties of the chemical crosslinking are not in a linear relationship with the physical, the part properties that are primarily determined by the degree of crosslinking cannot be clearly predicted. If all the process signals as well as the multivariate monitoring indicate that the flow properties have changed, even if the predicted tan(δ), which is determined principally by the curing reaction, lies within the intervention limits, then detailed part checking should be performed. Only then can it be assessed whether the unexpected process fluctuations are uncritical or whether they lead to rejects.

Based on the iQ clamp control intelligent assistance system, it was possible, using principal component analysis, to set up a multivariate process monitoring system for rubber injection molding that immediately identifies changes in the process conditions, and classifies the corresponding molded parts as unacceptable. The advantages of this system lie in the low training effort and the possibility of simultaneously processing multiple process variables. Additional information about the significantly better performance compared with standard methods can be requested from the authors. The challenge in elastomer injection molding consists in the fact that most available process signals correlate strongly with the flow properties of the materials, however only weakly with the chemical reactivity, which further sets limits on process signal-based fault identification. The system developed by Engel, PCCL and MUL shows trends in process fluctuations even before they have affected the part quality. This opens up great potential for optimizing series processes in the rubber-processing industry.

[1] Lindsay, J. A.: Practical guide to rubber injection moulding. Smithers Rapra, Shawbury, Shrewsbury, Shropshire 2012

[2] Fasching, M.; Friesenbichler, W.; Pacher, G. A.; Berger, G.: Advanced Mold Concepts to Improve Part Quality and Productivity in Rubber Injection Molding. In: Proceedings of the Regional Conference of the Polymer Processing Society, Graz 2015, pp. 49

[3] Chen, Z.: Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods. Springer Fachmedien, Wiesbaden 2017

[4] Yang, Y.; Chen, X.; Lu, N.: Injection molding process control, monitoring, and optimization. Hanser, München 2016

[5] Zeppenfeld, M.; Hirn, R.; Wardenberg, M.: Wireless Life Cycle Monitoring in the Injection Molded Part. Kunststoffe international 109 (2019) 3, pp. 11–16

[6] Pillwein, G.; Lettner, J.; Giessauf, J.; Steinbichler, G.: Breathing Exercises for Process Optimization. Kunststoffe international 105 (2015) 10, pp. 81–84

[7] Pillwein, G.; Lettner, J.; Lehr, M.: Using Breathing Signals for Process Optimization. Kunststoffe international 110 (2020) 1, pp. 26–31

[8] Hutterer, T.; Berger-Weber, G. R.; Friesenbichler, W.: Fault detection in rubber injection molding with multivariate statistics. Rubber World (2019), pp. 40–44

[9] Hutterer, T.; Berger‐Weber, G. R.; Kerschbaumer, R. C.; Friesenbichler, W.: Rubber injection molding: Applying multivariate statistics to identify quality issues solely from process signals. Polymer Engineering & Science (2020), <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/pen.25604">https://onlinelibrary.wiley.com/doi/full/10.1002/pen.25604</a>

[10] Yang, Y.; Gao, F.: Injection molding product weight: Online prediction and control based on a nonlinear principal component regression model. Polymer Engineering & Science 46 (2006) 4, pp. 540–548

[11] Wold, S.; Geladi, P.; Esbensen, K.; Öhman, J.: Multi-Way Principal Components and PLS-Analysis. Journal of Chemometrics (1987) 1, pp. 41–56

[12] Russell, E. L.; Chiang, L. H.; Braatz, R. D.: Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes. Springer, London 2000. URL: <a href="http://dx.doi.org/10.1007/978">http://dx.doi.org/10.1007/978</a>–1–4471–0409–4

[13] Yang, Y.; Gao, F.: Injection molding product weight: Online prediction and control based on a nonlinear principal component regression model. Polymer Engineering & Science 46 (2006) 4, pp. 540–548

The collection and interpretation of process data supports producers of safety-relevant rubber products in ensuring a constant high quality

