Emerging trends in global advanced manufacturing

whitePaper | November 11, 2022

A fast-changing world forces companies to adapt faster. This creates special challenges for their digitalization initiatives. Composability and no-code technologies enables companies not only to keep up with changes. They allow them also to react rapidly and proactively and even use changes as an opportunity to achieve competitive advantages

whitePaper | January 9, 2023

Modern manufacturing theory, tools, and best practices are focused on how to make thousands or millions of identical parts or products at a low cost per unit. Custom and low volume production pose unique challenges that require manufacturers to adapt from typical mass production models. Thanks to the rapid development of technologies and materials over the last decade, additive manufacturing has grown more capable as a true manufacturing tool and can now enable the fabrication of these custom and low volume end-use parts rapidly and cost-efficiently. This white paper presents Formlabs 3D printing hardware and material solutions for end-use part production. It documents various users' case studies and includes stress testing results to verify the suitability of 3D printing materials for end-use applications.

whitePaper | July 23, 2020

Robots now come in many sizes, and in configurations of two to seven axes. They can do simple or complicated work, even surgery, and are easier to integrate than ever into any manufacturing or processing environments, including food zones, clean rooms and warehouses. But what’s most important is to know which type of robot articulated, cartesian, SCARA, delta or cobot fits your needs from both capability and cost standpoints.

whitePaper | November 12, 2022

Since the first Industrial Revolution, the manufacturing process has been revolutionised from water and steam-powered machines to electrical and automated ones. The term "Industry 4.0" or simply I4 was introduced by the German government in 2011, which encourages the use of modern technology in the manufacturing process. It defines that automation technology can be improved by offering different methods of self-configuration, self-optimisation and self-diagnosis. The fourth era of revolution (Industry 4.0) is all about connectivity trends, advanced material, processing technology, service orientation and collaboration in advanced manufacturing networks. It presents a unique level of control and coordination over the whole value chain of the products. Internet of things (IoT) and machine to machine communication (M2M) are merged to improve communication and automation. It creates a new set of design principles for effective and computerised control of the manufacturing process which includes

whitePaper | November 30, 2021

There has been a fundamental shift in product innovation. Not merely a revolution in materials or design, but a revolution of possibilities. Advanced software technologies such as artificial intelligence have given us a peek of what could be, enabled by methods to deliver the speed that turn worldchanging ideas into reality. We’ve now reached an event horizon — one where semiconductors and software actually have the ability to bring about the most uplifting advances in the history of humankind. Thanks to this leap forward, chip technology and a new era of product transformation are poised to take us places previously unimaginable

whitePaper | July 7, 2022

Production scheduling primarily deals with arranging different jobs in various machines based on the timelines for each job. There can be numerous combinations of schedules possible. Production schedules can be designed to maximize the capacity utilization, minimize idle time of the machine, earliness, overall costs etc. Manual design of a schedule can be a tedious and time consuming task. There are a lot of dependencies which the scheduler needs to tweak based on a single change. Generating a production schedule targeting a specific objective function can be done using mathematical models. Mathematical models involve formulating the production scheduling problem as an optimization model wherein there is a certain objective function which needs to be optimized based on a set of constraints. Mathematical models generally outperform manually designed schedules which involve a lot of inter-dependencies among the jobs. Due to improvement in computational power, developing a production schedule using mathematical models is relatively quick and efficient.