Mineral glass is difficult to work, especially the rock crystal beryl initially used to make spectacle lenses, because of its hardness. So anyone who wanted to turn a piece of glass into a visually effective lens needed a lot of patience and perseverance. Whether a lens could ultimately deliver a distortion-free image depended entirely on the precision with which it was worked. Thus it is not surprising that, in the late Middle Ages, methods were sought based on wind and water power to produce spectacle lenses with the required finish, if possible without the need for human labor. The advent of the industrial revolution two centuries ago with its motor-driven machines finally made it possible to manufacture spectacle lenses on a routine basis.
By Dr. Hans-Walter Roth
In the beginning grinding of spectacle lenses was done entirely by hand. Thus chroniclers report dozens of unqualified workers spending days giving the blanks supplied by the glassworks their desired shape. On grinding machines, whose antecedent was the potter’s wheel, one side of the lens was ground to a shape corresponding to a spherical surface. The other side was initially flat, as with lenses for reading placed on the page. Only later did lenses become biconvex. Provided the diameter and refractive index of the material were known, the strength of the spectacle lens could be calculated from the difference between the central thickness and the edge thickness. Prior to the introduction of the metric system two centuries ago when the diopter was introduced as a measure of spectacle lens strength, previously only the focal length was specified. It was easy to determine: you just had to hold the lens up to the sun and, by focusing the rays of light for example on a piece of paper, you could find the focal point. The oldest surviving pair of glasses was found in 1953 under the choir stalls in the nunnery at Wienhausen, founded in the 13th century. Like most glasses at that time, the strength of the lenses was +3.4 diopters, showing that they served as a reading aid to compensate for presbyopia.
The beginnings of automation
With the invention of printing, the demand for reading glasses increased dramatically as more and more people learnt to read. The lengthy process of grinding by hand, however, prevented mass production of reading aids. Thus it became imperative to automate the lens-grinding process. As early as the 16th century, there had already been some quirky constructions to grind several lenses in series simultaneously.
Diderot’s encyclopedia, published in France from 1751, includes illustrations of various devices and tools used to make lenses for a variety of optical instruments, including spectacles. The grinding machine has a solid wooden frame similar in principle to that of a potter’s wheel. A large flywheel is driven by a hand-operated crank, with a leather drive belt transmitting the rotation to a small wheel, thus multiplying the speed of rotation by a factor of 1: 5. The precast biconvex glass lens is mounted in a concave support cup on a vertically mounted rotating rod. This in turn is connected via a wooden bevel gear to the small wheel in such a way that one turn of the hand crank makes the lens rotate five times about its own axis.
Above the lens holder there is a metal bar fixed with two wing nuts, to which various grinding and polishing heads can be attached. These are shown in the subsequent figures with different radii of curvature, showing that different lens thicknesses can be ground on the same machine. The concave polishing heads and the holders for the lenses are made of metal – usually brass – prefabricated on a lathe. A thin piece of leather between the lens and the holder prevents the surface of the lens from being scratched while it is being worked and held securely.
The illustrations shown here are from one of the numerous editions of the Diderot encyclopedia. They were purchased individually from one of the many booksellers on the banks of the Seine in Paris. Unfortunately, today there are hardly any complete editions of the famous encyclopedia from the 18th century on the market; savvy dealers preferring rather to separate them and sell the pages individually, in order to make more money.
Diderot Lunetier 1, Lenses from different sources.
Diderot Lunetier 2, Tools.
Diderot Lunetier 3, Tools.
Diderot Lunetier 4, Cutting and polishing machines.
All pictures in this article are courtesy of the Institute for Scientific Contact Optics Ulm.
Principal knowledge on procedures and best practise
The objectives of this article are to provide general principal knowledge on ophthalmic coating manufacture procedures and best practises based on a hands-on lifetime experience in coating manufacturing. Moreover, it is meant to draw attention to pitfalls and possible risks, to show shop-floor level staff how to apply themselves, to take ownership of the work and to enable suitable candidates to be an efficient coach in the lab. At the end of the day the quality level achieved in a coating department is determined by the quality of workmanship of the least trained staff. By Georg Mayer
The text is an extract of a tutorial held first at the Annual Society of Vacuum Coaters (SVC) Techcon 2019 in Long Beach and to be presented again in an updated version at this year’s SVC Techcon April 22nd in Chicago. I will focus on the next few pages on some highlights of the full day course tutorial. In nearly 20 years of sole responsibility of the coating division of a then market leader, I gained some valuable lessons. Having had the privilege and pleasure to work with staff from 5 European and African Countries was an enriching and exciting experience.
Some ground rules for every case
Here are a few conclusions after working nearly 40 years in coating
- Be a professional pessimist, expect the worst to happen and plan accordingly.
- Don´t assume, ask! There are no stupid questions, only stupid mistakes.
- Be a stickler to the rules of the process owner, don´t change anything, it is always better to ask again.
- Consistency is the name of the game, more of that later …
You might ask yourself how can this experience still be of any interest today, in our increasingly automated Rx factories, gearing up for Industry 4.0?
Rx lens making is mostly automated – a computerised digital production with the ability to check and verify every lens to all standards applicable in situ. Rx surfacing runs in a continuous flow, job by job and can be driven by only a few staff with the help of advanced fully integrated lab management software.
By Elisabeth Mayer
However, when the Rx jobs arrive at the coating department´s door we seem to go back in time.
