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Shims, shims they’re good for your bike, the more you change them, the more the handling goes to…..

Introducing shims to dampen oil flow was an absolute triumph in suspension technology. There’s no doubt that the switch from squishy and harsh orifice damping to using these round little slices of glory improved performance massively, and brought in a whole new world of tuning options. Sounds like great news right?

Of course it is, but the new challenge of figuring out how to adjust the shim diameters, thickness’s and arrangements created the dark art of suspension tuning that we know of today.

As a quick recap, shims will bend out of the way of the oil flow, making a more linear or digressive damping curve. This gives the suspension good hold up over low speed impacts, but allows the suspension to move on high speed impacts. Compared with orifice damping which was just a hole drilled in a tube, so it offered little to no resistance under low speed, and increased damping exponentially at high speed, causing it to feel harsh.

The graph below is from the Racetech Suspension Bible  and shows how increasing the orifice diameter affects the damping curve on the left, and how using a shimstack compares with an orifice on the right.

I have to preface this article with the fact that I have never worked as a suspension technician. However, I do have a masters in engineering, but my time working as a car mechanic with my older brother taught me that the piece of paper they give you at the end of college isn’t much use when you’re wrestling a diesel four wheel drive gearbox onto a spigot shaft.

What I will try to do here then is marry together the literary research I have carried out over the years, with the practical knowledge I have garnered from hanging around suspension technicians.

The question we will try to answer today is, which part of the shim stack should I target to make a rider feel more comfortable?

There are probably technicians who could answer this question just from raw experience, and trial and error. By using an analytical approach though, we can reduce testing time, and document a lot of that information gathered through experience. Every technician has to retire some time, and it’s an awful pity to lose all that knowledge.

Let’s start with what we know already.

From our previous articles, we know the average speed of the suspension movement on different sections of the track, and that we can use dyno’s to measure the suspension damping force effectively for the speed’s we see on the circuit.

What we don’t know, is which part of the shim stack is affected by how much force. If we know the answer here, it becomes straightforward to know which part of the stack to adjust. So how can we calculate the stiffness of each part of a shim stack?

Fortunately, there are lots of very clever people in the world, and one website I came across www.shimrestackor.com, has developed a piece of software that you can input all the shim dimensions from your stack, and it will print out a stiffness graph. You do have to pay a license fee for the software, and for the purpose of this article, I cam across a formula on the site that I could use as a rough substitute to demonstrate my point. The following formula basically calculates the stiffness of each shim, and by adding all the stiffnesses together, you can see how stiff the stack is at each point.

Woah!! What the heck is that?

Don’t get too put off now, let’s just look at this in plain English.

There are a few different symbols in there that mean the following:

K Stack: How stiff the whole shim stack is.

n: The number of shims in the shim stack.

Ki: The stiffness of the i-th shim.

Ei: How stiff a material is.

ti: The thickness of the i-th shim.

vi: How much the shim will squish out when it’s squeezed together.

Ri: What the radius is for the curve of the shim when it’s being pushed out of the way by the oil.

So what we do is calculate out K for each shim, and then add each K value together.

There are many assumptions built in here, and this equation also doesn’t account for crossover shims, or other variations, but as I said, this is a rough estimate.

The shim values I am using are the standard settings from a WP XACT fork for the base valve and the mid valve. I just plug in the values from this sheet into the formula above, and it results in the following two graphs:

These graphs show the force created by each shim in the base valve and the mid valve using the formula. There were 27 compression shims in each stack, excluding the clamp shim. What’s strange to me is how the mid-valve maxes out at 600N, while the base valve goes up to 1200N.

And if you remember from our last article on dyno’s the above graph is from Kreft suspension, and shows the damping force in Newton’s created at speeds from 0m/s up to 4m/s. Our last piece of the puzzle is to know how fast the suspension is moving on the part of the track the rider doesn’t feel comfortable on. There are two ways we can do this, one is to take the average speed of the suspension in compression on that part of the track, or two is to view the actual speed in the Motoklik app.