Table 1. In preliminary tests, settings ensuring a stable manufacturing process were found for all process parameters. These parameters were held constant in all experiments

Fig. 1. Mold breathing: The mold is compressed by the build-up of the clamping force. During filling, the resulting part generates an opening force, which is counteracted by the compression. The opening force is proportional to the cavity pressure and to the projected surface area 

Fig. 2. The maximum mold temperature at the cycle end follows immediately after the setpoint values of the heating elements

Fig. 3. The tan(δ) values of the elastomer parts are strongly influenced by the mold temperature (left). SPE statistics allow changes in the process conditions potentially leading to rejects to be recognized (right) [9]

Fig. 4. Mold breathing responds with a delay compared to the cavity pressure, but then simultaneously reaches a pronounced peak (left). Maxima of mold breathing and cavity pressure are strongly positively correlated (right)

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Principal component analysis is a dimensionality reduction method for increasing the defect recognition rate during process monitoring. At the same time, the complexity can also be reduced. Its particular suitability for industrial processes lies in the fact that it can eliminate linear dependencies between process signals. More detailed insights into the methodology and other application reports can be found in Russel, Chiang et al., Yang, Chen, et. al. and others [4, 10 – 13].

DI Dr. mont. Thomas Hutterer is a research assistant at the Polymer Competence Center Leoben (PCCL), Leoben, Austria; <a href="mailto:thomas.hutterer@pccl.at">thomas.hutterer@pccl.at</a>

Assoz. Prof. DI Dr. mont. Gerald R. Berger-Weber is a research assistant at the chair of injection molding of plastics at Montan University Leoben; <a href="mailto:gerald.berger@unileoben.ac.at">gerald.berger@unileoben.ac.at</a>

DI Dr. mont. Roman C. Kerschbaumer is group manager for processing technology at the PCCL; <a href="mailto:roman.kerschbaumer@pccl.at">roman.kerschbaumer@pccl.at</a>

Univ.-Prof. DI Dr. mont. Walter Friesenbichler is Head of the Chair of Injection Molding of Plastics at Montan University Leoben; <a href="mailto:walter.friesenbichler@unileoben.ac.at">walter.friesenbichler@unileoben.ac.at</a>

DI Alexander Klutz is development engineer in Global Application Engineering at Engel Austria GmbH Schwertberg, Austria.

DI Thomas Leng is Vice President Global Application Engineering at Engel; <a href="mailto:thomas.leng@engel.at">thomas.leng@engel.at</a>

DI Karlheinz Mayr is Head of Global Application Engineering at Engel; <a href="mailto:karlheinz.mayr@engel.at">karlheinz.mayr@engel.at</a>

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Kunststoffverarbeitung: Verfahren

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Industrie 4.0 für die Qualitätssicherung

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Machine Learning für die Qualitätssicherung

Inline Testing Methods for Monitoring Processes

The quality of final products can be increased by using measurement technology for monitoring production processes. Interest in it is increasing especially for compounding. There are various existing methods of non-destructive testing that can also be used inline in the manufacturing process. The different methods are suitable for different applications: an overview of the current capabilities and limitations of the various inline methods.

Optimizing production processes to avoid production errors is often an important step toward achieving profitability with a new or altered product. To carry out such improvements, the first thing that has to be known is the state-of-the art. Existing processes also profit from metrological monitoring, since it can ensure uniform product quality. Moreover, it enables quick reactions when irregularities happen to occur, and can document product quality according to objective criteria with a view to potential liability issues. In the plastics industry, the demands are rising for process monitoring especially in compounding. By applying suitable measurement methods, resources and time can be saved, and rejects or even complaints minimized.

Non-destructive testing (NDT) is ideal for process monitoring. It encompasses various procedures and makes it possible to exploit relevant parameters during decisive production steps. The measurable parameters far exceed the capabilities of established and already widely used pressure and temperature measurement and include, for example, inline detection of screw wear, filler content and particle size distributions, water content, detection of inclusions of foreign matter, characterization of the degree of melting and the downstream measurement of wall and layer thicknesses of extrudates.