We interrupt the job flow for batching, not only once but often twice with related waiting times and sorting action per material/index and per coating type. Due to the unique shape and form of Rx lenses we still have too many hands on those lenses, all the way through the coating department. First they go from batching into carriers to cleaning to hardcoating and curing. And then again they go from batching and handling into different carriers for preparation and vacuum coating, typically twice with manual flipping and prep-steps in between.
In summary, we have multiple manual manipulations on lenses and machines, meaning a lot of staff are in direct control of the coating quality. And if those manual manipulations on lenses, machines or processes are performed in a country by operators with language barriers, training needs particular attention as those staff form a crucial part of a consistently good quality.
On top of all of this we don´t have the ability to check the full and final quality of each of our coatings produced without destroying it, we can only test samples or “witness” substrates which have taken part in the same process and batch.
Test yourself: what do you think is depicted in these pictures? You can find the solutions at the end of this article.
Keep an eye on sampling method, process and FPY
This leads directly to a first major subject, – the sampling method as a basis for coating quality metrics.
- Sampling only works if all lenses are processed consistently to the rules of the process owner
- Only then such witness samples will have the same properties as all other “real” Rx lenses
In conclusion, if processes and staff are not consistent the sampling approach is misleading.
A second important subject is the previously mentioned process and the lab taking ownership and responsibility. The following list gives an overview of the most important points that should be considered regarding the process:
- Apply stable robust processes, manageable by the local infrastructure, machines and team.
- The process must be well documented and the documentation has to be easily understandable and available to staff at their workplace.
- Part and parcel of a stable process is strict maintenance of machines and use of the correct consumables since you can´t separate machine from process.
- Well trained staff is essential, each of them could be the weak link in an otherwise strong team.
- Create a work atmosphere that is open to continuous questions, learning and improvement, all the time.
Such stable and robust processes are the basis for a third most critical topic, the first pass yield (FPY) and its primary impact on an Rx lab’s ability to deliver consistently on time which leads to customer satisfaction.
A simplified example/case for this is based on the Markov Chain:
Of 100 lenses to enter a coating department, how many will come out “first round / first try” with its typical successive process steps? Let´s assume each main coating production steps FPY being
Hard coat 96%
First side AR 99%
Second side AR 99%
Looks pretty good, doesn´t it, but the key is that in combination the final score is a multiplication of 0,95×0,99×0,99×0,99=0,92 or 92%, meaning 8 of our 100 lenses didn´t make it first time round.
To make matters worse, in Rx where jobs are 2 lenses it could be double the number as a single bad lens in a job will hold back the other good lens too. So, this coating department could have a FPY as low as 84%, or in other words 16% of all work is not out first round in the expected time.
Once the whole Rx lab’s various factors have been modelled the results can be used for the prediction of the customer’s perception of this lab, i.e. if customers’ expectations will be met.
The main delivery quality indicators are delivery speed, reliability and consistency.
Such a lab might be competitive fast with the jobs making it on first attempt (84% FPY), but with a significant 16% of work not on time the lab will be seen as unreliable, and worse, of those 16% another 2% will not make it through even the second time and will need a third round. And because of this noticeable tail of very late work the lab is also regarded as not consistent.
Conclusion and brief outlook
Rx labs coating management must focus all efforts to achieve and maintain the highest possible yield rate for each type of product made by the lab, based on stable processes and well-trained staff, because of its primary impact on delivery quality and customer satisfaction.
Once this goal is achieved the focus can shift to removing the remaining major system shortfalls in most coating departments, i.e. to the improvement of the degree of automation and the reduction of the process times while still maintaining expected market quality standards for Rx lenses.
We can foresee some exciting new developments and projects in that direction, partly already in use or still to come, which will change the landscape of Rx lens coating to bring it closer to the level of automation seen already in Rx surfacing.
Solution for pictures a) to d):
a) Hardcoating failure
b) AR stack thermal cracking
c) Hardcoating striae
d) Droplet spitmark residue under AR
Sustainability in ophthalmology, what does that mean for our industry? Can we withdraw from the current debate about ecology and climate protection? Can we just ignore these topics? Undeniably, we are subject to physical-chemical laws in the manufacture and disposal of ophthalmic optical products. And supply and aftercare industries will carry on trade economically, simply out of self-interest. But do we have to tolerate unconditionally the poor working conditions in low-wage countries? Do we have any influence on business ethics there? The reputation of our industry will benefit if we discuss these environmental issues openly and look for solutions.
By Johannes Schweinem
The term ‘sustainability’ was originally coined by Hans-Carl von Carlowitz, when clearing forests to provide fuel and building materials. Around the year 1700, when neither natural gas nor oil were yet used as fuel nor concrete in the construction industry, wood was under threat because of short supply. Then coal mining took off with the advent of industrialization and the term ‘sustainability’ disappeared from view – especially as there seemed to be no limit to the supply of coal. Later, as the steam engine was supplanted by the internal combustion engine, hydrocarbons came on the scene which, as everybody knows, were – and still are – derived from oil. By 1960, there was a policy of ‘unrestrained economic growth’ and the throwaway society reached its pinnacle. The discovery of the ozone hole in 1980 and the decimation of forests in 1985 – to name just two key issues – made society aware of environmental issues. In 1990 discussions about recycling were instigated for the first time.