As an example, if the rider is having an issue on corner entry, the data from the speed of motocross suspension article showed that the average speed on corner entry was 0.506m/s. Working this back through the Kreft graph shows that the average force is around 120N, and working this back again into the shim stack graphs shows that this is around shim 6 on the base valve, and shim 8 on the mid valve.

Looking at example two, using the Motoklik app, I have picked a corner entry on the track. If the rider was complaining saying that they feel a harsh hit, we can easily see this in the speed the suspension moves on the graph. Taking this a step further, and assume we don’t want to affect the other parts of the turn, we can isolate the hard hits to between 2m/s and 3.6m/s. Converting this into a force shows that we are between shims 13 and 17 on the base valve, and 22 to 27 on the mid valve.

This whole article is just looking at one particular circumstance, and all of the values and forces can change depending on the type of rider, type of track, and configuration of the shim stack.

I do think it’s clear to see that by taking an analytical approach, we can be much more targeted in the changes we make to shims, and by gathering data continuously, we can build a detailed picture of suspension performance.

If you want to work with the Motoklik system, you can email [email protected] with any questions you have.

Kind Regards,

Jens

P.S.

Thankfully Craig Dixon was on hand to point something out which I missed in the article, please read below to see what else to be aware of:

“This is both correct and incorrect, identifying the shim area is fine but shims do not act as individuals, nor is there an identifiable high speed or low speed area of a shim stack. changing any shim changes the forces across the entire velocity / damping range. For example if we are to make a change to a high velocity shim (#13 to pick a number) and make this part of the damping stiffer, the low speed area will also increase in damping in proportion to the change made, if only a high speed adjustment is needed then we must also change the low speed shim stack (softer) to keep this section at the original damping force.”

In our last newsletter, I wrote about the average speeds of motocross suspension on a hardpack circuit. There were some surprising values in there, including max speeds of up to 7.1m/s on the forks, and 2.3m/s on the shock. This brings up the question then, is it worth it to use a shock dyno in motocross?

The reason being that most shock dynos on the market have a maximum speed of 2.5m/s to 3m/s, and many times suspension technicians will run their tests at 1m/s. That’s surely a problem, isn’t it? How can a technician be sure that what they change on the shim stack is having the desired effect on the track?

There are two things we need to find out:

Can a dyno be run at their maximum speed of 3m/s with motocross suspension?

And, how much time does the suspension spend above 3m/s on the track?

In the image above on the left are the specifications of the Andreani DB4 and DB4-Plus dynamometer’s, and on the right is a force vs speed graph for standard WP XACT fork suspension from the Kreft suspension website.

For the most part, the type of dynos you will find with a motocross suspension technician are Scotch-Yoke or Crank dyno’s, and you can read about the Pro’s and Con’s of each on Laba 7’s website.

What’s being shown in the Andreani graph is that with the DB4 dyno, the suspension can only produce a force of 2,000 Newtons at 2m/s.

On the Kreft moto graph, it shows the measured force of the fork vs the speed. The speed goes up to a max of 4m/s, and the force at that point is slightly less than 700N.

That all seems fine then, the fork can be ran at maximum speed on this dyno.

In the image above on the left are the specifications again of the Andreani DB4 and DB4-Plus dynamometer’s, and on the right is a force vs speed graph for a Showa shock on the TCD Racing website.

On the TCD Racing graph, it shows the measured force of the shock vs the speed for both compression and rebound. The upward trending lines are the compression values which are the focal point of this article. The max force measured is 7,500N with the standard shim settings, and only at a speed of 0.52m/s. With the shim stack adjustments made by TCD, this value is dropped to 4,500N

Even with the softer TCD settings, this means that by the time the DB4-Plus dyno gets to 0.7m/s or 0.8m/s, it could already be at its limit. Now, I’m not pointing a finger at the DB4-Plus, it could very well be the same issue with most dynos that are used, it is just that Andreani were open enough to provide this information on their website, and it’s all I have to work off.

What about the answer to our first questions, can dyno’s be ran at their maximum speed with motocross suspension?

For the forks, we can likely say yes. For the shock, it’s a maybe leaning towards a probably not. It really depends on the stiffness of the shim stack and valving setup in the shock.

But does that really matter if we compare it to real world data? Let’s have a go at answering our second question.