Depending on the application, many aspects of the challenges to a direct inline measurement of these parameters have still not been overcome. Some of the difficulties come from having to deal with the high melt temperatures and pressures, as well as abrasiveness. Even so, numerous applications could already be implemented on an industrial scale, or are currently being introduced to the market. Many of the available non-destructive testing methods are based on mechanical and electromagnetic waves (Fig. 1).

Ultrasonics: Proven, but Temperature-Dependent Technology

Ultrasonic waves are acoustic waves that connect to the audible sound range and cover a spectral range from approx. 20 kHz to 1 GHz. Two well-known examples are the examination of inner organs in medical technology and the natural tracking ability of bats. Ultrasonic waves, however, can also find application in the plastics industry, for example, for characterizing melts online. The technology has also been in use for such a long time, that proven signal processing and robust sensor technology are already in place.

Successful systems have already been developed that record the particle size of filler materials and their content in the melt. The calibration data banks required for this are already available for many applications (Fig. 2). By measuring ultrasound amplitude, they show the dependence of the extinction coefficient on filler content and the corresponding median size of particles. This technology is also capable of determining melt viscosity inline, thereby avoiding the use of expensive online rheometers. In addition to ultrasonic damping, the viscosity of the melt influences, above all, the reliably measurable speed at which ultrasonic waves propagate.

A main disadvantage of ultrasonic methods is the physically determined temperature dependence of the parameters. Its application can be limited by common temperature fluctuations during the processing of plastics. At SKZ – The Plastics Center work is proceeding on compensating them to enable wider marketability.

Optical Spectroscopy: Regulating Color Directly in Production

Optical measurement methods function mostly in the range of visible light or bordering spectral ranges. This includes ultraviolet (UV, 200 – 380 nm), visible (VIS, 380 – 80 0nm) and near infrared light (NIR, 800 – 2500 nm). Optical measurement methods use the reciprocity of this electromagnetic radiation with the plastics melt to examine the materials properties of plastics. Fiber-optical probes are used that illuminate the plastics melt and guide the reflected or transmitted light to a spectrometer. Depending on the wave length range used, various applications are possible. In the VIS range, color measurements usually take place based on color deviations that can be quantified during the process. This enables a reduction of rejects thanks to the shorter reaction times, compared to laboratory measurements (Fig. 3). Coupling measurement with dosing can enable automatic color regulation and dwell time measurement. The challenge here is presented by thermochromism, that is, the temperature dependence of color.

Thanks to the characteristic absorption of NIR radiation in the form of molecular vibrations, spectroscopy can be used to distinguish various contents in compounds qualitatively and quantitatively. For the latter, chemometric models are generally used that combine the measured spectrum with the amount of material examined. However, calibration can prove very complicated, since it has to be created specifically for each combination of materials. That is why the recording of well-defined reference spectra has been adopted as a practical alternative for purposes of comparison.

To be sure, additives can also be quantified in the UV range, but that is utilized for practical reasons only to a small degree, since both the additives as well as the plastic reciprocate under radiation. The wide variety of measuring capabilities of broadband and high-price all-in-one spectrometers are rarely required for compounding. That is why there are simplified solutions tailored to particular targeted parameters, such as hydrolytic materials degradation, or a color change. The SKZ has long supported companies with the selection of a suitable system.

Microwave and Terahertz: Temperature- Independent  and Non-Contacting

Microwave and terahertz methods are in daily use as body scanners at airports and for distance measurement in the automotive field. They use electromagnetic waves bordering on the frequency range of radio waves and the visible range. Compared to x-rays, the frequency range is significantly lower in energy and, therefore, non-ionizing and not hazardous to health. In extrusion, wall thickness measuring systems have become established for measuring individual layers in the micrometer-to-decimeter range.