Cornerstones of environmental policy
In Germany, the Recycling Management Act was passed in 1996. The ‘green dot’ and the ‘yellow recycling bin’ are direct consequences of this law. Research based on drilling ice cores, which took place at roughly the same time, and the insights this gave into events over the last 150,000 years, provided the final proof of anthropogenic climate change. In 1997, the Kyoto Protocol was drafted, which was subsequently adopted as the United Nations Framework Convention on Climate Change. Sustainability became an international obligation which today is integrated into the three-pillar model:
- Environmental sustainability,
- Economic sustainability,
- Societal sustainability.
Environmental sustainability calls for examination of our ‘ecological footprint’. This is the natural area that is necessary to permanently enable a person’s standard of living and lifestyle under current production conditions. This includes the production of clothing and food, the provision of energy for heating, work and leisure, as well as the disposal of pollutants (e.g. residual waste). The area of the forest required to assimilate CO2 emissions is also included in this area (Fig. 1). Some Internet platforms offer an online calculator that allows people to calculate their own ecological footprint.
Fig. 1: Size of the “ecological footprint” of the world population in ha/head/year (Source: Global Footprint Network, Brussels, 2014)
Economic sustainability concerns natural resources, how long certain raw materials will continue to be available before they are exhausted (Fig. 2) and how they can best be shared, so they continue to be available to future generations. This includes, in particular, the recycling of used materials and the secondary raw materials which can be derived from them, as well as increased efficiency in the production of new goods.
Fig. 2: Metal extraction from primary raw materials (ores)/Plastics production from crude oil (Metal extraction from primary raw materials (ores)/Plastics production from crude oil)
Societal sustainability identifies and corrects the even distribution of the ‘footprint size’ across all nations. Ethical responsibility with regard to other cultures is just as important as the solidarity of all people with regard to public health. Europeans today want to know how our jeans are made in Bangladesh and under what conditions people in Far Eastern factories work for us. Exploitation is today largely rejected by a majority of the population. It is not always the cheapest which is best, where sustainability is concerned.
Spectacles – still the core business of ophthalmic optics
Spectacles are likely to remain the core business of ophthalmic optics for the foreseeable future. Besides good quality – perhaps the key attribute of a reputable local optometrist – the question of sustainability is becoming increasingly important. Not only do the materials used need to be biodegradable – this could be considered a shortcoming in daily use –it is also important under what conditions the lenses and frames were made. Is health protection assured for everyone involved in the production process? Are ecological and economic considerations compatible with the production process? Are the spectacles ultimately recyclable?
For an example, we should consider a pair of spectacles where the lenses are made of plastic with a standard refractive index of 1.6, the rims are made of Monel and the sides made of stainless steel. One can assume that the side tips are covered with cellulose acetate. The replacement rate for spectacles today is about four years. How, then, do the three pillars of sustainability impact on these spectacles – from manufacture to disposal?
Lenses made of plastic…
The monomers for the lenses are produced by a branch of the Japanese petrochemical industry and supplied in drums. Monomer 1 is a tetravalent molecule that reacts with terminal mercaptans. Mercaptans are short, homologous hydrocarbon chains with a terminal hydrogen sulfide grouping. The tetravalency affects the subsequent thermosetting of the finished lens. Monomer 2 is a di-isocyanate, i.e. a bivalent, reactive nitrogen-carbon-oxygen compound with a cyclic nucleus. From an ecological point of view, the chemical production of the monomers involves several intermediate stages requiring heat and releasing CO2 during the heating process. The chemical manufacturing technology in Japan as well as the occupational health and safety standards there are comparable to those in Europe.
… when casting the monomers…
In the further course of production, the monomers are supplied to those carrying out the casting in separate containers. The two monomers are poured together there into molds to make the blanks. Here, however, there are significant differences with regard to social and societal sustainability. On the one hand there are the castings manufactured under European safety standards, while on the other hand there are cheaper blanks from the Far East, manufactured without adequate health and safety protection often by underpaid employees, which are supplied to Europe.
Both the professional associations such as the Federal Institute for Occupational Safety and Health (BAuA) monitor that the workplace regulations specified in the German standards – also known as MAK values – are complied with in production. Just as any liquid can evaporate before it reaches its boiling point, i.e. takes vapor form, so too can monomers release toxic molecules into the surrounding atmosphere. The more toxic the volatile substances are, the lower the MAK values set by law. Monomer 1 is classified as “toxic”, monomer 2 as “very toxic”. The MAK value of the di-isocyanates is 0.005 ppm (parts per million) in ambient air, which may not be exceeded over an 8-hour working day. In Germany, the casting process takes place in isolated areas. In China – as a connoisseur of the Chinese casting market for optical products has reported – the two liquid monomers sometimes run directly over the employees’ hands. Here, it is right to ask whether social and societal sustainability is genuinely assured – even if business ethics are being taken increasingly seriously in China.
… and during edging
The smell of rotten eggs which arises in German opticians’ labs during edging is caused by hydrogen sulfide being given off. After all, 1.6 index plastic lenses contain 20% of sulfur, which originally stem from the mercaptans in the monomers. Although hydrogen sulfide is also toxic, the mucous membranes of our nose are so sensitive to the smell that irritation occurs long before a dangerous concentration of toxins can build up.
A study in 2004 by the Higher Professional School for Ophthalmic Optics in Cologne (HFAK) together with then professional association BGFE showed experimentally that even in the worst-case of grinding 42 high-index plastic lenses per hour in a closed room (without ventilation) only 1.0 ppm of hydrogen sulfide was emitted. The MAK value of hydrogen sulfide today is 5 ppm. So this effectively gives the all-clear for toxins in German grinding labs.