Throughout these articles, I have been using the same data set for each example. The data comes from a German hard pack track, with a young rider on a 2023 Husqvarna TC 250.

You will have seen the graph above before in our previous article on improving the accuracy of our predictions for suspension setup. It shows the speed of the forks throughout the entire session, which was around 3 laps long. We found out in our last article on average suspension speed that the suspension moves fastest on jump take off’s and particularly landings.

We can see a number of times where the suspension reaches speeds far higher than the max speed for most dyno’s of 3m/s, but how often does it move that fast really?

I have broken the data out for the front and rear and calculated what percent of time is spent above 3m/s for the forks and shock.

The total time the forks spend above 3m/s is 0.91%.

The total time the shock spends above 3m/s is 0.00%.

Yippee! This means that measuring the suspension at the max speed on the shock dyno doesn’t bring us an awful lot of value, and probably isn’t worth worrying about. Remember, on the rear shock, we extrapolated that the max speed would probably be around 0.8m/s.

However, from speaking with suspension technicians, I know that many of them run their dyno’s at 1m/s. So let’s carry out the same analysis again, and find out how much percent time the suspension spends above 1m/s.

The total time the forks spend above 1m/s is 12.76%.

The total time the shock spends above 1m/s is 1.45%.

13% is a pretty significant amount of time to ignore, but we also know now that the dyno should be able to run forks at max speed because the damping force is low enough.

What does all this mean then.

The bottom line is, the higher end dyno’s that are available on the market should be satisfactory to cover 99% of the time the suspension spends moving in the real world, but you have to be careful about the maximum force for the rear shock. If you’re a technician that is working with supercross suspension, there could be some real challenges if the dyno isn’t up to the specification you need.

Something else to watch out for here, is that this is only one example with some pretty big assumptions about the damping forces created by the suspension, and data from just one session on one track. There can be a lot of variables that skew the outcome of this study one way or the other.

There is no doubt that dyno’s are an excellent tool for performing quality assurance checks, as well as optimising the damping characteristic for different type’s of riders. Even if you want to see what the force curve is at the max fork speeds of 7m/s that we saw in the first graph, by running the dyno at max speed of 3m/s, you could probably have a good guesstimate at what the damping curve looks like.

If you want to work with the Motoklik system, you can email [email protected] with any questions you have.

Kind Regards,

Jens

In our last article, I wrote about how to improve accuracy when analysing suspension data.  When making changes to suspension shim stacks however, it can be extremely helpful to know how fast the suspension is actually moving, to know which part of the damping system to target i.e. the mid-valve or base valve stack on forks for example. In this article, we will look at sample data from one track. So let’s jump straight into it.

All speeds shown in each graph are metres per second, and the graphs show the lap overall average, as well averages for different sections of track.

The graph above shows the average compression speed of the front forks for the overall lap, as well as different sections of the track.

The graph above shows the average rebound speed of the front forks for the overall lap, as well as different sections of the track.

The graph above shows the average compression speed of the rear shock for the overall lap, as well as different sections of the track.

The graph above shows the average rebound speed of the rear shock for the overall lap, as well as different sections of the track.

One thing that might be jumping out at you is that the fork speeds are much higher than the shock speeds. This is because the front axle is connected directly to the fork, whereas the rear axle is connected to the swingarm, and then through the linkage. This connection reduces the speed from the rear axle to the shock by around 3:1.

There are also some observations which we knew already from the last article, like how the fastest speeds are measured at the jump take offs, and how the compression speeds are slightly higher when braking into a turn, than accelerating.

From my experience, it has to be said that the speed of the rider, the speed of the track and the roughness of the track can all have a significant impact on the average speed values measured, and just because an overall fork compression value of 0.476m/s works well on one track, it absolutely doesn’t mean that this is a target value to aim for at every other track.

When you match this data up with a force / speed graph from a suspension dyno, and with experience know which areas of the curve relate to which part of the shim stack, this data becomes incredibly useful in finding answers fast to suspension setup problems.

