Why data matters for hardware engineering companies

As a hardware engineering company, you might be excused for not paying too much attention to the latest trends in data science and artificial intelligence. After all, a lot of your daily work consists of exactly the kind of creative thinking and developing that is making these robots possible. You sell products made from aluminium, silicon and high-grade plastics, not click-per-view advertisements. You might not have a huge interest in the typical artificial intelligence stories about predictive maintenance or managing huge warehouses with robots.

On the other hand, what does matter is how good and how efficient you are at finding out what to create, at designing, manufacturing and selling. And that is where good data management is more and more essential. From evidence-based technology roadmaps to customer relationship management, enterprise resource management and many more. Automating all kinds of manual operations through scripts and macros helps to increase efficiency drastically, and reduces the possibility for errors.

So while as a hardware engineering company, you are maybe not focussed on ‘big data’ and artificial intelligence in itself, still being able to work with sizeable amounts of data and extracting knowledge out of them is quickly becoming an important competitive advantage.

If you went to bed last night as an industrial company, you’re going to wake up today as a software and analytics company.
– Jeffrey Immelt, former CEO of General Electric

Engineering data is typically not of the huge-quantity / low-quality type that gets all the attention. However, there are other reasons why it can still be a big challenge to work with design and manufacturing data. It is spread across many software tools, each performing its calculations, design or analysis with inputs and outputs that are manually copied and pasted. It is contained in documents, spreadsheets, presentations, emails, in different versions and local copies. There is no uniform way of indicating what number represents which parameter, let alone a clear structure in what numbers belong to what actual parts of the design.

At Valispace, we think of data storage, design automation and optimization as parts of a ‘hierarchy of needs’, inspired by ‘The AI Hierarchy of Needs’ as proposed by Monica Rogati from Data Collective. Just as in Maslow’s pyramid of needs, food and shelter are needed before self-actualization, in the engineering data pyramid a basic unified data storage and a certain data structure are needed before it is possible to perform advanced data analytics, apply optimization algorithms and machine learning.

Valispace engineering data pyramid, inspired by ‘The AI Hierarchy of Needs’.

At the bottom of the pyramid is data collection into a single database, available to all engineers collaborating on a certain product or project. Data is exchanged with as many other tools as possible, feeding in and reading it out again. This is the basic set-up to create a single source of ‘truth’ with the latest, up to date values.

Adding structure to that data is one step above in the pyramid. To allow for clear communication and analysis, values are structured using a simple data model. The relations to other values are also stored, for example through formulas. A difference can be made between a physical property (e.g. the mass of an engine blade of 0.3 kg) and other numbers describing such as a design margin or a maximum value. Keeping the data structure simple allows to put in many different kinds of data easily, from back-of-the-envelope spreadsheet calculations to the most elaborate fine-meshed analysis results, from complex state machine logic to large batches of manufacturing, calibration and testing data.

This structure allows for advanced ways of exploring engineering data and its connections, improving the efficiency of daily work and creating new insights. It becomes possible to filter and track data and the sensitivity of one parameter to other values. Interactions with parameters are based on their meaning rather than where they happen to be in a spreadsheet. This opens the door to new user interfaces such as voice, chat or augmented/virtual reality. You could imagine asking Alexa (or J.A.R.V.I.S.) for a breakdown of the latest design changes inside an important subsystem of your product and to detail their impact on the power budget. In fact, we have already set up a prototype Alexa skill for Valispace to do exactly that, get in touch if you would like to be a beta tester.

Building on these layers of data collection, structure and advanced exploration, powerful data analytics can be automated, providing oversight in charts, budgets, timelines or powerful custom scripts.

Finally, having engineering data stored in one place, but also connected, structured and analyzed, many features of a product or project can now be expressed as values inside Valispace. This provides a perfect platform to apply optimization algorithms and automation tools on a real engineering project, the kind of advanced algorithms that have until now been restricted to academic use or small subsets of data. For example, at IAC 2017 in Adelaide, Johannes Norheim from MIT presented a paper on optimization of a satellite design using geometric programming using Valispace and our MATLAB toolbox.

