Ningxu Cai

Architecture design for distributed process planning

Abstract Todays machining shop floors, characterized by a large variety of products in small batch sizes, require dynamic process planning capabilities that are responsive and adaptive to the rapid changes of production capacity and functionality. To meet the requirement, this research proposes a new methodology for dynamic and distributed process planning. The primary focus of this paper is on the architecture of a new approach using function blocks. The secondary focus is given to the other supporting technologies—machining features and agents. Different from conventional methods, this approach uses a two-layer structure—supervisory planning and operation planning. It is expected that the new architecture can improve the system performance in a dynamic environment.

Enriched machining feature-based reasoning for generic machining process sequencing

This paper presents an enriched machining feature (EMF)-based reasoning approach to generic machining process sequencing for distributed process planning (DPP). An EMF is represented by combining its machining volume with surface, geometric and volume features, as well as other technological information needed to machine the feature. The information embedded in the EMF is retrieved progressively for machining sequence generation. Following an introduction of EMF and its representation scheme, the problems in determining machine-independent feature groups (set-ups) in DPP and their machining sequences to be followed for a given part are investigated. Based on the EMF concept, five reasoning rules are formulated and the algorithms developed. As the set-ups and sequences are generated based on manufacturing constraints and datum references but separated from specific resources, they are generic and applicable to machine tools with varying configurations and capabilities. This approach is further validated through a case study.

GA-based adaptive setup planning toward process planning and scheduling integration

Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setup planning.

ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, todays customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.

Adaptive setup planning of prismatic parts for machine tools with varying configurations

Setup planning for machining a part is to determine the number and sequence of setups (including machining features grouping in setups) and the part orientation of each setup. Tool accessibility plays a key role in this process. An adaptive setup planning approach for various multi-axis machine tools is proposed in this paper focusing on kinematic analysis of tool accessibility and optimal setup plan selection. In our approach, feasible Tool Access Directions (TADs) of machining features are denoted by partially sequenced unit vectors; The Tool Orientation Spaces (TOS) of different multi-axis machine tools are generated according to their configurations through a kinematic model, and represented on a unit spherical surface. Starting from a 3-axis-based machining feature grouping, all possible setup plans of a given part for different types of machine tools (3-axis, 3-axis with an indexing table, 4-axis, and 5-axis machines) can be achieved effectively by tool accessibility examination. The optimal setup plans are selected from obtained candidates by evaluating both their locating and grouping factors. A so-generated setup plan can provide not only the best coverage of machining features and the primary locating directions but the optimal orientations of the work-piece for each setup. Only prismatic parts are considered in this proof-of-concept study, and the algorithms introduced in this paper are implemented in MATLAB. A case study is conducted to validate the algorithms.

An Effective Approach for Distributed Process Planning Enabled by Event-driven Function Blocks

This chapter presents a function block enabled approach towards distributed process planning. It covers the basic concept, generic machining process sequencing using enriched machining features, process plan encapsulation in function blocks, and process monitoring through event-driven function blocks. A two-layer structure of supervisory planning and operation planning is proposed to separate generic data from machine-specific ones. The supervisory planning is only performed once, in advance, at the shop level to generate machine-neutral process plans, whereas the operation planning is carried out at runtime at the machine level to determine machine-specific operations. This dynamic decision making is facilitated by resource-driven algorithms embedded in the function blocks. The internal structures of typical function blocks are also introduced in the chapter. Our approach and algorithms are verified through case studies before drawing conclusions. It is expected that the new approach can greatly enhance the dynamism of fluctuating job-shop operations.

Overview of a Distributed Process Planning Approach Targeting Manufacturing Uncertainty

This paper presents an overview of our DPP (distributed process planning) approach, covering DPP concept, generic machining process sequencing using enriched machining features, process plan encapsulation in function blocks, and process monitoring enabled by the function blocks. A two-layer structure of Supervisory Planning and Operation Planning is proposed in DPP to separate generic data from machine-specific ones. The supervisory planning is only performed once, in advance, at shop level, whereas the operation planning is carried out at runtime at machine level. This dynamic decision-making is facilitated by a set of resource-driven algorithms embedded in the function blocks. The internal structures of typical function blocks are also introduced in the paper. The DPP approach and algorithms are further verified through a case study before drawing conclusions. It is expected that the new approach can largely enhance the dynamism of fluctuating job shop operations.Copyright

Adaptive Setup Planning for Job Shop Operations under Uncertainty

This chapter presents a novel decision-making approach toward adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilises a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of this research is to enable adaptive setup planning for dynamic job shop machining operations through collaborations among multiple system modules residing in different resources and interactions with human operators. Particularly, this chapter covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for specific machines. The decision-making process and algorithms validation are further demonstrated through a case study. It is expected that the proposed approach can largely enhance the dynamism of fluctuating job shop operations.

Adaptive Setup Planning of Prismatic Parts by Tool Accessibility Examination

Setup planning for machining a part is to determine the number and sequence of setups (including machining features grouping in setups) and the part orientation of each setup. Tool accessibility plays a key role in this process. An adaptive setup planning approach for different types of multi-axis machine tools is proposed in this paper by investigating Tool Access Directions (TADs) of machining features, Tool Orientation Spaces (TOSs) of machine tools, and Primary Locating Directions (PLDs) of workpieces. In our approach, feasible TADs of a machining feature are predefined based on feature geometry and best practice knowledge, and denoted by unit vectors; The TOS of a machine tool is generated according to its configuration through kinematic analysis, and represented by a unit spherical surface patch; Primary locating directions and their priorities of a workpiece are determined based on the surface areas and the surface accuracy grades of non-machining surfaces. Starting from a 3-axis based machining feature grouping, setups for a 3-, 4- (or 3-axis with indexing table), or 5-axis machine can be achieved effectively by tool accessibility examination. A so-generated setup plan can provide not only the best coverage of machining features but the optimal orientation for each setup. Prismatic parts are considered in the proof-of-concept phase. Algorithms introduced here are implemented in MATLAB, and a case study is used to show the results.Copyright