If you would like to work with the Motoklik system, or have any questions, e-mail [email protected]

Kind Regards,

Jens

In our last article, I wrote about the challenges of suspension data acquisition in motocross, and why we believe it hasn’t been widely used to date. As part of that discussion, I brought in some fairly abstract ideas to do with height of children versus adults, and how to accurately predict the temperature on a given day in Ireland. If you were brave enough to stick with that to the end, thank you, because today you will see where I was going with it!

Where we ended up was that by using a larger historic dataset based on averages, we can improve the standard deviation (σ; pronounced sigma) of our data, the ± bit. When σ is improved, we can make more accurate predictions about the value we should expect for height, temperature, or what we’re interested in, suspension speed. Let’s finally get to the good bit, looking at some actual suspension data measured by Motoklik.

The image above shows the speed of the front forks over the course of one lap on a motocross track in Germany. Anything greater than 0m/s is compression, and less is rebound. Looking at the data like this, it all looks like a bit of a mess. If we are to take the average σ for the full lap, we get:

Front Compression = ±0.611 m/s

Front Rebound = ±0.382 m/s

Rear Compression = ±0.526 m/s

Rear Rebound =   ±0.239 m/s

However, when we take a closer look, we can start to see some patterns…

Any guess for what the data above is?

Yes, you’re right it’s a jump! (Well maybe you read it from the graph title, but I’ll give you the benefit of the doubt 😉). It’s pretty easy to recognise how the forks are compressed on the upramp, how they stay fully extended in the air, and how they compress again on the landing.

Let’s look at another example.

No guessing this time, this is data from a turn on the track. It shows the speed of the front suspension. You can see on corner entry how the forks are compressing and rebounding at much higher speeds than they are exiting the corner. The apex of this turn is around sample number 151.

The point I am making is that as we start to look at the data more closely, we can see how some data is more related than others, so if we are to group all of the data from turns together, and all of the data from jumps together, and from braking and accelerating and so on, what do you think might happen to our σ?

The image above shows the front fork σ for the overall lap, as well as for the different sections of track we have grouped together. The blue line represents compression, and the yellow rebound. The green space between the flat line, and the data line shows how much we have improved the σ by grouping the data together.  We see a really good improvement for most things, except for maybe braking, and jump landings. Remember, a smaller σ allows us to make more accurate predictions!

The image above shows the rear axle σ for the overall lap, as well as for the different sections of track we have grouped together. The blue line represents compression, and the red rebound.

So overall we are showing a massive improvement in the accuracy of compression data, and some good improvements for rebound, but the rebound data was already more accurate than compression from the get go.

There is one Achilles heal though, and that’s the jump landing data. On both front forks and the rear, we got a significantly worse σ value 🥹 Does this mean all our hard work was for nothing? Surely we can find an answer that makes this all worth while.

Stop and have a think about what we could do next…

Well what do we know about jumps? From the “Front Suspension Position for 1 Jump” graph closer to the start of this article, we can see that when we are taking off and landing from a jump, that the suspension moves a big distance in a short amount of time. This must mean that the suspension on average is moving a lot faster on a jump than elsewhere on the track. Is this true?

We can check this easily by calculating the average speed of the suspension movement for each of our grouped sections. It gives the following results:

Front Compression Lap Average: 0.476 m/s

Front Compression Jump Landing Average: 0.907 m/s

Rear Compression Lap Average: 0.463 m/s

Rear Compression Jump Landing Average: 1.108 m/s

Hey! 😲That’s a pretty big difference in average speed!

Now we know that adjustments to higher speed movements of the suspension will have the biggest effect on jumps. Let’s try adding a group, within a group, and see what it does to our σ value for jumps.

When we filter out the low speed movement of the compression from the data, we dramatically improve the accuracy of predictions for jump take off’s and landings. We have to be a little bit careful here though for two reasons:

1: When we look at the jump take off speeds, there aren’t  that many that reach up into the higher threshold, which in most cases is going to give us a better σ value by default.

2: The jump landing σ still isn’t quite as good as the overall σ, but it is a whole lot better than where it was.

Right, let’s try and wrap this thing up.

It can be easy to dive into all these numbers and get very excited, but it’s more important to not lose sight of what we are actually trying to achieve.

We are trying to give riders the best suspension setup possible for the current track conditions and their riding style.