The middle part made of Monel…
Monel has long been a common material used for the bridge of a pair of spectacles (Fig. 3). A similar alloy (nickel-silver) is used to make closing blocks, bridge and joint parts. The metals nickel, copper and zinc, mainly used in the alloys, only need be present as pure metal in small amounts. Thus the largest proportion of the nickel-based alloy Monel and the copper-based alloy nickel-silver are produced from charges of scrap metal (Fig. 4). During charging, high demands are made on the reproducibility of alloys, whereby – by presorting scrap by alloy composition and precise doses of scrap master alloys as melt addition – only a small proportion of pure metals from primary raw material sources (ores) are needed for fine adjustment. The method of scrap charging has the economic advantage that the process only needs to heat the charge to the melting point of the metal alloys, and the heat which otherwise would be needed to melt the ores – which may be four times as high – is not required. In ecological terms, too, this means that the reserves of ores can be saved in this way. In 2014, the European Copper Institute (ECI) in Brussels declared that the known reserves of copper from primary sources will have dried up in about 50 years. Consequently, the ECI is promoting the use of copper alloys from secondary sources (Fig. 5).
Fig. 3: Process of alloy-metal recovery from secondary raw materials (*Pure metals from primary sources are essential to obtain the required composition of the alloys)
Fig. 4: Metal extraction from secondary raw materials. Increase in the amount of recycling over the past 5 years. (Sources: Federal Institute of Geosciences, German Raw Materials Agency, Economic Association for Non-Ferrous Metals, ECI, Steel Industry, Remondis Recycling)
… and sides made of stainless steel
The same applies to spectacle sides, which in our model are made of stainless steel. The traditional coal and steel industry, i.e. the historic production of steel from the reduction of iron ore using coal in a conventional blast furnace, is no longer absolutely necessary today and has largely been replaced by electric-arc furnaces melting recycled steel scrap. The stainless steels used for spectacle frames are mainly ferritic steels made of alloy steel and chrome. Again, here too, the production of crude steel has largely been replaced by alloys.
Over the wearing life of our model spectacles – in our example four years – no di-isocyanate was given off by the glasses (Fig. 6). The finished blanks are non-toxic because the substituents of the monomers (here mercaptans) are poly-added with the isocyanates. Polyurethanes consist of the chemically stable urethane moiety within the macromolecules. Plastic lenses comply with European standards without compromise, in accordance with CE conformity and the German Medical Devices Act. Likewise, the nickel content of the spectacle frame is protected by proven coating processes.
Fig. 6: Treatment plant for residual waste.
The end of a pair of glasses…
What happens to our spectacles after four years at the end of their life? It certainly makes sense to collect old pairs of spectacles and give them to the German Opticians’ Development Service or equivalent institutions in other countries. In Germany, the Association of Opticians and Optometrists can also provide addresses of opticians who maintain contacts to Zimbabwe or Mali. Passing on technologies and goods to other countries may well be an ethical responsibility that should not be ignored.
From an ecological point of view, recycling also makes sense by ensuring that metal frames are given in large quantities to a scrap merchant, thus relieving the pressure on primary ore resources. Unfortunately, lenses made from plastic cannot be recycled because as thermosets they cannot be remelted. So what happens to them? And how do customers finally dispose of their old glasses? The legislation does not require the individual parts of the spectacles to be separated and often they simply end up in the residual waste. So let us now take a closer look at this final stage in the life of a pair of spectacles.
… into the incinerator
The content of a residual waste bin is transported by truck to a collecting area, from where it is taken by train to a large waste incineration plant. The Recycling Management Act of 1996 requires sorting of residual waste for recyclable materials; residual waste may no longer be dumped untreated into landfills. Waste incineration plants are now well established, ultimately leading to a reduction in the overall amount of waste. At these waste-disposal plants, the incoming waste is initially sorted on shakers using magnets (Fig. 7). As likely as not, the ferritic of the sides will be attracted to the magnets and thus the whole spectacle frame will be fished out. However, if the spectacles were first crushed in the trash – with the steel sides becoming separated – the central part of the spectacles may end up in the incinerator.
Fig. 7: Cotton plants – the raw material for cellulose acetate.
On rotating grids, the burning garbage is constantly turned over. The plastic parts of the frame (side tips, nose pads and plastic lenses) burn mainly to carbon dioxide and water vapor. The cellulose acetate side tips account for about 45% by weight of the carbon-neutral CO2 which the cotton plants absorbed during growth (Fig. 8). More problematic are the lenses whose cyclic components of the plastic matrix in combination with chlorine from the overall waste can produce dioxins and furans.
The legislation specifies strict regulations concerning the retention of pollutants in waste incineration plants. Thus, the incineration chambers are designed in such a way that temperatures of up to 1200 °C occur above the flames, to ensure thermal decomposition of any dioxins or furans. The high temperatures, however, also have the side effect that nitrogen oxides are produced. Thus sulfur dioxide, sulfide residues and suspended particles are also left behind by our 1.6-index lenses. For this reason, filters are installed in waste incineration plants, such as suspended-particle scrubbers, hydrogen halide and sulfur dioxide separators, resin filters for heavy metal removal, ammonia catalytic crackers for the removal of nitrogen oxides and active carbon filters through which all gases have to pass before water vapor and carbon dioxide leaves the chimney. A little digression concerning plastic frames: it can be calculated stoichiometrically that a 12 g polyamide spectacle frame gives off 28 g CO2 during combustion.
