###### Abstract
In our applications, ML models make it possible to identify patterns in complex data (including biomedical data used for exoskeleton design), support design and optimisation at every stage of manufacturing, and provide support in finding nonobvious engineering solutions with fewer design iterations, shorter duration, and lower cost. This is particularly important for exoskeletons, where varying size, movement patterns, type, and deficit levels require a personalised approach [10]. One of the important problems to be solved in assistive technologies and rehabilitation robotics is the need to provide materials with controlled bending force and direction. More and more methods are being used to address the challenge of preprogrammed elastic deformation mentioned above, so as to avoid complicated elasticity calculations [11]. One way to solve this problem is cellular neural networks, in which local chainmail position corrections are captured as local cell connections. In this way, the dynamic deformation behaviour (not only of 3D-printed fabrics, but also of neural structures or soft tissues) is directly transformed into the dynamic behaviour of the cellular neural network [12; 13; 14]. This approach is able to efficiently model both typical and atypical nonlinear mechanical behaviours. It gives the designers a lot of freedom, as it is enough to program the boundary parameters of the chainmail structure and the remaining issues are solved automatically in some common cases. In other cases, it is necessary to tune the solution parameters to the requirements/expectations, which are usually described by clinical parameters [15; 16]. Chainmails are included in the four-dimensional (4D) printing family of materials that has been developed since 2013. This approach extends the previous complex geometries of manufactured 3D objects by giving classical static structures dynamic properties that change over time and ensuring that these structures adapt to, for example, the changing shape of the hand in the therapeutic process [17]. Our 3D-printed chainmail makes it possible to obtain materials and ready-made elements (here an exoskeleton on a hand) with preprogrammed properties related to:  
* The size and shape of the elements and the resulting properties;
* The material used (PLA, ABS, and others) and the resulting properties;
* Bending: in all directions, in only one direction, or in selected directions, with a programmed maximum bending angle;* Tensile properties in both directions (e.g., kinesiotex tapes with properties similar to human skin);
* Degree of softness;
* Other properties (durability, etc.) as needed;
* Opportunities for computerisation of design and optimisation of ownership thanks to artificial intelligence (AI) [18]. Technological and material advances in 3D printing allow for faster fabrication, greater freedom of shape selection, and programmable control of mechanical properties, providing the potential for innovative interdisciplinary engineering solutions in mechanics, automation, robotics, computer science, and biomedical engineering. An example is the availability of AI-assisted 3D design of personalised medical devices, such as in dentistry, where in-office 3D printing is booming and design individualisation is very much required. With the advent of new biocompatible 3D-printing resins, the ability to 3D print personalised devices such as orthodontic distalisers and power arms requires 3D modeling skills, but knowing the biomechanical aspects in relation to the different strengths of 3D-printing materials is much more complicated for dentists. In such cases, support with shape optimisation would be extremely useful [19; 20]. Programmability here can refer to a combination of programmable mechanical design, programmable materials, programmable fabrication, and programmable operation of the finished device. Research work in these areas can be accomplished through hierarchical programming, from modelling and fabrication to the operation of the finished device [21; 22].