On most suspension we can control the low speed compression and rebound on the front and rear, and the high speed compression on the rear via the clickers. We know now that jump data is mainly comprised of high speed movement of the suspension.

If we redesign our graphs to those shown above, we can look at how much we have improved the accuracy of our predictions for the clicker adjustments by grouping the data together in related track sections. We can even take this a few steps further, and add in more sub-groups to group the data together even more.

As you can imagine, to break out the track like this manually for every lap and every section can be a terribly time consuming task, but the results are totally worth it for the improvement we get in suspension setup prediction accuracy.

I think that’s enough for today’s discussion, and if you’ve made it all the way to the end, thank you for your time.

If you would like to work with us, or use the Motoklik system yourself, and you have a few questions, feel free to send an email to [email protected] 🙂

Kind Regards,

Jens

On Track Off Roads Adam Wheeler released an article this week on the OTOR website with the focus on data and how it is used in MXGP.

In the article, Adam writes about his interaction with the topic over the last 10 years, and what the current view in the paddock is on working with sensor technology on dirt bikes. Around the mid 2000’s to early 2010’s, we saw the move to data acquisition in motocross, most notably with Chad Reed’s TwoTwo Motorsports Honda, but little has changed in terms of the actual hardware or software since that time. There are a myriad of things that can be measured in motorsports all based around temperature, pressure, flow rate, strain and position, but of course our focus here at Motoklik is on suspension.

So what are the current views on suspension data acquisition in motcross? Thanks to Adam, we have some answers on hand:

Roger Shenton, Team Co-Ordinator, Team HRC: We’ve used sensors on suspension for a few years but we never race with it. It’s delicate. We have to use a harness and the bike carries more weight, so we put it on only for pre-season and mid-season testing to know what the suspension is doing and not just for rider feedback but to see what our technology is doing. We don’t incorporate it with racing at all.

Wim Van Hoof, Technical Manager, Standing Construct Honda MXGP: I know they use sensors on the suspension in the U.S. and in supercross I can understand it as the track changes little compared to motocross, and how many times a season do they have a mud race?! They don’t have braking bumps like we do or changing lines. If we put sensors on our suspension then after two-three laps the data will have changed, and from where can you make the comparison?

Harry Norton, Red Bull KTM Factory Racing Team Technical Co-Ordinator: Being able to understand the exact forces on the suspension is useful. If you could put suspension data over engine data on the track then you’ve got a really clear picture of what is happening. For sure there are already people looking into this and I think it will be a priority for teams in the future.

As per Roger, Wim and Harry, suspension data is used at MXGP and Supercross level, but there are two main challenges, reliability and changing track conditions.

In the picture above, you can see the types of sensors most commonly used today. This is a carry over technology from tarmac based motorsports and not built particularly with motocross in mind. While that sensor technology works brilliantly on-road, some of the issues faced by these sensors in off-road are:

• Contact parts and electronics get exposed to sand, water and grit causing them to fail prematurely.
• Shafts bend during high speed movements.
• Return springs aren’t fast enough to measure high speed compression movements accurately.
• Accuracy can be reduced from changes in pressure due to temperature and oil volume fluctuations.

What has Motoklik done to overcome these problems?

We knew from the outset, that a reliable system is key to making suspension data work in motocross. This is the reason why we developed our own brand new type of sensor (shown in pictures below) that solves all of the issues outlined above. How did we do that? Magnetism! Magnetic field strength is an amazing feat of physics. It’s ability to pass through materials eliminates the need for contact parts, however, until now the distance that could be measured was limited by how sensitive the sensor was. The new Motoklik sensors work over longer distances and adapt to changes in the position of the magnet in the x, y and z axis’s making them incredibly versatile, and who knows maybe we will see them used in other professional motorsport in the near future 😉

Our first problem of reliability is now solved, but what about the ever changing conditions? Surely a track that’s lines and roughness change from lap to lap can’t be interpreted through data…

If we put on our data scientist hat here for a minute, what’s really being said is that motocross suspension data has a really high standard deviation value. Standard deviation is a measure of how spread out the data is that has been recorded.