From an economic point of view, when plastics are burnt, on average 60 % of the process heat required to produce the plastic is recovered as a combination of heat or power, and then returned to the grid as electrical energy. The remaining metal components of our model spectacles can also be recovered from the ashes and slag. They, too, can thus be returned to the metallurgists for use as a secondary raw material.
Polyethylene and polypropylene (plastic bags) burn with few emissions to H2O and CO2, whereby their calorific value is equivalent to heating oil (around 40,000 kJ/kg). If necessary, dirty packaging from the yellow recycling bin can be added, if the delivered residual waste mixture proves inefficient as a fuel.
Sustainability has now become a high priority. Today’s industrial practices are setting new economic-ecological standards. The questioning of the ethical production of cheap plastic lenses in part is up to us. We are all challenged to confront the key issues of the 21st century, to ensure that life for future generations remains worth living.
 All references to government or legislation in this article refer to Germany unless stated otherwise.
Innovation is the heartbeat of society. Big ideas for existing problems combined with the right technology results in innovative products with the power to revolutionize industries. The first industrial revolution resulted in tools, the second industrial revolution resulted in serial production and the third industrial revolution, 3D printing, will result in mass customization.
Across all industries, 3D printing has come to the forefront as a disruptor in creating completely new products and for more efficient manufacturing processes. It is now poised to offer a solution for frames and for custom lenses. The eyewear requires both customization and mass production, 3D printing fits seamlessly to fulfill those requirements.
By Guido Groet
Trends in society offer opportunities for the eyewear industry
If you zoom in on four main trends today that have a major impact on the eyewear industry:
- The first trend is instant gratification, end-users want products immediate and without any compromises. To address this you see emerging platforms across the eyewear industry like online ordering platforms for sunglasses, websites to try out your glasses in a virtual environment or opportunities to measure your own prescription with a mobile device.
- The second trend is customization of products, end-users prefer custom products over mass products. They want unique products tailored to their needs. This results in evolving distribution models to satisfy the end-user. For the eyewear industry, you see this emerging in custom 3D printed frames fully tailored to fit your face or 3D printed lenses customized for your eyes.
- With technology evolving every single day, the third trend is that more products become smart. Why only correct vision through eyewear when the functionality of a phone or other devices could be added? Big tech players develop first generation smart glasses to enhance your regular glasses with functionality. Glasses have been passive devices for decades but with integration of technology your glasses will become active. The end-user will have one single product with multiple functionalities and might not need a phone at all.
- The final trend is sustainability, end-users take into account the burden manufacturing processes and products have on the environment. The world will be focused on reduction of waste, energy and reducing the carbon footprint of product manufacturing. Also elimination of toxic components will be the norm. With 3D printing, you can provide a more sustainable alternative compared to traditional manufacturing as you add material.
The impact of 3D printing for the eyewear industry
3D printing has the most impact where there is a need for volume, and individual customization, which is exactly what the eyewear industry requires. The eyewear industry has found a way to address this need within the limitations of today’s legacy technology. With offering 3D printed lenses you will not require huge inventories, a complex supply chain or have a limited product design.
3D printing offers ophthalmic labs and eyewear companies an opportunity to maximize the industry’s potential and change aspects of the eyewear industry. This plays in satisfying the trend of instant gratification.
Focus on smart glasses
Let’s focus on the one trend where we see a strong contribution from 3D lens print technology: smart glasses. After the introduction of the first smart glasses several years ago, the adoption was not quite what technology companies anticipated. However, in today’s world people get more and more interested in adding multiple functionalities to their products.
The adoption of the smart phone is an example of adopting smart devices. But why stick to adding more devices in your day to day life while you can combine it into one single product? This paves the way for introducing real smart glasses.
Sixty percent of the population is not able to see without a prescription. Each lens must be customized for the wearer. The lenses cannot be simply picked from a shelf. Roughly 4.6 billion people require vision correction. In the developed world, greater than 84% have selected spectacle lenses as their primary vision solution. People need a good vision solution in smart glasses. Any smart glass solution will require addressing the prescription need, and this is where 3D printing can play a unique role.An AR/VR design which truly integrates prescription lenses would meet the visual needs for the majority of the population. A manufacturer which takes this approach would gain a cosmetic and functional advantage over competitors who are offering bulky, heavy, unattractive products which attempt to fit over regular prescription eyewear. Providing the best wearer experience using an amazing new 3D print technology might be what is needed.
What eyewear experts considered impossible is now possible; transparent and high-quality 3D printed prescription lenses. We are the first in this space and our 3D printing technology is installed commercially, printing prescription ophthalmic lenses daily, for shipment to end users. We understand the complexities of ophthalmic lenses and are poised to work in partnership with the AR/VR industry.
Most of the AR/VR glasses available today look bulky and uncomfortable and people even feel silly wearing them. By truly integrating prescription lenses into smart glasses design, manufacturers have an opportunity to disrupt the industry of mostly bulky, heavy and unattractive products. Some companies have made steps to provide prescription inserts which can work but could be significantly improved and be truly part of the design.
When manufacturers treat prescription lenses as a “must have” element instead of an afterthought, this will open many opportunities for AR/VR sales and positively influence buyer adoption.
Prescription lenses for technology companies
Technology companies often have a limited understanding of the complexity of delivering prescription lenses to end users. Prescription lenses are medical devices, with regulatory requirements. Many technology companies have a business model which stocks products until a consumer places an order and they ship the product to the consumer. Once vision correction is required, this model will not work.