Summarizing:

  • Working with engineering data in an efficient way is a key competitive advantage, breaking up data silos and integrating knowledge across your company.
  • Advanced data analytics is based on structured data storage that forms a single source of truth and has automatic data exchanges with other engineering tools.
  • Exciting new user interfaces and methods of advanced optimization and machine learning become feasible with this data structure in place.

Interested? Take the first step today: centralize engineering data from fragmented spreadsheets and emails into a single place where they create value. To learn more about Valispace, have a look at our website and our documentation page, or try out the tutorials in the online demo.

How engineering students at KTH keep track of their satellite design data

You might have heard that satellites are massive objects that weigh several tons and cost millions of dollars to launch into space. Now, imagine scaling down both size and cost until you could hold a satellite in your own hands – a miniature satellite! The so called cubesats are already revolutionizing the satellite industry by making access to space more affordable. These mini-satellites are made of one or multiple standardized 10x10x10 cm cubic units which only weigh a little more than 1 kg each. They make it affordable for small companies, universities and research centres all over the world to build their own satellites carrying custom-made experiments. In 2017, there will be over 350 cubesats launched to space and the prediction is over 400 for next year.

Artist’s rendering of a cubesat in space.

The idea of MIST is to allow students to play an active part in designing, testing and launching a cubesat satellite.

The Royal Institute of Technology (KTH) in Stockholm is building MIST or MIniature STudent Satellite – a satellite designed only by students. “The idea of MIST is to allow students to play an active part in designing, testing and launching a cubesat satellite”, says Daniel Bogado, student in Aerospace Engineering and responsible for System Budgets in the MIST project. MIST was proposed to the KTH Space Center in 2014 and the work started in early 2015. Currently the 6th team is working on the development of the satellite. Each team stays with the project for half a year.

The experiments on the MIST are from different institutes and companies with many different objectives. Two of the experiments have their focus on radiation analysis, there is one biological experiment, one experiment is testing their self-healing/fault-tolerant computer system in space, another experiment is testing a semiconductor of silicon carbide. Furthermore, there is a piezoelectric motor being tested in space, a new propulsion technology and finally a camera. All these experiments pose different requirements on the satellite making the combination of these an interesting task for the students.

3D-printed model of the MIST satellite.

It’s very important that each new team member quickly gets up to speed in all areas necessary for their work.

Although the satellite is tiny, building a cubesat is a big challenge. The biggest hurdle in the MIST project is the change in personnel involved in the project each semester, many students stay only for half a year. It’s very important that each new team member quickly gets up to speed in all areas necessary for their work. With 8 experiments the MIST satellite has a high number of custom parts for a 10x10x30 cm (3 Unit) cubesat. Keeping a clear overview about all aspects of each experiment is a major challenge, as well as keeping every team member up to date with the most recent values and what the impact is if something changes.

Valispace allows everyone on the team to have the latest results available from everyone else’s work.

The MIST team has found a solution to this challenge by using Valispace to store their engineering data. They use Valispace as the central platform for saving all data regarding power consumption, mass budget and other important characteristics of each experiment and subsystem of the satellite. This allows everyone on the team to have the latest results available from everyone else’s work and therefore make the right assumptions in their calculations and analysis.

With small satellites, mass and power constraints are very strict. You want to maximize the number of experiments and their operation time, but there is only so much power that the tiny solar panels can generate. On top of that, part of the satellite orbit is in eclipse, meaning that the Earth blocks the sunlight, and the satellite must rely on the power stored in batteries. A small change in the power consumption of an experiment can completely change the overall performance of the mission. The MIST team uses Valispace for the creation of a mass and power budget for the satellite. The stored data is used to create a power schedule for the experiments and allow for an easier overview of the system budgets, to ensure that the system is performing at its best capacity.

MIST mass budget created in Valispace.

Right now, the mechanical design of the MIST satellite is being finalized and after that the required thermal analysis of the whole system will be done. Even a small cubesat must be carefully tested before being launched into space to be sure that all experiments work as they should. Once the satellite is in space, no repairs can be done. MIST will be launched sometime after 2019. You can read more about the MIST satellite and find the newest updates on their blog.

The 6th MIST team together with the Swedish astronaut Christer Fuglesang.