Purpose The purpose of this paper is to describe the use of a test artifact proposed by NIST to quantify the dimensional accuracy of a metal additive manufacturing process. Insights from this paper are given concerning both the performance of the machine, a concept laser Mlab cusing machine, and the applicability of the NIST test artifact in characterizing accuracy. Recommendations are given for improving the artifact and standardizing a process for evaluating dimensional accuracy across the additive manufacturing industry. Design/methodology/approach Three builds of the NIST additive manufacturing test artifact were fabricated in 316 stainless steel on a concept laser Mlab cusing machine. The paper follows the procedure described by NIST for characterizing dimensional accuracy of the additive process. Features including pins, holes and staircase flats of various sizes were measured using an optical measurement system, a touch probe and a profilometer. Findings This paper describes the accuracy of printed features’ size and position on the test artifact, as well as surface finish on flat and inclined surfaces. Trends in variation of these dimensions are identified, along with possible root causes and remedies. This paper also describes several strengths and weaknesses in the design of the test artifact and the proposed measurement strategy, with recommendations on how to improve and standardize the process. Originality/value This paper reviews a previously proposed design and process for measuring the capabilities of additive manufacturing processes. It also suggests improvements that can be incorporated into future designs and standardized across the industry.

We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant and a new, similar task is learned by manipulating the firing sensitivity of each neuron via a supplemental bias input. We refer to this as neuromodulatory tuning (NT). We demonstrate empirically that neuromodulatory tuning produces results comparable with traditional fine-tuning (TFT) methods in the domain of image recognition in both feed-forward deep learning and spiking neural network architectures. In our tests, NT reduced the number of parameters to be trained by four orders of magnitude as compared with traditional fine-tuning methods. We further demonstrate that neuromodulatory tuning can be implemented in analog hardware as a current source with a variable supply voltage. Our analog neuron design implements the leaky integrate-and-fire model with three bi-directional binary-scaled current sources comprising the synapse. Signals approximating modulatory neurotransmitter mechanisms are applied via adjustable power domains associated with each synapse. We validate the feasibility of the circuit design using high-fidelity simulation tools and propose an efficient implementation of neuromodulatory tuning using integrated analog circuits that consume significantly less power than digital hardware (GPU/CPU).