Jaguar I-Pace vs. Tesla Model 3: Which Is the Better Electric Car?

although both tesla and jaguar have made big mistakes electric cars are better than everALL EC 5 Elements for Architecting Integration SoftwareExperts believe that enterprise-wide data interchange can be streamlined with a dedicated EDI integration software combined with a focused approach. Here are some best practices for supercharging your EDI program. Learn more.To suggest that electric cars are having a painful birth would be a colossal understatement. Tesla clearly plowed this field and quickly recognized that the lack of a charging infrastructure was going to be a problem and, with reasonable effectiveness, dealt with it tactically. However, those "tactical" chickens are about to come home to roost and it probably won't be pretty.

Jaguar, the first company to offer a true alternative to the Tesla, did some things very right and some very, very wrong. Rather than developing a Tesla killer, it instead created an impressive SUV that could have been far better.

I bought the Jaguar I-Pace, one of a tiny handful in customers' hands right now, and I think the perfect electric car would be a blend of the Model 3 and the I-Pace (pictured above). I'll explain why and then close with my product of the week: a high-tech hearing aid I saw at CES that might give you one of Superman's powers.

Tesla's Brilliance and Nasty Mistakes

While Tesla isn't known for being the smartest car maker, having made a ton of manufacturing mistakes over the years, it is by far the most experienced company with electric cars. Its brilliant decisions include coming in at the top rather than the bottom of the market, as Fiat did. Fiat loses a ton of money on every electric car it sells. While Tesla did lose money, its losses were tied to explosive growth and not the profitability of the Model S.Tesla built a charging infrastructure reasonably, though far from completely, dealing with one of the two electric car problems. It has the closest thing to gas station equivalence for chargers right now, particularly when it comes to high-speed chargers, which Tesla calls "Superchargers." Finally, it focused on making the cars really safe, and actually broke some of Consumer Reports testing equipment because its cars were so robust.

Tesla Model 3

The number of connected devices worldwide is growing exponentially and this ‘Internet of Things’ affects every area of our lives from electricity to agriculture. A recently published International Standard will help ensure these systems are seamless, safer and far more resilient.

From autonomous vehicles to precision agriculture, smart manufacturing, e-health and smart cities, the Internet of Things (IoT) is already everywhere – and growing. It involves integrating “things” within IT systems, thus enabling electronic devices to interact with the physical world.

The applications are endless, but as the phenomenon explodes, so too does the need for trust, security and a base from which the technology can be developed further, with robust measures and systems in place.

ISO/IEC 30141, Internet of Things (IoT) – Reference architecture, provides an internationally standardized IoT Reference Architecture using a common vocabulary, reusable designs and industry best practice.

Dr François Coallier, Chair of the joint technical committee of ISO and the International Technical Commission (IEC) that developed the standard, said the IoT is growing fast due to rapid developments in ICT.

“So we saw a need for a reference architecture to maximize the benefits and reduce the risks”, he said.

ISO/IEC 30141 aims to do just that, providing a common framework for designers and developers of IoT applications and enabling systems that are “trustworthy”, meaning they are reliable, safe, secure, respect privacy and can withstand disruptions such as natural disasters and attacks.

“There are already many published standards for resilience, safety and security,” adds Coallier, “and this standard will provide the reference architecture to apply them to IoT systems.”

ISO/IEC 31041 was developed by joint technical committee ISO/IEC JTC 1, Information technology, SC 41, Internet of Things and related technologies, the secretariat of which is held by KATS, ISO’s member for Korea. It is available from your national ISO member or through the ISO Store.

Fear mongering about killer robots and the recent deaths connected with Uber and Tesla autonomous vehicles have rekindled concerns about artificial intelligence in the machines around us. We are well beyond answering Alan Turing's question, "can machines think?" There is now good reason to ask how we should think of AI, and what we should expect from it.
There have been phenomenal advances in AI in just the past few years. They are due in part to advances in processor technology that have increased exponentially the compute performance for artificial neural networks, the development of deep learning software frameworks, and the massive amounts of data mined directly from the Internet and the world around us.
We now can train artificial neural networks in the time it would take to make a cup of coffee. Should that scare people? Not really.