The characterization of plastics melts has, until now, not become widespread due to the rough production conditions known for the high temperatures and pressures. For more than ten years, the SKZ has been researching the transfer to industrial applications and can, for the first time, now offer a corresponding system technology and its adaptation to individual requirements. It can be applied for the inline detecting of, for example, the degree of melting in plastics (Fig. 4) and screw element wear. Signal amplitude and runtime are measured analogous to ultrasonic technology. Just as screw wear tends to increase the distance between it and the extruder housing, thereby lengthening the signal runtime, the degree of melting affects wave scatter and amplitude in consequence. This principle ensures that the method is fundamentally suited for detecting gel particles and foreign materials (Fig. 5). The method’s temperature independence and non-contacting measuring technique, which considerably reduces maintenance cost, give it a considerable advantage over ultrasonic technology.

The Appropriate Method for the Particular Application

An appropriate non-destructive test and/or measurement method for recording defined quality features can be found for every application. Besides the physical principles, an important role is played by the technical framework, such as access to the product, processing speed, and production environment. Economic factors are also quite relevant. Sensor technology makes sense only if the use of measuring technology reduces rejects and complaint costs, minimizes liability risks, or offers other advantages. The SKZ can offer support with the selection of an appropriate test and measuring method by taking the stated requirements into consideration holistically. Testing methods based on ultrasonic, microwave, and terahertz radiation, as well as spectroscopic methods are at the heart of the activities at the SKZ. In addition to these, methods such as thermography, the use of thermal imaging cameras, or methods for detecting mechanical properties online are objects of development and provide very good approaches to process monitoring.

Fig. 1. An overview of electromagnetic and mechanical waves arranged in the order of frequency and wave length: The red colored areas represent the NDT methods used in plastics processing

Fig. 2. Dependence of measurable ultrasonic amplitude (extinction coefficients) of filler content and the accompanying median size of chalk particles in plastics compounds: By using such databank-integrated calibration curves, both of these quality relevant features can be quantified inline by ultrasound

Fig. 3. Process chain exemplified by color measurement of dyed plastics products: Dyed compounds are manufactured from various raw materials and processed further, either continuously by extrusion, or discontinuously to injection molded products. All along the process chain there are various possibilities for monitoring color quality

Fig. 4. Dependence of a characteristic value, the so-called refractive index determined via terahertz from the degree of melting in a compound or the melt temperature: The degree of melting can be recorded inline by consulting a databank specific to the material

Fig. 5. The dependence of so-called intensity, a characteristic value recorded via terahertz from the stochastic appearance of foreign materials in a compound (left), as well as a microscopic image of a machine-related wear particle in the melt (right): A threshold value (red line, left) can be defined by taking a variety of processing states into consideration beyond which the measuring signal can be univocally consigned to a foreign particle 

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Pierre Pfeffer is a scientific associate in the non-destructive testing group at the SKZ and concerns himself with ultrasonics, among other subjects.

Norbert Halmen is a scientific associate working in the spectroscopy group at the SKZ.

Dr. Thomas Hochrein is the Managing Director of Education & Research at the SKZ.

Giovanni Schober heads the SKZ non-destructive testing group whose goal is to develop, test, and integrate methods into production processes; <a href="mailto:g.schober@skz.de">g.schober@skz.de</a>

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Qualification and Gauge Management Software for Injection-Molded Thermoplastic  and Thermoset Parts

At the beginning of every quality assurance effort lies the individual measurement value. If this value is measured incorrectly, the collected data is unreliable and has an undesirable effect on all downstream quality management processes. The Kunststoff-Institut Lüdenscheid relies on CAQ AG’s software solutions for gauge and qualification management in order to ensure consistently trained and qualified employees, calibrated test equipment, and valid measurement values right from the very beginning – even in today’s age of working from home and decentralized e-learning.

