Dr Paul Goodall

Published in the Computers In Industry Journal (Volume 150, September 2023, 103943)

Understanding electrical energy consumption of machines and processes is of increasing importance to (i) minimise costs and environmental impact of production activities and (ii) provide an additional information stream to inform condition monitoring systems (i.e. digital twins) about a machine’s status and health. The research outlined in this paper develops a Virtual Machining Energy Toolkit (V_MET) to predict the electrical power consumption of a Computer Numeric Control (CNC) milling machine cutting a particular part program from preparatory codes (i.e. G code). In this way the evaluation of the energy impact of manufacturing part programs prior to implementation and real-time monitoring of the process can become a routine activity at part of a total manufacturing system optimisation. The novelty of this work lies in the inclusion of a virtual CNC process model to determine cutting geometry (i.e. width and depth of cut) to enable the prediction of relatively complex part program geometry.

V_MET consists of three components: (i) the NC interpreter to extract key parameters (e.g. spindle speed, feed rate, tool path) from G-code instructions, (ii) a virtual CNC process model to determine instantaneous cutting geometry (i.e. width and depth of cut) and the material removal from the resulting machining by simulating the motion of the tool path to predict the interaction between the tool tip and workpiece and (iii) an energy model to predict the electrical power consumption for a given set of conditions, developed using regression analysis of data collected under real manufacturing conditions.

Validation of V_MET has been conducted by physical machining of different product features to evaluate the validity over a range of different cutting parameters, NC operations (i.e. linear, clockwise interpolations) and repasses over previously cut regions. Overall good accuracy has been observed for the predicted energy requirements as a function of the cutting regimes, with 4.3% error in total energy and Mean Average Percentage Error (MAPE) of 5.6% when compared with measurements taken during physical cutting trials.

Published in the IET Cyber-Physical Systems: Theory and Applications Journal

Within the Industry 4.0 landscape, humans collaborate with cyber and physical elements to form human-cyber-physical systems (HCPS). These environments are increasingly complex and challenging workspaces due to increasing levels of automation and data availability. An effective system design requires suitable frameworks that consider human activities and needs whilst supporting overall system efficacy.

Although several reviews of frameworks for technology were identified, none of these focused on the human in the system (moving towards Industry 5). The critical literature review presented provides a summary of HCPS frameworks, maps the considerations for a human in HCPS, and provides insight for future framework and system development. The challenges, recommendations, and areas for further research are discussed.

This paper was presented at the 2023 3rd International Conference on Mechanical, Aerospace and Automotive Engineering (CMAAE 2023) held in Nanjing, China.

A buffer is an important element in a production line and its allocation influences the throughput and inventory of the line. The buffer allocation problem can be framed as a multi-objective optimisation problem and is often addressed by using meta-heuristic algorithms, such as evolutionary algorithms. However, these algorithms primarily focus on the “genetic evolution” aspect and do not take in to account the impact of the biological “organism development” process, potentially constraining the exploration of the solution space. In this paper, a bio-inspired evolution-development (evo-devo) approach for modelling and optimising buffer allocations is proposed. The organism representing a production line is defined and modelled, and the evolution and development processes of organisms are developed for researching optimised solutions. The method has been validated by a simulation of a buffer allocation optimisation in an unreliable serial production line with multi-objectives, aiming to maximize production throughput and minimize the total buffer size. Results show that the
proposed method can efficiently obtain solutions, while also achieving greater exploration of the solution space than competing evolutionary algorithms such as the Non-Dominated Sorting Genetic Algorithm II. The proposed approach’s functionality means that it could be applied to other areas of generative design of future factories.

This journal paper is published in MDPI Algorithms (Volume 18, Issue 8) and was also selected as the actual Issue cover too accessible via https://www.mdpi.com/1999-4893/18/8.

The paper belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition).

