We can make our 3D-printed parts even more capable when we start mixing them with some essential “mechanical vitamins.” By combining prints with screws, nuts, fasteners, and pins, we get a rich ecosystem for mechanism-making with capabilities beyond what we could simply print alone.
Today I’d like to share some tips on one of my favorite functional 3D-printing techniques: adding heat-set inserts. As someone who’s been installing them into plastic parts for years manually, I think many guides overlook some process details crucial to getting consistent results.
Make no mistake; there are a handful of insert guides already out there [1, 2]. (In fact, I encourage you to look there first for a good jump-start.) Over the years though, I’ve added my own finishing move (nothing exotic or difficult) which I call the Plate-Press Technique that gives me a major boost in consistency.
Join me below as I fill in the knowledge gaps (and some literal ones too) to send you back to the lab equipped with a technique that will give you perfectly-seated inserts every time.
For all the advances in medical diagnostics made over the last two centuries of modern medicine, from the ability to peer deep inside the body with the help of superconducting magnets to harnessing the power of molecular biology, it seems strange that the enduring symbol of the medical profession is something as simple as the stethoscope. Hardly a medical examination goes by without the frigid kiss of a stethoscope against one’s chest, while we search the practitioner’s face for a telltale frown revealing something wrong from deep inside us.
The stethoscope has changed little since its invention and yet remains a valuable if problematic diagnostic tool. Efforts have been made to solve these problems over the years, but only with relatively recent advances in digital signal processing (DSP), microelectromechanical systems (MEMS), and artificial intelligence has any real progress been made. This leaves so-called smart stethoscopes poised to make a real difference in diagnostics, especially in the developing world and under austere or emergency situations.
Representatives from SpaceX, Blue Origin, and United Launch Alliance participated in a forum last week held by NASA to determine the future of humans on the moon. This isn’t just how they will live, how long they will stay, or what they will do; no, this is far more interesting: this was how humans will travel from lunar orbit from the surface of the moon. The future of the next generation of lunar lander is being determined right now.
The plan right now is entirely unlike Apollo, which sent a pair of spaceships in orbit around the moon, sent one to the surface, then returned to the mother ship for the trip back to Earth. Instead of something somewhat simple, the next era of lunar exploration will happen from a gateway orbiting in cis-lunar space. What makes this so amazing is how weird the orbit is, and the reasons behind it.
Our digital world is so much more interactive than the paper one it has been replacing. That becomes very obvious in the features of Jupyter Notebooks. The point is to make your data beautiful, organized, interactive, and shareable. And you can do all of this with just a bit of simple coding.
We already leveraged computer power by moving from paper spreadsheets to digital spreadsheets, but they are limited. One thing I’ve seen over and over again — and occasionally been guilty of myself — is spreadsheet abuse. That is, using a spreadsheet program to do something I probably ought to write a program to do. For those times that you want something quick but want something more than a spreadsheet, you should check out Jupyter Notebooks. The system is most commonly associated with Python, but it isn’t Python-specific. There are over 100 languages supported — many community-developed. You can even install a C++ interpreter backend for it. Because of the client/server architecture, it is very simple to share notebooks with other users.
You can — in theory — use Jupyter for anything you could use Python for. In practice, it seems to get a lot of workout with people analyzing large data sets, doing machine learning, and similar tasks.
The Good: Simple, Powerful, Extensible
The idea is simple. Think of a Markdown-enabled web page that can connect to a backend (a kernel, in Jupyter-speak). The backend can run on your machine or remotely and will support some kind of language — often Python. The document has cells that line up vertically (like a single wide spreadsheet column). For example, here’s a simple notebook I created to explain how a bunch of sine waves add up to a square wave:
It’s no secret that I rather enjoy connecting things to the Internet for fun and profit. One of the tricks I’ve learned along the way is to spin up simple APIs that can be used when prototyping a project. It’s easy to do, and simple to understand so I’m happy to share what has worked for me, using Web2Py as the example (with guest appearances from ESP8266 and NodeMCU).