To stock just the most common lens prescriptions would result in more than 3 million SKU’s (Stock Keeping Units). Luxexcel technology offers a turnkey solution without massive capital investment. In the long term point-of-purchase printing is possible so when you buy your smart glasses you can get your prescription added right in the store.Offering an immediate solution to the customer demand.At the moment 3D printing of lenses is deployed in the eyewear market, commercially shipping lenses daily to consumers through customer labs. In parallel, we are providing prescription lens inserts to several companies in the AR/VR space. We have not yet rolled out the technology to fully integrate 3D printed lenses where we print on top of the smart technology.
Is the eyewear industry ready for a sustainable future?
Soon our world will be populated by more than 10B people. We collectively generate an impressive amount of waste. It is clear that also the eyewear industry will need to make its own contribution and work towards reducing the impact on the environment.
Today this is clearly not happening enough with legacy technology. 3D printing is a technology that can help reduce the carbon footprint and the amount of waste generated, for our industry.
Is the eyewear industry ready for a smart future?
Several of the four main trends are addressed by eyewear companies. New innovations emerge every single day like custom 3D printed frames, measurement devices for your own home and new AR/VR glasses.
However, 3D printing opens up real possibilities for ophthalmic labs and technology companies to make custom products with added value. Examples are lenses with a screen or reflective device inside.
We have made prototypes in which we print on top of the electronic device creating an integrated visual solution. What it does for the end-product is to combine smart and functional eyewear with an acceptable and fashionable appearance. The eyewear industry is ready for the next innovation, but are you?
Guido Groet is Chief Commercial Officer at Luxexcel. He has an extensive background in technology and has been instrumental in bringing new technologies to market. Having worked for many years in both Europe and the USA for technology giant ASML world leader in semiconductor equipment. He has held VP positions in Business development, M&A, and Strategy development and has been in charge of the business relationship with key optics partner Zeiss in Germany. He has also been COO and subsequently, CEO of a venture capital financed company in disruptive high tech manufacturing technologies. At Luxexcel Guido is in charge of all commercial aspects of the business.
Self-driving cars, service robots and smart homes – all AI applications – will change how we live. This article discusses the following questions: Which existing technical innovations already use AI and which will do so in the near future. What effect will they have on society? How should we deal with these effects? Should we intervene to regulate them, in order to make sure we pass on a sustainable world to our children?
By Wolfgang Ertel
What is Artificial Intelligence?
For over sixty years, Artificial Intelligence (AI) has been concerned with the question of whether and how computers and robots can do things that we humans can (still) do better. Many cognitively difficult tasks for us, such as playing chess and mathematics, logic and pattern recognition, can be solved better today by computers than by humans, for example using AI techniques.
Other tasks, such as recognizing and gripping a key in your pocket or finding a door in a room, are easy for humans, but (still) pose considerable difficulties for robots. According to the definition above, these are the tasks which Artificial Intelligence is currently working on. Indeed, in service robotics today, intense research is being carried out into tasks such as learning to grasp an object or how to recognize an object in complex surroundings.
Particularly significant here is the ability of people to learn, which in many areas is still far superior to that of computers. Ever since the early days of Artificial Intelligence, research has been going on into machine learning, and since the mid-1980s there have been a number of breakthroughs. For example in 1987, a program called Nettalk was developed that was capable of reading texts aloud. In 1999, LEXMED introduced an intelligent medical diagnosis program [SER01] that was as reliable at detecting appendicitis as an experienced surgeon.
Deep Learning led to a breakthrough
In the past decade, there has been a breakthrough in the field of machine learning. Based on the results of many researchers in the field of artificial neural networks, computers today can use deep learning to recognize almost any object on a photograph, better than we humans can.
This success is based on the fact that it is now possible to train neural networks successfully with up to 1000 layers of simple neurons on the computer. Previously, neural networks with more than three layers posed major problems and only relatively simple objects could be detected by pattern recognition in images.
In the years ahead, deep learning will lead to significant improvements in medical diagnosis, for example based on radiographic imaging. Deep learning will also play a major part in autonomous driving, which is expected to become common practice worldwide from about 2020 onwards. Another important application of deep learning is in service robotics, because robots need to be able to recognize objects reliably. Indeed this is already possible to a very high standard.
AI will make almost all specialists redundant
Already today, or at the latest in the near future, many difficult cognitive and specialist motoric tasks can be performed better using machine learning algorithms than by humans. For example, in games like chess, Go, and poker, even the most skilled humans can no longer compete with adaptive algorithms. Automatic pattern recognition, already mentioned above, will become common practice in all areas of life. Diagnoses in almost all areas of medicine and technology are now done better, faster and more effectively by computer.
AI systems are superior to humans for a variety of reasons:
- Potentially a computer has a wider variety of sensors at its disposal than a human being. An autonomous vehicle is not only aware of its surroundings through 3D cameras but also through laser scanners, radar and ultrasonic distance measurement.
- Machine learning algorithms are now able to extract knowledge from large amounts of data in multi-dimensional space and apply it quickly and efficiently. Such quantities of data can be far larger than anything a human could assimilate, even over the course of a whole lifetime. For example, a doctor who performs a specific operation every day may carry out this operation about 8000 times over 40 years. By contrast, a computer can learn from the data of millions of operations, thereby significantly improving the results.