It Won't Be Perfect
You have to remember that these solutions are being trained for a specific function. They do not think out of the box, do not ponder the meaning of life, and do not have feelings. In most cases, especially today, both the initial training and continued training are limited to large server systems in cloud data centers.
As a result, public interaction with AI is limited to cloud-related services like Web browsers or trained models that then are passed down to what we call "edge devices" (referring to the edge of the network) such as smart speakers, smartphones or even cars.
Eventually, continued training or even initial training may be done at the edge, but that may take a revolutionary change in processor technology -- such as neuromorphic computing, which is only in the research stages.
"AI" is exactly as the name implies -- the ability to acquire and apply knowledge and skills -- meaning that it learns over time and, more importantly, learns with additional data. The more data a system utilizes for training in the form of files or even live sensors, the more accurate it will be in performing a specific task.
However, as a form of intelligence, it never will be perfect. Just as humans learn through new information and interactions, so do machines. New teenage drivers may be caught by surprise the first time they drive on ice, but they learn from the experience and get better with time. So too will AI-based systems, but there always will be uncertainty with new data or circumstances.

We'll All Be Safer
The potential for AI to enhance people's lives and change society are endless, but the areas where we'll see the greatest short-term impact are healthcare and transportation. Consider the possibility of having genetically engineered prescriptions for each person, or the ability to find cures for an infectious disease in days, or even hours, because of the abilities of AI systems.
Also think about autonomous trucks and cars being able to ferry people and goods around the world with no need for stop lights. This is all possible, and it's coming sooner than you think.

AI already is used in a wide variety of scientific, financial, Web applications, user interfaces, manufacturing, and more. This is one of the most enabling advances in technology ever -- and like other major advances, it will change the world dramatically. However, it won't be perfect.

With autonomous vehicles, for example, the only way to eliminate any possibility of a human death is to separate pedestrian and vehicular traffic completely. That might happen, but it will require significant infrastructure changes that could take from decades to a century.

As a result, there will be more accidents that may result in more deaths from cars and other autonomous machines enabled by AI. However, the number of deaths and injuries will be drastically lower compared to human-operated machines. Just as airline accidents have become uncommon, so too will auto and other accidents, due to the use of AI. The rarity of such accidents, however, will result in spectacular headlines when they do occur.

AI also will be used in defense applications, another case in which it should improve systems to reduce or prevent virtual and physical attacks, as well as loss of human life.

Tesla vs. Jaguar: The First Real Electric Car 

 

Tesla

Tesla was first to build a decent electric car for this century. Not only that -- its Model S set records in terms of safety and reliability. Most of the problems the firm has had have been due to a lack of competency in manufacturing and a borderline insane CEO. However, the design of the cars, with the exception of the Tesla X, generally has been better than first rate.

I-Pace vs. Tesla vs. Gas

As noted, the I-Pace (pictured above) is the first real challenger to Tesla's dominance. You'd think I'd be disappointed that it didn't do better on the track, given it was designed for the track. However, the I-Pace is a crossover, not a sedan, and when was the last time you saw an SUV run against a hot sedan and win on the track? An SUV is designed to go on and off road. It sits higher, and thus it won't corner as well. Plus, it has far more wind resistance.


Until recently, Tesla cars, when tracked, would go into limp-home mode after a lap or two. You couldn't track them at all until Tesla did a software tweak and introduced Track Mode in the Model 3, and now it's a decent track car. On my last track day (I track a Mercedes GLA45 AMG) there was a Model 3 on the track, and it did impressively well. It was surprisingly competitive.

Now the issue with tracking any electric is where the hell do you charge the thing up? You use a ton of fuel when you track a car. I went through about a tank and a half of gas in my fast hatch in that one day on the track, and fortunately there was a place to fill up at the track.

There was a charger as well, but it looked to be a low-powered charger (not high-powered or Tesla supercharger), which means a full charge is measured in days not hours. That makes tracking any electric really risky. You could end up getting stranded at the track if you don't allow enough reserve power to get to a high-powered charger (there aren't many out there) or a supercharger (there are more, but they're still not exactly as common as a gas station).


Why the I-Pace Didn't Do Better
Now what has been driving a number of us nuts is that the I-Pace has a far bigger battery than the Model 3 typically ships with, and yet it has less range. The cause appears to be threefold: The car is an SUV and thus not as aerodynamic as the Tesla; the front motor Jaguar uses (which may be better off-road, but this hasn't been confirmed) can't be turned off to save energy; and the battery appears to have far higher protection against premature aging than the Tesla's.

The battery's life span is largely speculation, but it appears that Jaguar uses less of the battery than the Tesla does. I used to be the lead battery analyst for North America years ago, and I recall a Toyota test that concluded if you kept a battery above 10 percent charge and below 90 percent charge it would last indefinitely. It was charging to the limits that caused the battery to degrade.