An example would be the heights of people. If we had a classroom full of 5 year old’s, and we measured their height, we would get an average of 109cm ± 4cm. If we measured a room full of adult men, it’s 1,778cm ± 8cm. The standard deviation is the ± bit, and just because adult men have a higher standard deviation, it doesn’t mean we can’t make relatively accurate predictions about their height, in the same way that just because motocross suspension data has a higher standard deviation than road race data, it doesn’t mean we can’t apply data analysis techniques to it.

The trap that you can easily fall into is focusing on one specific issue at one specific part of the track. Suspension setup in motorsport is a compromise. We need to have a wide view of all opinions (or in this case data) to make a fair decision. This gives a clue as to what one part of the solution is when analysing suspension data in motocross.

Let’s work through an example again, and this one is close to my heart, the temperature on a given day in Ireland. The graph above is the maximum temperature from everyday throughout 2022. We can see a bit of a trend there as we go from Winter into Summer, and back to Winter again, but a prediction as to what the temperature is going to be on a certain day is very difficult to make with any level of accuracy. This is what happens when we look at suspension data for one specific section of track, or one specific lap. We’re trying to make a prediction with a low amount of historical data.

When we add in data from the last 10 years, things become a lot more clear. We can see how most of the data lies inside the two curved red lines, so on any given day we can make a prediction within around ± 2.5°C. The accuracy of our prediction can be further improved if we add in more data such as humidity, atmospheric pressure and cloud cover. This is what’s possible when we have enough suspension data to work with also.

So why hasn’t that been done before?

I believe the answer lies in the reliability of the current sensors used, and the individuality of the data recording. With race teams and manufacturers having to work on their own, it’s very difficult to record enough data with such a small group of riders. There is a lot of time already spent on trying to test cams, gearing, ecu maps, triple clamps, linkages, tyres, handlebars and so on, it leaves very little time to dedicate to suspension data recording especially in Europe with bad weather and the sensors may not last more than a couple of laps.

This is where Motoklik has such an advantage. We can reliably and continuously monitor and record the data from 100’s and 1000’s of riders from all over the world in every type of condition to build a vast data base of suspension data to make predictions on setup, in the same way that Google uses internet browser data to predict what items you might be interested in buying, or where you’d like to go on holidays.

There is a lot more that can be done to improve the accuracy of our predictions for suspension setup, but as you might think, it starts getting tricky to do it on your own if you are separating out the data into multiple features and combining it with a huge historical data base. This is where Artificial Intelligence comes in, but I think that is a topic for another day 

Excellent suspension is one of the most vital components of speed around a motocross circuit whatever the level. A fast bike with bad suspension won’t help as much as a slower bike with great suspension.

Suspension is crucial for trust, speed and safety but setting it up is complicated for most riders. For many, it’s a bit of a guessing game but that guesswork is now redundant thanks to Motoklik, a revolutionary company making great suspension set-up available everyone.

Motoklik is an easy to use, easy to fit, robust suspension setup and lap-timing system for use in motocross.

In short, once the sensors are on, everything can be done on your smartphone and gives you, to the very last click, how your suspension should be set up from hard pack to sand, the sensors on the bike are tracking everything from lap times, and gives information as detailed as relative speed based on track roughness to earlier in the day as well as when and where the bike bottoms out. It’s impressive and could revolutionise how the weekend warrior is able to set up their bike.

So, does it really work? We tested with club rider Richard Greer to get feel of how the product really can benefit the average rider, and spoke to Stuart Edmonds , who currently uses the technology at the Pro level, we also caught up with MotoKlick CEO Jens Koepke to learn more about the product that’s available here.

From club rider to pro, the reflections were positive.

Stuart Edmonds: “I don’t think anyone who gets it shouldn’t get a benefit out of it. If they don’t they are doing something wrong. Straight away you stick ot on your bike and you get your laptimes and suspension combined. It gives you a massive input on increasing speed, increasing stability on the bike and being able to ride faster for longer and being comfortable.

“Sometimes in myself you get a bit caught up with is it too hard or soft. To be able to go and look at the date and be able to tell straight away, ‘yeah, it’s actually too hard.’ It’s an asset for me to have.”