At the beginning of every quality assurance effort lies the individual measurement value. If this value is measured incorrectly, the collected data is unreliable and has an undesirable effect on all downstream quality management processes. The Kunststoff-Institut Lüdenscheid relies on CAQ AG’s software solutions for gauge and qualification management

When dealing with such an incredibly flexible material such as plastic, one rock-solid factor must never be underestimated: quality. The Kunststoff-Institut für die mittelständische Wirtschaft NRW GmbH (KIMW), Lüdenscheid, Germany, founded in 1988, is an institute made up of absolute plastics technology professionals that is dedicated to research regarding techniques and measures in the field of quality assurance for the plastics industry. The institute is certified according to DIN EN ISO 9001 and has been operating a testing laboratory accredited in accordance with DIN EN ISO/IEC 17025 since 2000, as well as an international round robin test series accredited according to DIN EN ISO/IEC 17043:2010 . The continuous improvement of quality and efficiency in the plastics industry is the clear focus of the institute – especially with regard to injection molded parts made of thermoplastics or thermosets.

Implementing a company-wide quality mindset is the key to securing the future of any organization. Establishing said mindset can, however, only be achieved if every individual employee can join in on the challenge and continuously improve himself via ongoing training measures. At KIMW, 102 experts are devoted to research in materials, process, tooling, surface, process, and testing technology. Among other items of equipment, 12 injection molding machines (eight with insertion and removal automation), a compounding system for new material development, and 108 systems for material, component, and surface testing are at their disposal. For the planning, administration, control, and implementation of its internal qualification measures and the management of its vast array of inspection equipment, KIMW relies on CAQ AG’s CAQ.Net software solutions.

Goodbye, Excel: Enter Dedicated Software Solutions

While Excel spreadsheets were initially sufficient for the small number of employees, handful of gauges, and easily manageable number of training courses, this type of solution soon proved to be outdated for the institute, not least due to its consistent growth in personnel. The risk of incorrect entries, data duplications, or version conflicts had become too great, as had the danger that individual gauges, employees, or training courses could simply be overlooked.

At the beginning of 2016, KIMW therefore started searching for a holistic software solution that would manage the institute’s training courses and test equipment in the future. After extensive market research, they found what they were looking for at CAQ AG. The company’s portfolio offers solutions for both areas, which do not function as self-sufficient islands, but are intelligently networked with one another. With the modules for training management and gauge management (Fig. 1), KIMW simultaneously ensures that only persons that have received the latest technical training are allowed to operate the gauges. This is supported by functions such as the qualification matrix. With the matrix, it is possible for all employees at KIMW to find out at any time, down to the individual item of test equipment, who needs which qualifications, who has which qualifications, and for whom which qualifications have expired.

Qualification Matrix and Training Management in One Software

CAQ AG’s Qualify.Net software that is used for this purpose combines qualification matrix, training management, qualification management, and e-learning in one. It offers everything that is needed for planning, managing, controlling, and implementing the various qualification measures at KIMW (Fig. 2). The system thereby supports the fulfillment of documentation requirements according to ISO 9001 and enables the creation of an expressive, easy-to-maintain qualification and training database. Last but not least, thanks to comprehensive e-learning functions, the software also makes it possible to tap the full potential of employees and thus set the course for sustainable corporate success – even in today’s age of working from home and decentralized learning.

While the focus of training management is clearly on people, the topic of gauge management is obviously more technical. Yet the need for software to replace the prevailing Excel spreadsheets arose from a similar motivation. Because, due to the growth at KIMW, in the meantime 774 gauges, 373 test plans, and 49 device managers had to somehow be brought under one hat and all correlations between the entities always kept up to date. Today, KIMW uses CAQ AG’s PMM.Net software solution for its gauge management, gauge monitoring, gauge capability testing, and gauge calibration (Fig. 1).

The Challenge: Managing 774 Gauges and 373 Test Plans

The raison d’être of gauge and test equipment management is actually strikingly obvious: as the only interface between the object that is to be inspected and the resulting measured value, correctly managed test equipment is crucial for monitoring the quality status of products and thus an indispensable precursor for any sustainable quality assurance system.