Both Tesla and Jaguar have settings that are designed to reduce battery loading, but the Tesla's settings can be overridden while the Jaguar's appear hard-coded, which is why many of us are speculating on why the Jaguar doesn't have a greater range.

The Mystery of the Jaguar Grill

One of the funny things that keeps coming up on the Jaguar I-Pace is the fact it has a grill and none of the Tesla cars have one. Folks talk about this as being a styling thing, but the reason that Tesla cars historically have gone into limp-home mode on the track is that their batteries overheat.

I once read that to get the car around the track, one car magazine would buy a ton of ice and park its car on top of it, in order to bring down the battery temperature enough to track the car.

The I-Pace uses what appears to be far more effective battery cooling, thanks to that front grill. It also conceals an impressive front spoiler, which provides additional downforce for cornering. Granted, that front spoiler also may increase drag, but it should improve track behavior.

Wrapping Up

In many ways, the Tesla Model 3 is the more practical car. It uses Tesla's increasingly convenient charging network; it is a sedan, which is likely closer to the way most of us drive -- few SUV drivers ever go off road; and, as the third Tesla line, its design showcases lessons Tesla learned over the last two cars.


However, the Jaguar arguably is better looking. It is rarer (though all electrics are rare) and should convey more status. It reflects higher quality (given that it isn't cheap, it likely should). Since my wife and I use our SUV mostly as a pet carrier, the SUV design is far more practical for us, and the huge ugly thing that the Tesla X became just isn't an attractive alternative.

The Jaguar is just closer in design to what we need, and since we rarely drive more than 50 miles a day, the charging and range limitations aren't issues. Still, had Tesla made a small, attractive, SUV with fold-down back seats and without those cool (but very unreliable) gull wing doors, our selection process might have ended very differently.

What many are just getting around to understanding is that these new electric cars can change a lot with software updates. The Track Mode thing with the Model 3 is relatively new and expected to migrate to other Tesla vehicles (meaning you eventually might be able to make it around the track in a Tesla Model S, and the I-Pace's track performance is likely to improve as well). Unlike most gas cars, your electric likely will improve over time.

 Artificial Intelligence Solutions

As of now, there's little to no doubt that the future of e-commerce lies with artificial intelligence. From personalized 3D avatars and virtual fashion advisors for increased interactivity, to AI-gathered never-seen-before data for boosting sales, AI is at the helm of an e-commerce revolution.

In order to avoid confusion, let's separate more hands-on use cases for AI in e-commerce (virtual style assistants and immersive try-it-on sessions) from those related to data (product management and marketing insight gathering).

Remember the character Cher from the movie Clueless? She had her computerized ultimate virtual wardrobe assistant armed with yellow checkered outfits. Seeing the program instilled pangs of envy into the hearts of many teenage viewers of the film.

With the help of AI, though, such an experience is closer than ever. The Echo Look, Amazon's pilot of a "fashion assistant," recently was introduced in the U.S. to a limited audience.

The program analyzes the user's outfit through a combination of algorithms and human stylist insight and passes on its fashion judgement.

Net-a-Porter, an e-store offering designer fashion, is experimenting with technology that scans user data for planned trips and events, and then offers ad hoc style options.

Virtual fashion assistants still have a long way to go, but what was considered fiction just a few years ago now is becoming very real.

As business decision making becomes steadily more data-driven, demand for measurable metrics is higher than ever. Conversion rate, website traffic and customer engagement levels are important guides for marketers in all industries, yet some don't even realize they have need of previously nonexistent data that is now available.
 Artificial Intelligence Solutions

AI analytics tools of 2019 will be able to track the way potential customers interact with product imagery embedded into retailers' websites, whether 2D or 3D images, and present the most telling metrics on a heat map. Apart from dwell time, the tool will highlight points of customer interest and the best angles for thumbnail product positioning.
 Artificial Intelligence Solutions

With the insights gathered by AI, e-commerce merchants will be able to improve product visualization, choose winning color combinations, and put bestsellers at the forefront of their offerings. For instance, say that 70 percent of a product page visitors spent the lion's share of dwell time examining the clasp on a certain jewelry item or zoomed in to see the stitching of a particular dress. To an aggressive marketing team, this type of data could prove invaluable.

Information like this is completely new to the market, and according to Smart Data Collective, the way retailers track their inventory and consumer interest soon will be revolutionized with the help of AI. Keeping in mind ever growing consumer expectations, "soon" is actually now.

