Machining process planning (MP) is inherently complex due to structural and geometrical dependencies among part features and machining operations. A key challenge lies in capturing dynamic interdependencies that evolve with distinct part geometries as operations are performed. Machine learning has been applied to address challenges in MP, such as operation selection and machining sequence prediction. Dynamic graph learning (DGL) has been widely used to model dynamic systems, thanks to its ability to integrate spatio-temporal relationships. However, in MP, while existing DGL approaches can capture these dependencies, they fail to incorporate three-dimensional (3D) geometric information of parts and thus lack domain awareness in predicting machining operation sequences. To address this limitation, we propose MP-GFormer, a 3D-geometry-aware dynamic graph transformer that integrates evolving 3D geometric representations into DGL through an attention mechanism to predict machining operation sequences. Our approach leverages StereoLithography surface meshes representing the 3D geometry of a part after each machining operation, with the boundary representation method used for the initial 3D designs. We evaluate MP-GFormer on a synthesized dataset and demonstrate that the method achieves improvements of 24\% and 36\% in accuracy for main and sub-operation predictions, respectively, compared to state-of-the-art approaches.

The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing and maximize resource utilization, which is crucial for modern manufacturing systems. Traditional methods using Mixed Integer Linear Programming (MILP) and heuristic algorithms can not well balance solution quality and speed when solving IPPS. In this paper, we propose a novel end-to-end Deep Reinforcement Learning (DRL) method. We model the IPPS problem as a Markov Decision Process (MDP) and employ a Heterogeneous Graph Neural Network (GNN) to capture the complex relationships among operations, machines, and jobs. To optimize the scheduling strategy, we use Proximal Policy Optimization (PPO). Experimental results show that, compared to traditional methods, our approach significantly improves solution efficiency and quality in large-scale IPPS instances, providing superior scheduling strategies for modern intelligent manufacturing systems.

In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. Approaches in this research area develop information models that capture all information relevant to interpret the requirements, effects and behavior of functions. These approaches are intended to overcome the heterogeneity resulting from the various types of processes and from the large number of different vendors. However, these models and associated methods do not offer solutions for automated process planning, i.e. finding a sequence of individual capabilities required to manufacture a certain product or to accomplish a mission using autonomous robots. Instead, this is a typical task for AI planning approaches, which unfortunately require a high effort to create the respective planning problem descriptions. In this paper, we present an approach that combines these two topics: Starting from a semantic capability model, an AI planning problem is automatically generated. The planning problem is encoded using Satisfiability Modulo Theories and uses an existing solver to find valid capability sequences including required parameter values. The approach also offers possibilities to integrate existing human expertise and to provide explanations for human operators in order to help understand planning decisions.

Computer-aided process planning and computer-aided fixture planning have been widely researched in the last two decades. Most of these computer-aided systems are, however, either dealing only with process planning or fixture design. A set-up planning system for the machining of prismatic parts on a 3-axis vertical machining centre is proposed. This system formulates set-up plans based on the initial, intermediate and final states of a part. The system uses the fuzzy set representation, along with production rules and object representation.

In this paper, crucial processes in a computer-aided process planning system, such as selecting machining resources, determining set-up plans and sequencing operations of a part, have been considered simultaneously and modelled as a constraint-based optimization problem, and a Tabu search-based approach has been proposed to solve it effectively. In the optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, set-ups and departure of good manufacturing practices (penalty function) are integrated as an optimization evaluation criterion. A case study, which is used to compare this approach with the genetic algorithm and simulated annealing approaches, is discussed to highlight the advantages of this approach in terms of solution quality, computation efficiency and the robustness of the algorithm.

Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities, leveraging AM's strengths in fabricating complex geometries and SM's precision and quality to produce finished parts. We present a systematic approach to automated computer-aided process planning (CAPP) for HM that can identify nontrivial, qualitatively distinct, and cost-optimal combinations of AM/SM modalities. A multimodal HM process plan is represented by a finite Boolean expression of AM and SM manufacturing primitives, such that the expression evaluates to an'as-manufactured' artifact. We show that primitives that respect spatial constraints such as accessibility and collision avoidance may be constructed by solving inverse configuration space problems on the'as-designed' artifact and manufacturing instruments. The primitives generate a finite Boolean algebra (FBA) that enumerates the entire search space for planning. The FBA's canonical intersection terms (i.e., 'atoms') provide the complete domain decomposition to reframe manufacturability analysis and process planning into purely symbolic reasoning, once a subcollection of atoms is found to be interchangeable with the design target. We demonstrate the practical potency of our framework and its computational efficiency when applied to process planning of complex 3D parts with dramatically different AM and SM instruments. Keywords: 1. Introduction Hybrid Manufacturing, Process Planning, Spatial Reasoning, Additive Manufacturing, Machining Hybrid manufacturing (HM), combining the capabilities of additive and subtractive manufacturing, is the new frontier of part fabrication. While additive manufacturing (AM) continues to enable unprecedented levels of structural complexity and customization, subtractive manufacturing (SM) remains indispensable for producing highprecision, mission-critical, and reliable mechanical components with functional interfaces. Versatile'multitasking' machines with simultaneous high-axis computer numerical control (CNC) of multiple AM and SM instruments (e.g., deposition heads and cutting tools) keep emerging on the market, enabling efficient use-cases for fabrication and repair (reviewed in Section 1.1).

This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defining new tasks/problems. In particular, it utilizes a principled Bayesian sequential decision problem framework for jointly and naturally optimizing the exploration-exploitation trade-off. In general, the resulting induced GPP policy cannot be derived exactly due to an uncountable set of candidate observations. A key contribution of our work here thus lies in exploiting the Lipschitz continuity of the reward functions to solve for a nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real time, we further propose an asymptotically optimal, branch-and-bound anytime variant of epsilon-GPP with performance guarantee. We empirically demonstrate the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian optimization and an energy harvesting task.

The operations sheets generator (OPGEN) is an expert system that helps industrial engineers at the Hazeltine manufacturing and operations facilities plan the assembly of printed circuit boards. In this article, we describe the evolution of OPGEN from its initial development in the Hazeltine research laboratories to its routine use in an integrated manufacturing environment. We describe our approaches to the problem that occurred during the development, integration, and rehosting of OPGEN and provide some methodological guidelines to expert system builders who are concerned with the final delivery of an expert system.