- Unlike humans, computers are able to share entire content of their memory – and thus their complete knowledge – in seconds with other computers. Thus with Artificial Intelligence learning can be distributed. For example, all cars in a fleet of millions of autonomous vehicles can send all the data collected on the road to a central server, where a learning algorithm can derive new driving strategies from the data. After extensive trials, these driving strategies can then be downloaded to all cars, so that all the vehicles have the benefit of learning from the data of the other vehicles. This is not possible with humans, because of the physical limitations of the brain and the body. In our case, our knowledge is specific to our brain which is firmly embedded in our body. In contrast to computers, the way we exchange knowledge and information through language and images is very complicated and time-consuming.
- Humans are not entirely rational, everyone has their own ethics . In the case of machines, this can be defined and implemented in a consistent way.
- We humans learn complex skills, such as motoric, best as a child. When we perceive objects, our brain has been trained to recognize things that we often saw as a baby, such as human faces. The recognition of cancerous tissue on noisy MRI images, however, did not form part of that experience as a toddler, and thus it can only be learnt in adulthood, when the brain is no longer so well adaptive or so fast. Highly specialized deep learning networks have a clear advantage here.
- The evolution of technology has developed exponentially over time since the invention of the steam engine in the eighteenth century. Where AI is concerned, this means that we should expect the pace of innovation to be faster over the next ten years than in the previous decade. This prediction is further supported by the fact that large parts of industry have not yet begun to embrace the current successes in machine learning.
Limits to Artificial Intelligence
Despite all the successes, adaptive AI systems are still inferior to humans in many fields. Coping with completely new situations remains a major problem. For example, an office worker or secretary is often faced with unexpected tasks or problems and has to come up with creative solutions. Even the most intelligent machine cannot handle such situations. Although creativity is a key focus of research into AI at present, there is still a long way to go.‘ Consciousness ’ still remains a major problem. Although research into consciousness has a long tradition in philosophy, there are few promising models for the implementation or evolution of a consciousness or intrinsic motivation in AI. This is related to our ability to reflect on ourselves at various levels and then draw appropriate conclusions. Research is being carried out into the field of ‘Artificial General Intelligence (AGI)’, however thus far with modest success.
Artificial Intelligence and the workplace
In January 2016, the World Economic Forum published a study [SS16], much quoted in the German press, which forecasts that over five million jobs would be lost in industrialized countries over the next five years due to Industry 4.0.
This forecast is hardly surprising, since the automation of factories, offices, administrative tasks, traffic, homes and many other areas since the invention of the steam engine has ensured that more and more work is now done by machines, computers and robots. Since about 2010, AI is one of the most important factors in this development.
Presumably most people agree that heavy, dirty and unhealthy tasks are better done by machines; which is why we are willing to let such changes continue.Thus in many cases automation is a blessing for humanity, at least when it is not accompanied by harmful side effects such as damage to the environment. Many of the unpleasant tasks mentioned above can be done faster, more accurately and above all more cheaply by the machines. This seems almost like paradise on earth: we have less and less unpleasant work to do and more time for the nice things in life – with the same, or even more wealth. After all, business would not use such machines if they did not significantly improve productivity. Nevertheless, it does not seem as if we are on the way to paradise.
For decades, we’ve been working over 40 hours a week, we feel under pressure, suffer from burn outs and other stress-related conditions and actual wages continue to fall. Why is this so, when productivity continues to rise? Many economists say this is all down to ‘competitive pressure’.
Due to competition, companies continually have to reduce their production costs and consequently dismiss workers, leading to increased unemployment. In order to avoid a reduction in turnover, due to the lower prices for the products, more products have to be manufactured and sold. Economic growth has to be sustained!
In a country such as Germany – and most other industrialized countries including China, where the population is no longer growing – for the economy to continue to grow, inevitably each citizen has to consume more.
This requires creating new markets1 and advertising has the task of convincing us that we want the new products. Only in this way – allegedly – can “sustainable” prosperity be assured. There seems to be no way out of this growth and consumption spiral. This has two fatal flaws. On the one hand, increased consumption does not make people happier; on the contrary, mental illness is on the rise. Even more obvious and more catastrophic are the effects of growth on our living conditions. It is no secret that the limits to global growth have long since been exceeded [MMZM72, Ran12]. In other words, we are depleting nature’s finite resources and thus living at the expense of our children and grandchildren, who consequently will have worse living conditions than we do today.
It is also well known that the additional growth in our economy continues to have a negative effect on the environment – for example, through increased amounts of CO2 in the atmosphere resulting in climate change [Pae16]. We are destroying the very basis of our own life. It is thus clear that we should abandon this fatal growth spiral immediately in order to ensure a future worth living. But how?
Let us go back to the way to paradise that AI and automation seem to be offering. Apparently the way we have adopted it does not lead to paradise. Understanding this problem and finding the right way to go is one of our key tasks today.
Due to the often complicated interrelations, this cannot be fully discussed here. Nonetheless, I would like to give the reader some food for thought and recommendations for action.
Despite the fact that economic productivity continues to grow in almost all areas, employees are still required to work as hard as ever while the actual wages fall [Pik14]. Thus workers do not benefit from the growth. Thus the question has to be asked: where are all the gains in productivity going?
Obviously not to the people who have created them. Instead, some of the profits are spent on investments and thus further growth, with the rest ending up with the people who own the capital [Pik14], leading to ever greater concentrations of capital among the few wealthy people and private banks, while poverty around the world continues to increase. This in turn increases tension and ultimately leads to wars, mass migration and flight. What is lacking is a fairer distribution of productivity gains.
How can a fairer distribution of wealth be achieved?