It is therefore not surprising that, in addition to the seemingly logical necessity of gauge management, numerous standards and legal guidelines also call for a well-functioning and consistently documented gauge management system. Chapter 7.1.5 of ISO 9001, for example, is devoted entirely to the management of an organization’s monitoring and measuring resources. The specifications in this chapter regarding factors such as calibration, adjustment, calibration status, protection, handling, and documentation are intended to ensure the suitability and functionality of test equipment. Furthermore, from a legal point of view, without test equipment management, manufacturers cannot prove in the event of damage that everything possible had actually been done to prevent failure of their product.

Using sophisticated gauge management software means that the requirements of pertinent standards, specifications, and legal frameworks regarding gauge traceability and history can be fulfilled, as all data relevant to a company’s test equipment is stored in the software and is available at any time at the push of a button (Fig. 1). Said data also includes documents such as operating instructions or maintenance certificates. In order to keep an eye on the test equipment itself, functions such as integrated check-out and storage location monitoring via barcodes, QR, and RFID are available. The software can even be connected directly to existing storage systems and conveyor mechanisms on the factory floor via interface technology such as OpenAPI.

The Benefits for KIMW: Efficient Training Management  and Reliable Measurements

With the software solutions for training management and gauge management at its disposal, KIMW now commands state-of-the-art digital tools that significantly assist the institute’s everyday processes. Thanks to their modular design and the easy definition of individual KIMW-specific interfaces, the solutions are both similar in appearance and operation, and – regardless of whether it is training or gauge management – KIMW employees always operate in the same simple and intuitive software environment they are familiar with.

The implementation of CAQ AG’s software solutions led to considerable time savings at KIMW, for example, by eliminating the need to search for specific data. At the same time, potential errors due to redundant data or overlooked inspection equipment, trainings, or employees were eliminated. All of the software-supported measures concerning training and inspection equipment management mean that KIMW can today always blindly trust its measured values (Fig. 3) – for quality, safety, and reliability right from the start.

Fig. 3. View into KIMW’s testing laboratory: The Lüdenscheid-based company devotes itself extensively to research on techniques and measures in the area of quality assurance for the plastics industry   © CAQ/KIMW

Fig. 2. With the matrix in <a href="http://Qualify.Net">Qualify.Net</a>, the qualification status of all employees at KIMW can be precisely determined at any time © CAQ/KIMW

Fig. 1. 774 gauges, 373 test plans, and 49 device managers are managed with the <a href="http://PMM.Net">PMM.Net</a> module © CAQ/KIMW

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Dipl.-Ing. Torsten Urban is the director of training and continuing education at the Kunststoff-Institut Lüdenscheid (KIMW).

CAQ-Systeme in der Rechnergestützten Qualitätssicherung

Software

XPlanar Transports Workpiece during Plasma Surface Treatment Process

The XPlanar motor system combines the advantages of conventional transport systems with magnetic levitation technology. This opens up completely new possibilities for handling and positioning products in the machining process. The floating effect obviates the need for mechanical guiding components and helps Plasmatreat to simplify the system configuration for plasma treatment.

Plasmatreat GmbH, a Steinhagen/Germany based manufacturer of plasma systems for high-efficiency surface treatment and environment-friendly production processes, unveiled a new plasma treatment unit at the K 2019 trade show (Fig. 1). A key innovation is high-precision, flexible floating workpiece transport that replaces prior systems used to move often sensitive workpieces such as PCBs and eliminates the need to install complex 6-axis robots and linear motors. Instead the XPlanar motor system (manufacturer: Beckhoff Automation GmbH & Co. KG) is used.

Like rotary motors, it consists of several stationary energized coils (tiles) and movable permanent magnets (movers). In contrast to rotary motors, both the coils and the permanent magnets are arranged in a plane. The tiles are the electrically active parts of the system: the current control of the coils contained in the system is orchestrated by a Beckhoff Industrial PC and allows the movers to float above the tiles.