Human use in AI System



No longer just a fictional theme for far-fetched science fiction movies, artificial intelligence is now very much a day-to-day part of our reality. In factories, in intelligent transportation, even in the medical field, artificial intelligence (AI) is just about everywhere. But what exactly is artificial intelligence? As AI becomes more ubiquitous, why is there a need for International Standards? And what are some of the topics surrounding its standardization?

A recent report by the McKinsey Global Institute1) suggests that investment in artificial intelligence (AI) is growing fast. McKinsey estimates that digital leaders such as Google spent between “USD 20 billion to USD 30 billion on AI in 2016, with 90 % of this allocated to R&D and deployment, and 10 % to AI acquisitions”. According to the International Data Corporation2)  (IDC), by 2019, 40 % of digital transformation initiatives will deploy some sort of variation of AI and by 2021, 75 % of enterprise applications will use AI, with expenditure growing to an estimated USD 52.2 billion.

From perception to reality
But what exactly is AI? According to Wael William Diab, Chair of the new technical committee ISO/IEC JTC 1, Information technology, subcommittee SC 42, Artificial intelligence, the field of AI includes a collection of technologies. The newly formed committee has started with some foundational standards that include AI concepts and terminology (ISO/IEC 22989). Diab stresses that the interest in AI is quite broad, bringing together a very wide range of diverse stakeholders such as data scientists, digital practitioners, and regulatory bodies. He also points out that there’s something of a gap between what AI actually is today and what it is often perceived to be. “People tend to think of AI as autonomous robots or a computer capable of beating a chess master. To me, AI is more of a collection of technologies that are enabling, effectively, a form of intelligence in machines.”

He also explains that AI is often seen as a group of fully autonomous systems – robots that move – but, in reality, much of AI goes into semi-autonomous systems. In many AI systems, a good deal of data will have been prepared before being fed into an engine that has some form of machine learning, which will then, in turn, produce a series of insights. These technologies can include, but are by no means limited to, machine learning, big data and analytics.

Close-up of industrial robotic arm gripping a spherical roller bearing in a welding operation.

Umbrella of technologies
Currently a Senior Director of Huawei Technologies, Diab is Chair of the ISO/IEC subcommittee for good reason. Armed with several degrees in electrical engineering, economics and business administration from both Stanford and Wharton, his professional life has focused closely on business and technology strategy. Moreover, he has also worked for multinational conglomerates Cisco and Broadcom as well as been a consultant specializing in Internet of Things (IoT) technologies, most recently as the Secretary of the Steering Committee of the Industrial Internet Consortium. He has also filed over 850 patents, of which close to 400 have been issued, with the rest under examination. That’s more patents than those filed by Tesla – and not one of his applications has been rejected.

Diab’s true specialism lies in the breadth of his expertise – his range stretches from the early incubation of ideas to strategically driving the industry forward. It’s also why he’s so keen on standardization, as he sees it as the perfect vehicle for the healthy expansion of the industry as a whole. He argues that we need standards for AI for several reasons. First, there’s the degree of sophistication of IT in today’s society. After all, an average smartphone now has more power than all of the Apollo missions combined. Second, IT is moving deeper and deeper into every sector. After a slow start in the 1970s and 80s, people no longer need IT systems merely for greater efficiency and it is now needed to reveal operational and strategic insights. Finally, there is the sheer pervasiveness of IT in our lives. Every sector relies on it, from finance to manufacturing to healthcare to transportation to robotics and so on.

Part of the solution
This is where International Standards come into play. Subcommittee SC 42, which is under joint technical committee JTC 1 of ISO and the International Electrotechnical Commission (IEC), is the only body looking at the entire AI ecosystem. Diab is clear that he and his committee are starting with the recognition that many aspects of AI technology standardization need to be considered to achieve wide adoption. “We know that users care deeply and want to understand how AI decisions are made, thus the inclusion of aspects like system transparency are key,” he says, “so comprehensive standardization is a necessary part of the technology adoption.”

The AI ecosystem has been divided into a number of key areas spanning technical, societal and ethical considerations. These include the following broad categories.

Foundational standards
With so many varying stakeholders, a basic starting point has been the committee’s work on “foundational standards”. This looks at aspects of AI that necessitate a common vocabulary, as well as agreed taxonomies and definitions. Eventually, these standards will mean that a practitioner can talk the same language as a regulator and both can talk the same language as a technical expert.