Richard Greer: “It is going to help. It gives you the ideal set-up for you and that track. You would become a better rider and better test rider just by using the system and making the changes it recommends and go from there.”

“I could feel the improvement, I could feel the suspension moving more. We took five clicks off the front and a few clicks off the back. When I made the adjustments I could go through the bumps, there wasn’t as much fighting with the suspension.”

Get the full in-depth verdict below:

Read the full press release on the new AiSetup to learn more about their latest technology:

Motoklik, the industry leader in motocross AI technology, is proud to unveil AiSetup, an innovative artificial intelligence software that revolutionises suspension set-up and lap-timing. With AiSetup, riders can optimise their suspension settings without the tedious trial-and-error process, thanks to precise recommendations generated by advanced AI algorithms.

Motoklik Key Features:

• Streamlined Suspension Tuning: Say goodbye to time-consuming trial and error. AiSetup provides clear, easy-to-understand recommendations for clicker adjustments, saving valuable track time and frustration.
• Lap-Timing: Motoklik’s lap-timing technology enables riders to track and analyse their performance while correlating it with suspension settings for better insights.
• Compatibility: AiSetup works with the main motocross models, ranging from 85cc up to 450cc, providing optimised suspension settings for a wide range of bikes.

Jens Köpke, CEO of Motoklik, shared his excitement about the launch of AiSetup, saying, “Motoklik has always been committed to pushing the boundaries of innovation in motocross technology. With AiSetup, we are redefining suspension tuning by leveraging the power of artificial intelligence. We believe this software will revolutionize the way riders approach suspension set-up and help them unleash their full potential on the track.”

Motoklik’s AiSetup is set to disrupt the motocross industry, offering riders of all levels an unprecedented level of precision and efficiency in suspension tuning. The system is available on Motoklik’s website, and is compatible with a wide range of motocross bikes.

For more information about Motoklik and AiSetup, please visit www.motoklik.com or contact [email protected]

Motoklik: The Ultimate Suspension Setup and Lap-Timing System for 85cc Dirtbikes

If you are looking for a way to improve your suspension setup and lap times on your 85cc dirtbike, you need to check out the Motoklik system. Motoklik is an electronic device with unique suspension sensors, a mobile app, and a cloud database that allows you to measure and analyze your suspension performance and lap times on any track.

Motoklik is designed to work with motorcycles that have off-road capability, and it is now available for 85cc dirtbikes from GasGas, Husqvarna, and KTM. Whether you are racing motocross, supercross or enduro, Motoklik can help you achieve the best suspension setup for your riding style and track conditions.

Motoklik is easy to use, easy to fit, and robust. It comes with a central control unit (CCU) that is located between the triple clamps and behind the front number plate, a satellite position antenna that is located on the handlebar with a start/stop recording session button and LED indicator, a front suspension measurement wand that is located on the left fork leg, and a rear suspension measurement pack that is located between the frame and brake master cylinder. It self-charges from the bike’s power supply, so you don’t have to worry about replacing batteries or recharging.

Once you install Motoklik on your 85cc dirtbike, you can start recording your suspension data and lap times with a simple press of a button. After riding your first session, you can use the Motoklik app to see your data and get recommendations from AiSetup. AiSetup is a smart feature that analyzes your data and tells you how much to adjust each of the clickers for high and low speed compression and rebound, so you can ride with maximum confidence. AiSetup also notifies you if your valving or springs are too soft or stiff for your weight and ability, so you know if you need to have your suspension serviced by a technician.

With Motoklik, you can also see live sag readout, bottoming analysis, suspension position and speed analysis, section analysis, average speed, lap variation, speed vs. track roughness score, and more. You can compare different sessions and tracks, see which line is faster, and see if your suspension setup changes are making you faster. You can also upload your data to the Motoklik online dashboard and share it with your technician.

Motoklik is not just a data logger or data acquisition system. It is a suspension setup and lap-timing system that can help you improve your riding skills and performance on your 85cc dirtbike. It is a must-have tool for every dirtbike rider who wants to get the most out of their suspension.

Don’t miss this opportunity to get Motoklik for your 85cc dirtbike today. Click on Buy Now to order yours, and get ready to experience the difference that Motoklik can make.