The plasma treatment unit can surface-treat a variety of material samples in a two-stage process, as Jochen Stichling, Head of Design at Plasmatreat, explains (Fig. 2): “During the first stage, the substrate is moved under a nozzle for cleaning and activation. During the second, a separate nozzle applies a functional coating.” This, he explains, is where the company focused its innovation efforts: “We wanted a fast, fully programmable, wear-free system to transport the workpieces, and that is what we have achieved with XPlanar.”

Planar Motor System with  Floating Movers

The system consists of tiles that can be arranged in any pattern, combined with contactless movers that float over them and can be positioned exceptionally fast, flexibly and precisely. The movers operate jerk-free and are capable of traveling at speeds of up to 2 m/s; they can also accelerate at 1g, and be positioned with a repeatability of 50 μm – silently, and without wear or abrasion. The system not only supports movement within the x-y space, it provides additional functions to allow movers to be positioned with up to six degrees of freedom when necessary (Fig. 3):

<ul><li>Raising and lowering by up to 4 mm (unloaded),</li><li>tilting by up to 5° when transporting and handling liquids,</li><li> rotation up to 360°.</li></ul>

The XPlanar system in the Plasmatreat machine consists of six 240 x 240 mm planar tiles and a single planar mover.

A major advantage with XPlanar is that the plasma jets used to treat surfaces no longer need to be moved and, as a result, can now be installed in fixed mountings. The jets are complex, both mechanically and electrically, and the ability to move the workpieces rather than the plasma jets themselves reduces wear to the feed lines. For Jochen Stichling, there are additional benefits from the increased flexibility too: “We can attach a variety of material samples to the mover for treatment using just simple adapters. We can easily add processing stations alongside the plasma jets – markers for good parts, for instance, or optical sensing heads to conduct full part inspections – and carry workpieces to them flexibly as needed. And XPlanar’s rapid acceleration also lets us move material samples at high speeds; with thin samples, for instance, this helps minimize treatment time with the fixed jet.”

According to Stichling, the functional benefits of the system are proving valuable in a range of applications: “Conventional setups use a 6-axis robot or linear motors to move a plasma jet around a stationary workpiece. From a cost perspective, XPlanar comes in somewhere between linear-axis and robotic systems. With flat parts that don’t require much vertical travel on the z-axis, where robotic systems are usually ideal, XPlanar offers an excellent alternative to gantry-type systems. XPlanar’s advantages in terms of lack of wear, easy cleaning, and cleanroom compatibility also play out here.”

Integrating Multiple Processing and Inspection Stations

For Stichling, XPlanar has the potential to optimize plasma surface treatment in two key areas, going forward: direct integration of in-line testing for full inspections during the treatment process, and custom-programmable mover travel routes for end customers. Another advantage for Plasmatreat was that it took less than two months to integrate the XPlanar system into its machine – not least because Beckhoff was quick to supply the 3D data and the electrical connection information that enabled the company to rapidly incorporate the XPlanar starter kit into its machine design. XPlanar, according to Jochen Stichling, has proved to be both robust and reliable. And another advantage, he points out, is that the entire plasma treatment cell has now been fully automated using PC-based control, making it a system solution from a single source.

A floating planar mover carries the workpiece exactly into position for surface treatment 

Fig. 1. The new plasma treatment unit is highly compact as it no longer requires a 6-axis robot or additional linear axes

Fig. 2. Jochen Stichling, Head of Design at Plasmatreat, values the custom-programming mover travel routes

Fig. 3. An electromagnetic field causes the movers to float and guides them over the XPlanar system with maximum freedom

Uwe Prüssmeier is Senior Product Manager Drive Technology, XTS, XPlanar at Beckhoff Automation GmbH & Co KG, Verl, Germany.