Barring the times I’m just being silly, there are two reasons I might do this. Most commonly I’ll need to collect data from a device, typically to be stored for later analysis but occasionally to trigger some action on a server in the cloud. Less commonly, I’ll need a device to change its behavior based on instructions received via the Internet.
Etherscan is an example of an API that saves me a lot of work, letting me pull data from Ethereum using a variety of devices.
In the former case, my first option has always been to use IoT frameworks like Thingsboard or Ubidots to receive and display data. They have the advantage of being easy to use, and have rich features. They can even react to data and send instruction back to devices. In the latter case, I usually find myself using an application programming interface (API) – some service open on the Internet that my device can easily request data from, for example the weather, blockchain transactions, or new email notifications.
Occasionally, I end up with a type of data that requires processing or is not well structured for storage on these services, or else I need a device to request data that is private or that no one is presently offering. Most commonly, I need to change some parameter in a few connected devices without the trouble of finding them, opening all the cases, and reprogramming them all.
At these times it’s useful to be able to build simple, short-lived services that fill in these gaps during prototyping. Far from being a secure or consumer-ready product, we just need something we can try out to see if an idea is worth developing further. There are many valid ways to do this, but my first choice is Web2Py, a relatively easy to use open-source framework for developing web applications in Python. It supports both Python 2.7 and 3.0, although we’ll be using Python 3 today.
How large is the cache of discarded electronics in your home? They were once expensive and cherished items, but now they’re a question-mark for responsible disposal. I’m going to dig into this problem — which goes far beyond your collection of dead smartphones — as well as the issues of where this stuff ends up versus where it should end up. I’m even going to demystify the WEEE mark (that crossed out trashcan icon you’ve been noticing on your gadgets), talk about how much jumbo jets weigh, and touch on circular economies, in the pursuit of better understanding of the waste streams modern gadgets generate.
Our lives are encountering an increasing number of “how do I dispose of this [X]” moments, where X is piles of old batteries, LCDs, desktop towers, etc. This leads to relationship-testing piles of garbage potential in a garage or the bottom of a closet. Sometimes that old gear gets sold or donated. Sometimes there’s a handy e-waste campaign that swings through the neighborhood to scoop that pile up, and sometimes it eventually ends up in the trash wrapped in that dirty feeling that we did something wrong. We’ve all been there; it’s easy to discover that responsible disposal of our old electronics can be hard.
Fun fact: the average person who lives in the US generates 20 kg of e-waste annually (or about 44 freedom pounds). That’s not unique, in the UK it’s about 23 kg (that’s 23 in common kilograms), 24 kg for Denmark, and on and on. That’s quite a lot for an individual human, right? What makes up that much waste for one person? For that matter, what sorts of waste is tracked in the bogus sounding e-waste statistics you see bleated out in pleading Facebook posts? Unsurprisingly there are some common definitions. And the Very Serious People people at the World Economic Forum who bring you the definitions have some solutions to consider too.
We spend a lot of time figuring out how to build this stuff. Are we spending enough time planning for what to do with the gear once it falls out of favor? Let’s get to the bottom of this rubbish. Continue reading “The Woeful World Of Worldwide E-Waste”→
I’ve noticed that we hear a lot less from corporate research labs than we used to. They still exist, though. Sure, Bell Labs is owned by Nokia and there is still some hot research at IBM even though they quit publication of the fabled IBM Technical Disclosure Bulletin in 1998. But today innovation is more likely to come from a small company attracting venture capital than from an established company investing in research. Why is that? And should it be that way?
The Way We Were
There was a time when every big company had a significant research and development arm. Perhaps the most famous of these was Bell Labs. Although some inventions are inevitably disputed, Bell Labs can claim radio astronomy, the transistor, the laser, Unix, C, and C++ among other innovations. They also scored a total of nine Nobel prizes.
Bell Labs had one big advantage: for many years it was part of a highly profitable monopoly, so perhaps the drive to make money right away was less than at other labs. Also, I think, times were different and businesses often had the ability to look past the next quarter.