Abstract: Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures and often produce customised solutions, lacking reusable generative rules that transfer across different problems. This work presents a bioinspired generative design algorithm utilizing the concept of evolutionary development (EvoDevo). This evolves a set of developmental rules that can be applied to different engineering problems to rapidly develop designs without the need to run full optimisation procedures. In this approach, an initial design is decomposed into simple entities called cells, which independently control their local growth over a development cycle. In biology, the growth of cells is governed by a gene regulatory network (GRN), but there is no single widely accepted model for this in artificial systems. The GRN responds to the state of the cell induced by external stimuli in its environment, which, in this application, is the loading regime on a bridge truss structure (but can be generalized to any engineering structure). Two GRN models are investigated: graph neural network (GNN) and graph-based Cartesian genetic programming (CGP) models. Both GRN models are evolved using a novel genetic search algorithm for parameter search, which can be re-used for other design problems. It is revealed that the CGP-based method produces results similar to those obtained using the GNN-based methods while offering more interpretability. In this work, it is shown that this EvoDevo approach is able to produce near-optimal truss structures via growth mechanisms such as moving vertices or changing edge features. The technique can be set up to provide design automation for a range of engineering design tasks.

Published in The International Journal of Advanced Manufacturing Technology, accessible via the following links https://link.springer.com/journal/170 and https://doi.org/10.1007/s00170-026-17862-8

Abstract
Tolerance allocation is an important design step for determining robot accuracy and directly affecting manufacturing cost. However, existing methods typically consider dimension synthesis first before tolerances are allocated, which neglects the manufacturability constraints arising from the dependency between part size and achievable tolerance grades. This often leads to costly iterations between design and manufacturing and increasing manufacturing cost. To address this issue, an integrated tolerance allocation and dimensional synthesis method of robot design is proposed for optimizing both positioning accuracy reliability and manufacturing cost. The method simultaneously optimizes joint dimensions and corresponding tolerances by formulating a cost function that captures the relationship between dimensional parameters, robot end-effector accuracy reliability, tolerance-grade rules, and manufacturing cost. Additionally, a matrix-based Monte Carlo simulation (MCS) method is introduced to accelerate evaluation workspace-wide reliability under tolerance uncertainty. NSGA-II multi-objective optimization algorithm is employed to find the Pareto front of the optimal solutions. A case study of a surgical robot is taken to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can reduce 22% of manufacturing cost while achieving better positioning accuracy reliability compared to the traditional tolerance allocation method, and the speed of matrix-based MCS method is improved by 400 times compared to the point-based MCS method.

This paper will included in the Advanced Engineering Informatics Journal, Volume 73, accessible via the following links https://www.sciencedirect.com/science/article/pii/S1474034626002922 and https://doi.org/10.1016/j.aei.2026.104600

Abstract

Robotic design and optimization often require extensive simulation-based iterations, particularly for predicting static deformations under varying postures, external loads, and gravitational conditions. Conventional finite element simulations can achieve high accuracy but are computationally expensive, limiting their use in large-scale design optimization. Data-driven approaches have emerged to accelerate prediction; however, most existing methods are restricted to simplified scenarios, such as fixed geometries with varying poses or fixed poses with varying loads, and typically predict only partial deformation at points rather than the full deformation field. To enable fast and generalizable deformation prediction, a new neural operator architecture named Robot-NO is developed. A geometric–load graph based on the finite element mesh connectivity, where each node encodes spatial coordinates with local load conditions is constructed. A graph neural network is then employed to extract a unified global representation that captures both geometric structure and external force distribution. A query–decoder module combines global features with arbitrary query-coordinates to predict the full deformation
field. This query-based formulation enables accurate continuous deformation prediction across varying robot geometries, postures, and load conditions. Experimental results demonstrate that the proposed model achieves a root mean square error of 0.91 μm in deformation prediction and is 6000 times faster than conventional finite element simulations. This significant improvement enables rapid design, analysis and optimization for robotic mechanisms.