Design for Additive Manufacturing | Smart Manufacturing Lab

Challenges

Application of optimization techniques considering combinations of weight reduction and linear elasticity and their analogy in the thermal and electromechanical domains.
The ability to make designs more complex to gain an advantage of possible free complexity available with AM process
To overcome the cognitive barriers imposed by past experience and conventional fabrication techniques.
Finding the optimal distribution of material and voids in a given design space, dependent on loading and boundary conditions, such that the resulting structure meets prescribed performance targets.

Research Opportunities

To incorporate material, mesostructures, and multi-scale design considerations in products.
Incorporating the geometric and material freedoms of AM at part level with macro-scale complexity and material level with micro-scale complexity.
Developing internal freeform structures for improved functionality and performance.
Designing of macrostructure topology optimized products for reduced material and energy use.
Designing custom-fit products for medical, dental, and packaging industries.

Objectives

To cater to the need of different AM processes which require different approaches to design processes and practices.
To make a functionally complex product by integrating multiple functions into a single part.
Methodologies for Consolidating parts for reduced assembly and cost keeping in mind the AM constraints

Equipment / Software

Collaborators: GE, Autodesk, Altair, Sedaxis, Vectraform and Eaton

Challenges

Process qualification and certification catering to medical, aerospace and defense
Process control and in-process measurements to reduce process defects
Develop new materials and novel new applications
Identification of suitable pre and post-processing methods to perfect the part production
Plan, prepare, print, and perfect complex designs by considering challenges such as material efficiency, sustainability, cost, and lead time.

Research Opportunities

Identification potential relationship between process-structure-property-function-behaviour relations.
Reducing the cost and time involved in the AM process simulation through innovative algorithms
Performance benchmarking of processes with respect to process capabilities of different metal AM processes
Hybridization of metal AM processes to optimise the buy-fly ratio of the major automotive parts

Objectives

To digitize the manufacturing process by adopting metal AM process and building an end-end digital implementation of the workflow
To create a knowledgebase of metal AM capabilities and to develop demonstrated abilities in creating application-specific solutions

Equipment / Software

Collaborators: GE, Autodesk, Altair, Sedaxis, Vectraform and Eaton, DRDL, IIT Madras