Machine tool sector has undergone radical changes during the past few years. There have been rapid developments in intelligent machining. Increasing mechanisation of manufacturing processes and other sectors has created an unprecedented demand for intelligent machining. Amidst the gloom of the all-encompassing pandemic the machining sector is one of the rare industries expected to witness a robust growth. Even if a majority of the forecasts come true, machining industry is expected to give a significant push to the sagging global economy.
Machine Tools market worldwide is projected to grow by US$40.1 billion, driven by a compounded growth of 6.2 per cent. Machining Centres, one of the segments analysed and sized in this study, displays the potential to grow at over 6.1 per cent. In view of the shifting dynamics supporting this growth makes it imperative for businesses in this space to keep abreast of the changing pulse of the market. Poised to reach over US$24.9 billion by the year 2025, machining centres will bring in healthy gains adding significant momentum to global growth.
Representing the developed world, the United States will maintain a 5.3 per cent growth momentum. Within Europe, which continues to remain an important element in the world economy, Germany will add over US$1.4 Billion to the region’s size and clout in the next five to six years.
Over US$1.2 billion worth of projected demand in the region will come from Rest of Europe markets. In Japan, Machining Centres will reach a market size of US$814.4 million by the close of the analysis period.
China, being the world’s second largest economy and the new game changer in global markets, has the potential to grow at 9.1 per cent over the next couple of years and add approximately US$10.8 billion in terms of addressable opportunity for the picking by aspiring businesses and their astute leaders.
Apart from the above-mentioned reason there other dynamics at play, too. Several macroeconomic factors and internal market forces will shape the growth and development of demand patterns in emerging countries in Asia-Pacific, Latin America and the Middle East. The research viewpoints and numerical data presented in this article are based on validated engagements with influencers in the market, whose opinions supersede all other research methodologies.
Machine monitoring happened very fast. However, once the enabling technology was put in place to permit real-time gathering and aggregating of performance data from CNCs (MT Connect, for example, is an important enabler), machine shops quickly embraced the capability. Now, the digital dashboard showing pie charts or green/yellow/red displays of machine tool status information has become a commonplace sight in shops, though it was practically non-existent not too many years ago. The change happened at kaleidoscopic speed and so we can reasonably expect the next step after machine monitoring will also happen quickly as well. I believe that step is liable to be machine learning.
As we can observe that there is far more data for the machine tools and processes to reveal than just what pie charts show. As machine monitoring systems get more efficient at capturing that data, and as the information available from ERP systems is also drawn in, the amount of data will soon outgrow the capacity of the machine shop personnel to analyse on a day-to-day basis. However, large data sets are both the resource and requirement for machine learning in an intelligent environment.
However, once a few enabling technologies are put in place, it seems reasonable to expect that this form of artificial intelligence will help manufacturers comb their performance data automatically to find opportunities to optimise processes or costing through improvements that otherwise might be too subtle or counterintuitive to see.
And yet, data is not necessarily wisdom. To get ready for a future in which machine learning is a commonplace tool in industry, I went through some books written by some renowned experts. One of the books I’ve found worth reading is The AI Delusion by economist Gary Smith. Contrary to the title, Dr. Smith does not discredit AI, but instead questions the level of faith at many places in it. AI simply looks for correlations in big data sets, that’s all. Humans finding correlations in data may or may not find something useful, he notes, and the same is true of AI. When AI comes to manufacturing facilities, it will be another tool in the shop — an effective tool but nothing more.
Some interesting points about AI and data analysis I drew from my studies include the following:
- Looking for correlations in data and expecting to act on them can be dangerous. Any large data set is going to have correlations that look predictive, but in fact do not mean anything. Patterns are part of randomness. If we find a correlation between two variables (say, discovering the rise in grooving insert tool wear correlates to a rise in job cost overruns), then we should consider the correlation meaningful only if we had good reason to believe the variables were related before looking at the data.
- Big data can be bad data. A big data set can be full of meaningless input that gets in the way of the few measures that are valuable.
- We can always rationalise why a seeming correlation found in data might make sense. This doesn’t make it true. As a test, assume the data suggest the opposite of the prediction you see. Watch how easily you can rationalise that correlation, too.
- The problem described in the points above has already played out in other fields. Seemingly convincing correlations that are actually meaningless are the reason why we’ve seen so many popular medical findings reversed (various substances that have been linked to cancer, for example). In manufacturing, the use of machine learning to mine data sets is going to reveal the same kind of noise.
- The new computational tools are only tools, and our words for them are only analogies. We should always keep in mind that machines using “artificial intelligence” are not really intelligent. Incidentally, machine learning does not actually result in a machine truly learning anything at all. People are the ones who learn (hopefully).
However, now is the age of intelligent machining and for that AI has become an integral part of the process. Our goal should be to take the positives of this evolving industry and add our contribution to the global economy. The pandemic has wreaked havoc and it squarely rests upon the shoulders of industry to put the miserably shattered economy back on rails!
Article by Arijit Nag
Arijit Nag is a freelance journalist who writes on various aspects of the economy and current affairs.
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1 Comment
Awesome post, thanks for sharing.