Unfortunately, too few economists are carrying out research into this highly interesting topic. Apparently politicians have no sustainable solution to offer; and this despite the fact that politicians and industry are constantly trying to improve our economic system. Obviously, all previous attempts to optimize the parameters of our current capitalist system have not led to a fairer distribution of wealth – in fact quite the reverse.
That is why economists, and financial experts in particular, should now start to question the current system and look for alternatives. We should ask ourselves how the laws and regulations which govern our economy can be changed so that everyone benefits from the gains in productivity.
There is now a growing number of economists and sustainability experts coming up with very interesting alternatives. Without going into the details, I would like to briefly touch on some of the key issues and solutions.
The first problem is the provision of available capital by the banks. New money – needed among other things to sustain our economic growth – is today created by private banks. This is made possible by the fact that banks only need to own a small proportion of the money they lend out, namely the reserve ratio. The reserve ratio in the EU in 2018 is one percent.
The states cash borrow cash money from private banks in the form of government bonds thereby incurring debts. This is how our current sovereign debt crisis arose. The problem could be solved relatively easily by banning the creation of deposit money by increasing the bank reserve ratio to 100 percent [Fis35].
The provision of money then becomes a state monopoly of central banks again and the new money provided can be used directly by the state for the common good. There is no need of further justification that this simple measure would significantly alleviate the sovereign debt problem. For example, states would no longer have to pay interest on the money made available to private banks.
Other interesting elements of such an economic reform could be a change of the current interest rate system to the so-called natural economic order [Ken06] as well as the introduction of the welfare economy [Fel14] and the biophysical economy [GK09, Küm11]. The practical introduction of these two or similar economic models could lead to a tax reform, the main elements of which would be the abolition of income tax and a significant increase in VAT on energy and resource consumption [SR17].
This would lead to a more sustainable humane world of high prosperity, less damage to the environment and increased regional economic cycles. In order to fund such an improved economic system, economists should focus their research on alternative sustainable economic models based on simulations and empirical investigations. The main goals are to achieve a more equitable wealth distribution and ecological sustainability as well as a peaceful social community.
At the end of this section, allow me to quote the renowned physicist Stephen Hawking. At www.reddit.com1 he was asked what he thought about the unemployment caused by automation, to which he replied: “If machines produce everything we need, the outcome will depend on how things are distributed. Everyone can enjoy a life of luxury, if the machine-produced wealth is shared fairly. Otherwise most people will end up miserably poor, if the owners of the machines are allowed to lobby successfully against a fair distribution of wealth. Thus far, the trend seems to be toward the second alternative, with technology driving ever-increasing inequality.”
A shocking video at autonomousweapons.org shows how tiny autonomous killer drones, using current technology including AI, can find and kill their victims autonomously. Today such weapons, first and foremost in the USA, form part of the standard arsenal of a modern army, despite petitions from thousands of scientists against the use of such weapons4.
Politicians from all countries and the United Nations need to urgently tackle this issue and address the ethical problems. The situation is comparable to the invention of the nuclear weapons which ultimately were used. There is good reason to fear that killer drones will also end up being deployed. So does that mean research into AI should be stopped? If the answer to this question were ‘yes’, this would mean that some research into mathematics, neuroscience and other disciplines would also have to be banned.
Leaving aside the fact that many organizations would probably ignore such a ban, the logical consequence would be that science as a whole would then be banned. Those who are committed to the pursuit of knowledge certainly do not want that. Thus the issue of ethics and morality must be focused on the application of the research results, in this case the application of AI. Thus society has to discuss whether we want autonomous weapons that decide independently on life and death.
The singularity: human and/or machine?
Due to the ever increasing pace of technological development already mentioned, AI systems are fast becoming better and better. By comparison, human intelligence will only increase very slowly, if at all, over time. As the left diagram in Figure 1 shows, under these conditions there must come a time in the foreseeable future when the two curves of human and machine intelligence intersect. This point when machines overtake humans is called singularity and according to a survey of 170 AI experts [MB16] it will occur between 2050 and 2100, although such estimates are very speculative.
There could also be a scenario as shown by the middle graph in Figure 1. According to this example, the intelligence of the machines would never exceed the human level, but at best approach it asymptotically. It is sometimes argued that humans will never succeed in building a machine that is smarter than its inventor, but this argument ignores the fact that machines are capable of learning. The ability to learn is currently still limited to specific tasks. However, it cannot be ruled out that algorithms will be developed that lead to machines capable of learning at a meta-level and thereby continue to develop themselves further. Singularity could then no longer be excluded. Thus we should start now to reflect on the possibility of singularity and its consequences.
About the author
Wolfgang Ertel studied mathematics and physics at the University of Konstanz and received his doctorate in 1992 at the Technical University of Munich with the dissertation parallel search with randomized competition in inference systems. Since 1994 Ertel holds a professorial chair at the University of Ravensburg-Weingarten. His teaching areas are mainly, Artificial Intelligence, machine learning, Mathematics and sustainability. Ertel’s fields of research are artificial intelligence, machine learning and intelligent autonomous mobile robots.
MAFO – The Conference 2019 is the 20th conference dedicated to the professionals of the ophthalmic business which is organized by the publisher of MAFO – Ophthalmic Labs & Industry. We open the doors in Milan on February 22, 2019, on the day before Mido. For our special birthday, 12 top-class speakers are waiting to inform you about the latest technologies, trends, innovations and the market situation in the ophthalmic industry.