Considering how quickly engineering design and manufacturing have advanced alongside computational developments, it may surprise you that very few engineers are trained in both engineering system design and artificial intelligence. There are countless opportunities for breakthrough improvements in how we develop new technology using AI in engineering design, but to succeed in these challenging areas, engineers must understand a new speciality--Design for Artificial Intelligence. Chris McComb, Associate Professor of Mechanical Engineering at Carnegie Mellon, and his student Glen Williams, now Principal Scientist at Re:Build Manufacturing, have developed a Design for Artificial Intelligence (DfAI) framework in collaboration with researchers at Penn State University to educate and encourage the academic and industrial engineering community to adopt AI engineering design. "Most of the time, we view AI as a tool to add onto an existing system, but to develop better systems we need to integrate AI into the engineering design process from the very beginning," McComb explains. A core challenge is motivating institutions to make investments in the long-term potential of AI technologies.

Automation has slowly been creeping into the industry for many years now. The shift to machine work first began with the industrial revolution, where 70% of all American workers made their livings on farms. When the industrial revolution swept through the food and manufacturing industries, robots replaced all but 1% of those workers. The trend continues, but never have we seen a technological revolution such as the one that faces the world right now.

In part 1 we reviewed why artificial intelligence (AI) is moving slowly. In short, AI is growing and will continue. Reports which say growth is slow may be looking into specific trends, industries, or AI on a mass scale. However, maturity, confidence, ROI, scaling, and connectivity might be slowing mass adoption. Additionally, we saw what AI can do in manufacturing.

Even early concerns related to artificial intelligence (AI) have not appeared to slow its adoption. Some companies are already seeing benefit and experts are saying companies not adopting new technology will not be able to compete over time. However, AI adoption seems to be moving slowly despite early successful case studies. AI is growing, but exact numbers can be difficult to obtain, as the definition of technologies such as machine learning, AI, machine vision, and others are often blurred. For example, using a robotic arm and camera to inspect parts might be advertised as a machine learning or an AI device.

With greater investment from healthcare organisations and surgical robot technology about to become generic, the conditions are perfect for a boom in the surgical robotics market. But how can design engineers and technical medical staff ensure these new systems operate reliably and safely? Michele Windsor, global marketing manager at surgical robot battery manufacturer Accutronics, has a solution. While they may sometimes feel like a new medical technology, surgical robots have actually been around for several decades. The first robot system successfully conducted a neurosurgical biopsy in 1985, while the US Food and Drug Administration (FDA) approved its first surgical robot -- the da Vinci surgical system -- in 2000.

Manufacturing industry is in a transition stage taking big leaps towards digitization and automation; resulting in what we today call as Industry 4.0. Some of the aggressive users of robotic technology and artificial intelligence – something most players are still grappling to adopt in the manufacturing industry – see artificial intelligence the next big logical step for improving the productivity. Originating from Information Technology and Software industry, AI has got benefits in stores for numerous others and manufacturing is just one among them. But to explore the true potential of AI in manufacturing there has to be the inculcation of AI for manufacturing right from the concept ideation of product design which helps in better streamlining of processes at a broader level. According to a report by Infosys, about 29% manufacturers have adopted AI as a part of their operations and decision-making process.

CAD--specifically parametric CAD--was developed as an efficient solid geometry construction approach. Therefore, CAD programs are ideal for design engineers who need to express their concepts, whether the shape of an automotive part or the housing of a smartphone, in detailed 3D geometry. Generative design, algorithm-driven design and topology optimization are the common terms used to describe programs that allow designers to seek the best--or optimal--forms for a project using specific inputs, such as stress loads, pressure, weight and material choices. The term topology optimization is specific to the exploration of shapes, structures and solid geometry. It's usually associated with automotive and aerospace lightweighting projects, where engineers seek ways to reduce material without jeopardizing the design's safety requirements.

Over the past few years, machine learning (ML) has evolved from an interesting new approach that allows computers to beat champions at chess and Go, into one that is touted as a panacea for almost everything. While there is clearly a lot of hype surrounding this, it appears that machine learning can produce a better outcome for many tasks in the EDA flow than even the most seasoned architects and designers can generate. EDA companies have been investing in this technology and some results are being announced. But developers and users appear to be taking it slow for a couple of reasons. First, results are non-deterministic and nobody is quite sure how to assess the risks associated with that.