Computational methods and techniques
At the heart of AI is an assessment of the computational approaches and characteristics of artificial intelligence systems. This involves a study of different technologies (e.g. ML algorithms, reasoning, etc.) used by the AI systems, including their properties and characteristics as well as the study of existing specialized AI systems to understand and identify their underlying computational approaches, architectures, and characteristics. The study group will report on what is happening in the field and then suggest areas in which standardization is required.

Trustworthiness
One of the most challenging topics for the industry is that of “trustworthiness”, the third area of focus. This goes straight to the heart of many of the concerns around AI. The study group is considering everything from security and privacy to robustness of the system, to transparency and bias. Already with AI, there are systems that are either making decisions or informing individuals about decisions that need to be made, so a recognized and agreed form of transparency is vital to ascertain that there is no undesirable bias. It is highly likely that this study group will set out a whole series of recommendations for standardization projects. Such work will provide a necessary tool and proactively address concerns in this area. “By being proactive in recognizing that these issues exist and standards can help mitigate them, that’s a huge departure from how transformative technologies were done in the past, which were more of an afterthought,” Diab says firmly.

Use cases and applications
The fourth area of focus is to identify “application domains”, the contexts in which AI is being used, and collect “representative use cases”. Autonomous driving and transportation, for instance, is one such category. Another example is the use of AI in the manufacturing industry to increase efficiency. The group’s reports will lead to the commencement of a series of projects that could include everything from a comprehensive repository of use cases, to best practices for certain application domains.

Societal concerns
Another area of focus is what Diab terms “societal concerns”. Broad technologies like IoT and AI have the ability to influence how we exist for generations to come, so their adoption creates impacts that go much further than the technology itself. One of these is economic considerations, such as AI’s impact on the labour force (which naturally goes beyond the remit of the committee). But others certainly do fall into its purview: issues such as algorithmic bias, eavesdropping, and safety directives in industrial AI are all central to what the committee must look at. How, for instance, should an algorithm be safely trained – and then, when necessary, re-trained – to function properly? How do we prevent an AI system from correlating the “wrong” information, or basing decisions on inappropriately biased factors such as age, gender or ethnicity? How do we make sure that a robot working in tandem with a human operator doesn’t endanger its human colleague?

SC 42 is looking at these aspects of societal concern and ethical considerations throughout its work, and collaborating with the broader committees underneath its parent organizations, ISO and IEC, on items that may not be under the “IT preview” but impacted by it.

Big data
A few years ago, JTC 1 established a programme of work on “big data” through its working group WG 9. Currently, the big data programme has two foundational projects for overview and vocabulary and a big data reference architecture (BDRA), which have received tremendous interest from the industry. From a data science perspective, expert participation, use cases and applications, future anticipated work on analytics, and the role of systems integration, the big data work programme shares a lot of commonalities with the initial work programme for SC 42. From an industry practice point of view, it’s hard to imagine applications where one technology is present without the other. For this and many other reasons, the big data programme has been transferred to SC 42. The committee will focus on how to structure the work at its next meeting. It is also anticipated that new work products for big data will be developed.

Industrial assistance and support from two engineers on a production line.

Exponential growth
The field of AI is evolving very quickly and expanding so much that the application of the standards being developed by SC 42 will continue to grow along with the work programme of the committee. Diab foresees many more standards taking shape, especially in areas that have broad appeal, applicability and market adoption.

And it’s also because of these standards that Diab is certain AI adoption will not only be successful, but is one of those major technology inflection points that will change how we live, work and play.

By-iso news

2010 की परियोजनाओं के साथ आर 80 मिलियन से अधिक की कुल परियोजनाओं के साथ, रोबोर पाइप सिस्टम एक वर्ष के लिए बड़ी सफलता से भरा हुआ है। उस सफलता में योगदान अनुसंधान और विकास में कंपनी का निरंतर निवेश है, और दक्षिण अफ्रीका की विकासशील दुनिया की अर्थव्यवस्था की जटिल सीमाओं के भीतर बुनियादी ढांचे के विकास की बढ़ती आवश्यकता के बारे में अद्यतित ज्ञान है।

