In order to increase the utilization and overall system effectiveness of machine tools, the production engineers plan automation solutions for the automatic loading and unloading of workpieces in the CNC machining center or the CNC lathe. Machine tool builders offer appropriate automation equipment as an option when purchasing new machines.
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Increasing complexity of products often means increasing complexity of manufacturing processes, with a simultaneous increase in the degree of automation. This leads to a growing field of application for sensors, which, however, are not always perfectly suited for every application. This often means: customized sensor solutions are needed.
In this article you will learn what customer and application specific sensors are all about, who needs them and how to get them.
Out with the (c)old, in with the new Recently I needed to look into time-to-live (TTL) capabilities within ElasticSearch. It has been several years since I used this feature (version 1.5, to precise – aeons ago!) Unsurprisingly, things have changed significantly since. Instead of setting _ttl in the index mapping
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In lab automation optimized mechatronic solutions play a major role. The requirements to be met are high precision and reliability with very limited installation space.
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Introduction In an earlier article I mentioned briefly some possibilities for bringing python-trained machine learning models into production. Specifically, a java environment. In this article I will take a closer look at how this can done and what pitfalls we should avoid. Keras In the words of the Keras website,
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Introduction When we work with classification or regression models we are seeking to predict either a discrete value, or a value along a continuum. The accuracy of these models is implicit in the metric we use when training: either in the cost function during the actual training, or something like
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Introduction Why Dask? For those of us who regularly work with python machine learning libraries, the Pandas DataFrame library is a firm fixture in our toolkit. Pandas DataFrames allow fast and efficient manipulation of data and a host of data wrangling functions. And the processing is fast because pandas does
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Experts more and more promote “cloud solutions” in industrial production. So it may be a good moment to look into the pros and cons of classical “on premise solutions”. We look into the example of tool identification systems.
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Introduction This is the next article in my collection of blogs on anomaly detection. This time we will be taking a look at unsupervised learning using the Isolation Forest algorithm for outlier detection. I’ve mentioned this before, but this time we will look at some of the details more closely.
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We can all think of examples of successful forecasting, but what should we be aware of and what pointers can we adopt going forward?
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