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The term ‘industrial design’ was coined in 1919 by Joseph Claude Sinel, a self-proclaimed industrial designer. In 1934, the Carnegie Institute of Technology created a formal product-design curriculum, a major milestone in the history of industrial design.
However, the need for design for manufacturing and assembly (DFMA), or designing products for optimised manufacturing ease and cost, began only after the dawn of the industrial revolution. It took over a century’s time for DFMA to bloom during the 1980s.
Cut to the digital era, design for manufacturing is still in its nascent stages – as it is process-driven – and requires a lot of back and forth between the teams, partners, and vendors. From designing the shape of a needle to a t-shirt, a brand new Lamborghini and an Airbus – the process to design has not changed much.
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In the last few years, automation (industry 3.0) has become a buzzword; further, people slowly realise the importance of data in industrial design (i.e. industry 4.0) and its importance in further enhancing performance, efficiency, and optimising cost, among other things. According to Andrew NG, Founder – Landing.AI and Coursera “Manufacturing is one of those great industries that has a huge impact on everyone’s lives, but is so invisible to many of us.” and he wants “to take AI technology that has transformed internet businesses and use it to help people working in manufacturing.”
Currently, we are transitioning from industry 4.0 to industry 5.0, and computer-aided drawings (CAD) are still being used to design multiple objects and simulate the design. From costly workstations based mainly on UNIX to off-the-shelf PCs, CAD has evolved in the last 60 years, and 3D modelling has become the norm across sectors, including manufacturing, automobile, aerospace, OEMs, and others.
I believe that this is going to change drastically going forward, where we will be transitioning from digitisation, IoT (connected devices, data science, data analytics), etc., to Industry 5.0, which introduces AI into manufacturing (starting from design, prototyping, productions and sales and after-sales, supply chain, etc.).
The vision for the fifth industrial revolution would be about mass personalisation and customisation made possible by cooperation between man and machine, as humans and AI continue to work together in harmony.
However, when it comes to design for manufacturing, there are whole different types of challenges –
Some of them include
- DFM collaboration with multiple stakeholders across geographical locations and places
- DFM safety and security concerns
- Mass customisation that leads to multiple iterations in designs often causes high cost and wastage of resources
Collaboration as an Experience
Collaboration is one of the biggest challenges faced in the design for manufacturing world today, as the majority are still using legacy softwares for design purposes. As a result, there is a need for a tool, preferably cloud or web-based, for designers to collaborate, share comments, review, iterate, and work together on the project – similar to Google Docs or Canva.
This not only helps optimise the resources but also brings transparency to the whole process, and it becomes easier for the manufacturer to monitor, assess the designs, and work on the prototype accordingly.
In addition, Metaverse has a huge role in bringing people, companies and designers together, and they can seamlessly collaborate on design projects. This platform can also help get feedback from various stakeholders, showcase designs to prospective clients, ask Q&A and more.
Design is a creative field. Most design projects for manufacturing are very critical and sensitive, and it has a lot of constraints in terms of whom you share your work. Any misuse of the design can put the company in a lot of trouble.
Sharing design files with stakeholders is also a challenge because sometimes the size of the projects is huge, and there is a lack of reliable platforms that users or companies can trust to share this sensitive information more securely. Most often, they are stored in the local systems or hard disk, which may be prone to risk in case of any damage or system crash. Retrieving the files becomes cumbersome.
This is where Web 3.0 can come in handy, where it can offer a more trusted and secure way to encrypt the project files and protect the designs online so that there is no misuse in the process.
Reduce computing power, time & money
When it comes to designing for manufacturing, design engineers often end up wasting so much time and resources; data scientists and AI engineers have a huge role to play in terms of developing deep learning and machine learning models or building a tool that can help predict plausible designs based on the previous works, and eliminate redundant processes. This can help save a lot of computing power and designers’ time and make their work much more efficient.
Value driven Digital Transformation for Product Development Life Cycle
R&D and Product Development is more the art than the science. Artificial Intelligence and Data Science which has shown a lot of good signs in the areas like GANs predicting new designs, 3D Geometrical Deep Learning impacting Digital Twin to make VR more intelligent. The “value” will start the play once the designers and R&D engineers
Industry 5.0 can impact the design for manufacturing in a big way. This is just the start. It is not just about AI anymore – it is about developing technology solutions that are human-centric, resilient and sustainable. Design for manufacturing is a great way to start in newer areas across Manufacturing and then go deeper in across the Product Lifecycle Management (PLM) through CAE/CAD.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.