रॉबोर पाइप सिस्टम्स की नई अधिग्रहित रोबोट वेल्डिंग आर्म मैनुअल वेल्डिंग की गति से चार गुना पर चलती है।
स्टील पाइप और पूर्ण पाइप सिस्टम के अग्रणी आपूर्तिकर्ता, रोबोर पाइप सिस्टम ने 2010 की पहली छमाही में विभिन्न परियोजनाएं पूरी कीं, जिनमें से एक स्लरी लाइन सिस्टम की आपूर्ति कर रहा था। इसमें सख्त ग्राहक आवश्यकताओं को पूरा करने के लिए थ्रूपुट में सुधार के उद्देश्य से व्यास में 12 मीटर लंबी और 600 एनबी व्यास के बड़े पैमाने पर उत्पादन पाइपिंग स्पूल के लिए स्टील पॉलीपाइप उत्पादन सुविधा का उन्नयन शामिल था।
एक डूबे हुए आर्क और रोबोट वेल्डिंग उपकरण में निवेश ने कंपनी की वेल्डिंग क्षमताओं का विस्तार किया है और सख्त समय की बाधाओं के साथ हाल ही में शाफ्ट पाइपिंग परियोजना की तेज़ी से डिलीवरी सक्षम की है।
डूबे हुए आर्क वेल्डर एक प्रक्रिया को सुविधाजनक बनाता है जिसके द्वारा धातुओं को एक नंगे धातु इलेक्ट्रोड और काम के बीच एक चाप से जोड़ा जाता है। शील्डिंग को एक दानेदार, फ्यूसिबल सामग्री द्वारा आपूर्ति की जाती है जो आम तौर पर फ्लक्स हॉपर से काम में लाया जाता है, जबकि फिलर धातु इलेक्ट्रोड से आता है और कभी-कभी दूसरी भराव रॉड से आता है।
रोबोर पाइप सिस्टम्स के सबसे रोमांचक अधिग्रहणों में से एक, रोबोट वेल्डिंग आर्म को इस साल की शुरुआत में कमीशन किया गया था और अब मैनुअल वेल्डिंग की गति से चार गुना पूर्ण रूप से परिचालित है। रोबोट वेल्डिंग आर्म पारंपरिक वेल्डिंग उपकरण के लिए एक बेहद लचीला विकल्प है और ट्यूब और पाइप के लिए संरचनात्मक और वाहन वेल्डिंग के लिए पूरी तरह उपयुक्त है। सेगमेंटेड झुकाव को फैब्रेटेड पूल झुकाव से अधिक इस्तेमाल किया गया है; हालांकि, पूल झुकाव के माध्यम से प्राप्त गुणवत्ता, जो सुनिश्चित करता है कि प्रवाह में कोई व्यवधान नहीं है, अब तक बहुत पसंद है। श्रम लागत बढ़ने और उच्च गुणवत्ता वाले उत्पादों की बढ़ती आवश्यकता के साथ, रोबोट वेल्डिंग आर्म आदर्श समाधान है। यह अतीत में एक बेहतर गुणवत्ता वाले मानक पर एक असफल-सुरक्षित खंडित मोड़ का उत्पादन कर सकता है और कंपनी की मूल्यवर्धन सेवाओं को बढ़ाने, स्थिरता, गुणवत्ता और गति सुनिश्चित करता है।

With 2010 projects totalling more than R80 million, Robor Pipe Systems is poised for a year filled with great success. Contributing to that success is the company’s continuous investment in research and development, and its up-to-date knowledge of South Africa’s increased need for infrastructure development within the complex limitations of our developing-world economy.

Robor Pipe Systems’ newly acquired robotic welding arm operates at four times the speed of manual welding.
A leading supplier of steel pipes and complete pipe systems, Robor Pipe Systems completed various projects in the first half of 2010, one of which was supplying a slurry line system. This involved the upgrading of a Steel Polypipe production facility to mass produce piping spools 12m long and 600NB in diameter, with the aim of improving throughput in order to meet tight customer requirements.
An investment in a submerged arc and robotic welding equipment has expanded the company’s welding capabilities and enabled the speedy delivery of a recent shaft piping project with strict time constraints.
The submerged arc welder facilitates a process by which metals are joined by an arc between a bare metal electrode and the work. Shielding is supplied by a granular, fusible material usually brought to the work from a flux hopper, while filler metal comes from the electrode and sometimes from a second filler rod.
One of Robor Pipe Systems’ most exciting acquisitions, the robotic welding arm was commissioned early this year and is now fully operational at four times the speed of manual welding. The robotic welding arm is an extremely flexible alternative to traditional welding equipment and is perfectly suited to structural and conveyance welding for tube and pipe. Segmented bends have been used more than fabricated pool bends; however, the quality achieved through pool bends, which ensure there is no disruption of flow, is far preferred. With rising labour costs and an increased need for higher-quality products, the robotic welding arm is the ideal solution. It can produce a fail-safe segmented bend at a superior quality standard to that in the past and ensures consistency, quality and speed, enhancing the company’s value-adding services.