Automatisierungskomponenten

Förderanlagen und -geräte

Oberflächentechnik

Fördersysteme

Kunststoffverarbeitung: Maschinen und Komponenten

When Traditional Approaches Meet their Micromechanical Counterparts

In the realm of virtual product development, the idea of the cycle has been around for many years. But where does the cycle begin? When we take a closer look at this concept, new aspects rightly start to emerge. For example, whether it is possible or necessary to augment or expand existing, hitherto proven mechanisms so that goals can be achieved, or perhaps even surpassed – and, if so, how this is to be accomplished.

In the realm of virtual product development, the idea of the cycle has been around for many years. But where does the cycle begin? When we take a closer look at this concept, new aspects rightly start to emerge.

Driving forces and the potential for further development have become more and more prominent in lightweight construction in recent years. This is due not least to pressure primarily emerging from within the automotive industry to reduce CO2 emissions. In this connection, injection molding processes and workflows, too, are increasingly becoming the focus of a need for change. On one hand, products are becoming ever more complex and, on the other, questions have been raised about the key elements of the manufacturing process, such as a reduction in the number of production steps, demanding design requirements and high expectations imposed on the product’s mechanical properties.

An important role here is also played by plastics foaming (both chemical and physical). The introduction of this technology necessarily entails investments, not only in machinery, but also in infrastructure, product development, staff training and the ensuing restructuring of workflows. The changeover to this process can therefore be viewed with a certain amount of ambivalence, especially as history shows that the adapting of components to existing molds designed for compact injection molding has met with very mixed success.

But, from the development perspective, what is the current situation with regard to foamed components? Up to now, tasks and steps have usually been considered in isolation. Thus, one development step leads straight from the designer to the tool designer, while another leads to a simulations department that simulates mechanics independently of any analysis of the process. The outcome is that simulation of the manufacturing process is usually performed within product design and/or tool design which is then faced with meeting the production challenges. Part of product design, for example, entails revisiting the wall thickness ratios of base bodies and ribs. And in tool design, too, adjustments have to be made, e. g. with regard to the positioning of the gates.

However, product development is also interested in how foamed products behave under mechanical load and how they should be designed. Traditionally, these approaches have been pursued in separate departments – a consequence of their different specializations.

For some years now, foam injection molding has increasingly been the focus of fiber-reinforced plastics processing. The “usual” separate product development steps are encountered here as well. But is this an efficient use of the technology’s potential? Or is a rethink needed? And if so, what are the technical and, above all, the temporal and financial implications of these adjustments?

The first step towards answering these questions is to assess the current status. This reveals that the tools needed for effecting an improvement often already exist. The question is not about how to use these tools. Rather, it is more about how the results are evaluated, how they are used and how and to whom they are communicated.

A fundamental aspect inheres in the usual approach taken to the mechanical design of products. Such an approach usually is based on the assumption that the mechanical properties are the same throughout the component. But this is precisely the crux of the matter, because to do so is to significantly reduce the possibilities of these technologies.

What potential is hidden in linking together these working steps – in the unification of two previously distinct components, in the examination of the process and analysis of the mechanical properties, in a cycle aimed at greatly enhancing the substance of virtual product development?

It may sound trite today, but consistency of design data is the firm foundation of all steps. Even today, the use of different CAD programs constitutes one source of challenges. Thus, every CAD kernel (not the CAD program) varies in the consistency of its details, and data import and export can lead to unwanted overlapping of surfaces and to other errors. As it is often the case that the CAD programs used for product design and for tool design are different, this is a common occurrence. However, specialized software is available for the necessary reworking or preparation of the data for simulation.

Process simulation serves as the basic tool for addressing challenges arising from the foaming process, product design, tool design and/or material. By performing calculations, analyses and optimizations, it simulates the entire process, from filling and cooling through to deformation. Common simulation results for foaming include two in particular that can exert a considerable influence on downstream development steps: the distribution of cell sizes and the cell density.

These two results not only allow conclusions to be drawn about “cosmetic” issues. More than that, they also reveal how pronounced the local edge layer is, whether more or fewer streaks can be expected on the surface and how much component deformation can be expected. Furthermore, they clarify process-related questions about the advisability of using variothermal temperature control.